AI is already being used to help researchers analyze massive datasets, identify drug targets, improve medical imaging, support clinical decision-making, and personalize treatment. In biotechnology, the pace is especially fast. Biotech leader Illumina recently announced its Billion Cell Atlas, the world’s largest genome-wide genetic perturbation dataset, designed to support AI-powered drug discovery by mapping genetic changes across one billion individual cells altered with CRISPR across more than 200 disease-relevant cell lines.

At the same time, AI-enabled medical devices are moving into regulated healthcare environments. The FDA maintains a public list of AI-enabled medical devices authorized for marketing in the United States, giving providers, patients and manufacturers a clearer view of where AI is being used in real medical technology.

That’s the world students are entering.

Most high school students won’t sequence genomes, run clinical diagnostics or design new drugs in a classroom. But they can begin to understand the systems behind modern biotechnology: how biological signals become data, how sensors capture information from the body and the environment, how software interprets that information, and how a device responds.

That’s where Discover AI gives schools a practical entry point.

NeuroMaker Hand: An intro to biotechnology

Discover AI is a STEM-focused, applied artificial intelligence program for high schools. Designed to help students explore AI through numerous topics – including biotechnology – Discover AI is a hands-on, project-based approach to AI in the classroom.

In the Biotechnology Experience of Discover AI, students will interact with robotic prosthetic technology, sensors, brain-computer interfaces, and coding as they understand how AI will impact healthcare and wearable medical devices. And it’s all centered around NeuroMaker STEM.

The NeuroMaker Hand 2.0 is a robotic prosthetic hand that students assemble, wire and then program to respond to various inputs in its environment.

It’s a little bit of mechanical design, engineering, programming, neuroscience and applied AI all combined into one device. However, it’s different than any other “robot” you may have in the classroom. Because it’s a prosthetic, students have to think about the human needs behind the design. Can it grip different objects? Can it respond predictably? Can it adapt to shape, distance or motion? Is it durable enough, flexible enough and usable enough for an everyday task? What tradeoffs exist between performance, cost, comfort and control?

Students work through those questions in a series of projects, giving them practice with the engineering concepts and the human-focused healthcare topics.

The NeuroMaker Hand across the edge-to-cloud continuum

Applied AI is all about the edge-to-cloud continuum.

At the edge, the physical system is the NeuroMaker Hand itself: the prosthetic hand, fingers, tendons, servos, control box, power source, and the objects it interacts with. It’s also were any AI applications will be applied. The hand has to open, close, grip, release, and perform tasks with enough precision to be useful.

The sensor layer is how the hand gathers information from the user and the surrounding environment. The NeuroMaker ecosystem includes an EMG muscle signal sensor, flex sensor, ultrasonic sensor, IR obstacle sensor, hall sensor, rotary encoder, temperature sensor, RGB color sensor, sound sensor, push button and RGB LED/output modules. Students can work with inputs such as muscle signals, hand motion, distance, color, sound, temperature and brainwave activity.

The onboard microcontroller is the control layer that gathers all the data from those sensors and tells the hand how to respond: adjust a grip, react to a distance threshold, follow a gesture, or translate muscle activity into a hand movement. Students can use the NeuroMaker Core controller or any other compatible third-party microcontroller including Micro:bit, Arduino, Raspberry Pi or Adafruit.

The cloud/AI layer, a local computer hosts block-based and Arduino programming options, where students set up programming environments and apply conditionals, events, sequences, loops, variables, and functions to control the hand.

With this approach, you’re making AI less abstract for students. They’re not just learning terms like “machine learning” or “automation.” They’re seeing how a system captures data, interprets it through software, and produces a physical response.

What Students Learn in the Discover AI Biotechnology Experience

The Discover AI Biotechnology Experience turns those edge-to-cloud concepts into a structured sequence of hands-on learning. Students begin by assembling the NeuroMaker Hand, identifying its mechanical and electrical components, and understanding how servos, tendons, controllers, wiring, sensors and power sources work together to create movement.

From there, they move into the biomedical engineering side of the system: neuroscience, body systems, prosthetics, the human hand, grip design and brain-machine interfaces. Because the device is modeled around a prosthetic hand, students have to think beyond whether the technology “works.” They have to consider whether it works for a person: Can it grip different objects? Can it adapt to shape and pressure? Is it comfortable, durable, affordable and usable?

Programming then gives students direct control over the hand’s behavior. Using block-based and Arduino programming concepts, they build projects around object detection, gesture matching, EMG muscle signals and autonomous hand movement. They’re learning applied programming in real-time. If a sensor threshold is wrong, the hand reacts incorrectly. If the logic improves, the hand performs the task more effectively.

The capstone brings everything together in a human-centered challenge. Students design and enhance the prosthetic hand to complete everyday tasks in a simulated grocery store environment, then prototype, test, evaluate and present their solution.

Discover AI is a modular, hands-on and project-based platform to bring applied artificial intelligence into high schools. It consists of an introductory eLearning course to help students understand how AI and machine learning work in the real world, then provides students the freedom to explore 45-hour courses in 12 different STEM fields like smart manufacturing, agriculture, energy, biotechnology, drones, self-driving cars and more.

To learn how to bring Discover AI to your school, contact ATS Midwest.

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That’s why troubleshooting can’t be treated like a skill people will just absorb over time. The strongest maintenance training programs build it on purpose. They give technicians the foundational knowledge to understand how systems work, the hands-on practice to apply that knowledge, and the chance to work through realistic faults in a setting where mistakes become part of the learning process instead of an expensive disruption.

Michael Foods, part of the Post Holdings family of companies, has 17 U.S. manufacturing facilities and a technical training center in Norwalk, Iowa. They’ve figured out how to successfully train their maintenance teams with a systematic, measurable, and engaging program. And it all takes place off the plant floor.

Why It’s So important to Teach Troubleshooting Skills

Troubleshooting sounds simple until you try to teach it.

It isn’t one skill. It’s a combination of technical understanding, logic, observation, pattern recognition, and confidence. A technician might know what a component does in theory and still struggle when a live system isn’t behaving the way it should. They may understand a wiring diagram on paper but freeze when they have to trace a fault through an actual control system under pressure.

That’s part of why troubleshooting often becomes a gap in maintenance training. In many workplaces, it’s expected before it’s really developed. Technicians are asked to think critically and solve problems on the fly, but their training may have been rushed, inconsistent, or limited to whatever they picked up from the people around them. The risk is that when a line goes down, a technician might just “throw parts” at the issue which increases downtime and cost.

Limits of On-the-Job Learning

There’s a lot of value in learning on the plant floor. Maintenance is, by nature, practical work. But the job itself isn’t always the best place to build troubleshooting fundamentals.

Production environments come with pressure. When equipment goes down, the priority is getting it back up. There usually isn’t much time to slow down, explain the reasoning behind each step, or let someone work through the fault carefully if they’re still learning. Mistakes can have real consequences, from extended downtime, to safety risks, to damage to confidence.

That’s why dedicated training environments make such a difference. They give technicians room to ask questions, test ideas, make mistakes, and build skill without the full weight of production bearing down on every decision.

Michael Foods‘ Maintenance Manager Steve Vaske described the value of a dedicated training room where technicians can “put [their] hands on stuff and make mistakes,” even “blow the fuse and have some water spill on the floor and have an air leak,” without “the pressure of having to get the line back up and run.” In that kind of setting, the pressure changes. Instead of being punished for trial and error, technicians can learn from it.

That matters because troubleshooting is built through repetition. People get better at diagnosing problems when they’ve had the chance to work through them in a structured way, not just react to them in a crisis.

The Formula for Effective Troubleshooting Training

In an ideal scenario, maintenance technicians have access to a systematic training program outside of the production floor where they can learn and get hands-on practice before applying their skills on live equipment.

The best troubleshooting training starts with fundamentals of manufacturing technology. That usually means building fluency in electrical principles, motor controls, drives, wiring, sensors, PLCs, safe testing procedures, and the logic behind how systems operate. Then, they get hands-on practice with operating and adjusting these systems.

For maintenance technicians especially, a the final critical piece to training is systematically teaching troubleshooting techniques and practices.

The formula for effective troubleshooting training = theory + hands-on skills + systematic troubleshooting practice.

This is where a system like Amatrol’s broader maintenance training ecosystem becomes especially useful. The combination of eLearning, hands-on trainers, and structured fault-based exercises creates a more complete learning experience. The eLearning helps break down manufacturing concepts into manageable pieces and explains the “why” behind the system. The physical trainers let technicians apply that knowledge in a way that feels real. And FaultPro’s systematic fault insertion capabilities simulate common troubleshooting scenarios that technicians need to work through. Together, those pieces make the learning process more approachable and more effective.

Case Study: Michael Foods

Michael Foods launched the ARMED (Achieving Results in Maintenance through Education and Development) Program to develop skilled industrial maintenance technicians from within their own workforce. A major component of the program is a word-class technical training facility at their Norwalk, Iowa location. ARMED Program Manager Brian Helm said, “The reception to the training center has exceeded my expectations. When we get technicians through here, they walk in, they see all these professional trainers, and they know that Michael Foods is taking this serious and making a pretty significant investment.”

The training program follows the formula of eLearning + hands-on training + troubleshooting skills using Amatrol’s suite of training solutions.

It’s designed to reduce intimidation and build confidence and skills quickly. Helm described how many technicians arrive unsure of their abilities in certain areas, but gain traction because “the way the curriculum is formulated, they’re able to kind of learn in bits and pieces.” Then comes what he called the “aha moment,” when technicians realize, “I got it. I can do this.” 

Industrial Electrician Jose Vera has been instrumental in running hands-on training. “I helped facilitate the training by showing people basic electrical skills, how to troubleshoot, how to wire a panel from scratch, how to test motors, how to make motors, wiring, proper panel etiquette, safety.”

Just as important, the training is tied directly to the kinds of problems technicians actually face in the plant. “We’ve added some sensors to the electrical wiring trainer, and it’s been really useful,” Vera said. “We customized it so that hopefully people in the future can go to their own facilities and be like, Oh, I know what this is. I know how to what to look for. I know how to troubleshoot this and I can fix it.”

Maintenance Manager Michael Harris commented on how this formalized approach to training will tremendously improve the company’s maintenance technicians’ skills. “I can tell by the trainers that the maintenance department across Michael foods as a whole is going to advance tremendously in this environment,” he said.

Teaching Troubleshooting with Amatrol’s FaultPro

What really sets Amatrol apart from any other technical training solution is FaultPro.

Amatrol’s FaultPro is a computer-based fault insertion system that allows instructors to introduce electronic faults safely into supported training systems. When a fault is inserted, the system behaves as though a real failure has occurred. Learners then have to interpret the symptoms, follow a troubleshooting process, identify the likely point of failure, and perform maintenance to get the system running again.

That changes the quality of troubleshooting training. It makes troubleshooting repeatable. It’s measurable. It allows instructors to tailor the challenge to a learner’s current skill level. And it helps students practice diagnosing faults without putting equipment or people at unnecessary risk.

It also gives instructors a more structured way to teach and assess troubleshooting. Because faults can be selected intentionally, training can target specific weaknesses. And because performance can be measured through numeric or mastery-based grading, instructors can evaluate not only whether a learner found the answer, but how effectively they worked through the problem.

Building Troubleshooting Into Your Training program

The Michael Foods case study proves that troubleshooting skill doesn’t just happen because someone has been around equipment for a while.

It grows faster when employers create the right training conditions. That means building a program that combines foundational knowledge, hands-on practice, and realistic troubleshooting scenarios. It means giving technicians the chance to make mistakes in a safe environment, learn from them, and come back stronger. And it means treating troubleshooting as a core capability that affects uptime, cost, safety, and team performance.

For industrial employers trying to strengthen their maintenance workforce, the goal isn’t just to teach people how a machine works. It’s to teach them how to respond when it doesn’t.

That’s the difference between familiarity and competence. And in maintenance, it’s often the difference between prolonged downtime and a problem solved well.

Get started with your maintenance technician training program with ATS Midwest and Amatrol today!

The post How to Train Troubleshooting Skills for Maintenance Technicians appeared first on ATS Midwest.

Jackson College in Michigan is addressing that challenge with the launch of its Applied Technology Center, a purpose-built facility designed to train the next generation of HVAC and utility professionals.

The new 15,000-square-foot Applied Technology Center, located on Jackson College’s Central Campus, was developed specifically to support hands-on technical training in utilities, energy systems, HVAC-R, and emerging Industry 4.0 technologies.

More than just a new building, the center represents a significant investment in modern workforce training environments that mirror real industry conditions.

Why Is HVAC Training in Such High Demand?

HVAC training programs are expanding nationwide because the demand for skilled technicians continues to grow across multiple sectors.

Several factors are driving this demand:

Aging Infrastructure + New HVAC Technology

Many heating and cooling systems in commercial buildings, industrial facilities, and homes are reaching the end of their operational lifespan.

At the same time, new building codes and energy efficiency initiatives require technicians trained in modern HVAC technologies and diagnostics.

Growth in Data Centers and AI Infrastructure

Modern data centers require highly specialized cooling systems to manage heat generated by high-performance computing and artificial intelligence systems.

JCC HVAC instructor Kyle Gardynik commented on the new demand for talent: “Especially with the new data based companies coming all around these new AI places, the trades are searching for people, left and right, and there’s not enough people out there.”

Skilled Trades Workforce Shortages

Large numbers of experienced HVAC technicians are retiring, creating a need for new workers entering the field.

“The importance of our HVAC program today is there is a shortage of workers in all the skilled trades,” explained instructor Richard Scott during the ribbon-cutting ceremony. 

That shortage is becoming even more visible as new technology industries expand across the Midwest.

Inside the Jackson College Applied Technology Center

The Applied Technology Center at Jackson College is a workforce training facility designed to prepare students for careers in essential infrastructure industries including utilities, HVAC-R, energy systems, and advanced technical trades.

The center was created in response to growing industry demand for skilled technicians and reflects a collaborative effort between the college, industry partners, and regional workforce organizations.

Key features of the facility include:

• 15,000-square-foot workforce training building
• Flexible technical training labs
• Two large program bays for utilities and mechanical systems training
• Extra-tall indoor training space for utility pole instruction
• Outdoor training yard for cable installation and bucket truck operation

These environments allow students to practice real technical skills in spaces designed to simulate field conditions.

“The Applied Technology Center gives students access to modern equipment, real-world training environments, and clear pathways into essential careers that keep our communities running,” said Jamie Vandenburgh, Dean of Workforce, Technical and Professional Education.

Jackson College President Dr. Daniel Phelan described the facility as a major investment in regional workforce development.

“This building is about opportunity: building talent, strengthening local industry, and helping Jackson County thrive for decades to come.”

A Modern HVAC Technicians Training Program

The Jackson College HVAC program combines classroom learning, digital curriculum, foundational technical skills and HVAC-specific training to ensure students graduate with relevant skills for the modern HVAC workforce.

Students begin by learning foundational technical skills around electrical systems, motor control, fluid power, etc. through structured online modules before transitioning into the lab environment, where they apply those concepts using industry-grade training systems and real HVAC equipment. This blended approach allows students to understand both the underlying principles and the real-world operation of modern heating and cooling systems.

A major component of the program is the use of advanced HVAC training systems developed by Amatrol, which are widely used in technical colleges and workforce training programs across the United States. These systems provide structured, hands-on instruction in HVAC operation, electrical systems, refrigeration cycles, and system diagnostics.

Within the lab, students learn how to:

• Observe HVAC processes in a controlled learning environment
• Practice troubleshooting faults introduced by instructors
• Perform electrical measurements and diagnostics
• Understand refrigerant flow and system components
• Develop technical documentation and work-order communication skills

Like many colleges, JCC has real HVAC units for students to train on, but it’s the Amatrol training systems designed for specific HVAC components and operations that sets this program apart.

“Yes, we have real units in here, so they can see what a system looks like, and it’s a pretty shell,” noted HVAC instructor Kyle Gardynik. “But in the end, these [Amatrol] systems break it right down on the proper flow diagnostics and electrical setup for them.” 

The Amatrol HVAC training systems cover heat pumps, refrigerants, installation, residential and commercial HVACR, HVAC motor control, thermal science, geothermal energy, and steam systems.

JCC’s program also emphasizes diagnostics and problem-solving. Instructors introduce system faults that require students to identify issues, determine root causes, and document their findings—mirroring the exact process technicians follow on the job.

By combining digital curriculum, hands-on training systems, and real equipment, Jackson College ensures that students leave the program with both the technical knowledge and practical experience needed to step directly into HVAC careers.

Growing HVAC Programs Provide More Opportunities to Students

Programs like the Jackson College HVAC training program help students pursue careers that offer strong earning potential, stability, and mobility.

Student Josh Reese described HVAC as a profession that can take technicians almost anywhere. “Trades are important. And I thought to myself, you know, people need that. I can do that. And more than anything, it’s a transportable skill, something I can go anywhere in the world. People will still need the work that I’m doing.” 

President Phelan emphasized the broader significance of these careers during the ribbon-cutting ceremony. “When you think about it, this building is really about dignity. These are careers that require intelligence, discipline and courage and craftsmanship. They are the careers that offer stability, strong earnings for families and purpose, and for many of our students, they are the difference between getting by and getting ahead in their life.”

To build an HVACR training program like this one, send us an email! 

The post Jackson College Applied Technology Center Meets Industry Need for HVAC Technicians appeared first on ATS Midwest.

Most AI that students will encounter in technical fields won’t be chatbots. It’ll be applied AI. Embedded intelligence inside real systems, systems that sense the world, make decisions, and take action. That intelligence is increasingly present in the tools technicians operate, the equipment engineers design, the infrastructure operators maintain.

That’s why the most important question for educators isn’t “Should I let my students use ChatGPT?” It’s, “How does applied AI actually work in the real world, and what does that mean for my classroom?”

What is Applied AI?

Applied AI is the use of AI and machine learning to improve physical processes and systems, typically by enabling them to sense, decide, and act with increasing accuracy over time. It’s the difference between an AI limited to digital interactions (ChatGPT) and an AI system that can detect a defect, optimize a route, predict a failure, or personalize an experience in real time on a physical system (self-driving cars, autonomous mobile robots, smart manufacturing systems, etc.).

To understand applied AI, you need one foundational concept: the Edge-to-Cloud Continuum.

The Edge-to-Cloud Continuum

The Edge-to-Cloud (E2C) Continuum is the full pathway that connects the physical world to intelligence in the cloud: how data is gathered on devices, transmitted to the Cloud, analyzed and deployed in AI/ML models, and sent back to the device for improved performance.

In our framework, the E2C Continuum is easiest to understand as stacking, connected layers. Each layer has a distinct job, and together they explain how applied AI systems operate in the real world.

1) Edge: The physical device

This is the physical device the AI system exists to support: an iPhone, a vehicle, a production line, a robot, a tractor.

2) Sensors: How the system gathers data on the Edge

Sensors are how the physical world becomes data. In industrial settings, that can mean vibration, temperature, pressure, torque, current, vision, proximity or RFID sensors on production equipment. In consumer technology like your iPhone, it can be the 15 sensors in your phone measuring light, pressure, direction, speed, etc., or ways you interact with your apps (every tap, click and swipe is measured as an input signal and captured as a data point).

Sensors do two critical things:

• They capture the raw data that machine learning learns from.
• They define what the system can “see,” which ultimately defines what it can improve.

3) Control system: Where decisions become actions

A control system is the brain of the Edge device that aggregates the sensor data for immediate action and distribution to the Cloud. It might be a PLC controlling a production line, a robot controller adjusting motion, or the iOS on your iPhone that controls Siri.

In some situations, minor AI and Machine Learning are taking place here at the Control system. Where speed, latency and bandwidth become an issue (like an autonomous vehicle being able to stop at a split-second notice), AI is embedded at the control level, often housed on the Edge device itself.

For much larger-scale data processing and Machine Learning operations, data is transferred through the rest of the E2C Continuum.

4) Transmission: moving data reliably and efficiently

Transmission is the connectivity layer that moves information between the Edge and the rest of the system: Wi-Fi, cellular, industrial networks, ethernet, protocols, routing, and all the practical realities of latency, bandwidth, intermittent connectivity, and security.

Even though “transmission” is more of a transitional step than a stopping-point in the continuum, it’s still important to understand how data is transmitted. A system has to function when networks are congested, when bandwidth is expensive, when data volumes are high, and when response times must be predictable.

5) Fog: nearby computing that bridges Edge and Cloud

Fog computing (sometimes called edge gateways, local servers, or regional compute) sits between the Edge device and the Cloud. This is where data can be stored temporarily for faster retrieval and other key actions including:

• Caching content and reducing round trips
• Performing local analytics or inference when cloud latency is too slow
• Keeping operations running even when connectivity is limited

Fog often helps smooth data transmission and shorten latency by providing the user access to data centers that are more local to their Edge device, rather than waiting for the raw data to flow all the way to the central Cloud and back again.

6) Cloud: large-scale learning, coordination, and continuous improvement

This is where the magic happens. The Cloud is the centralized brain center of the entire E2C Continuum, where data is gathered (often from many users of the same application) in one place, where AI and ML train models, run experiments, analyze massive datasets, monitor system performance, and coordinate improvements across many users, devices and locations.

At times, users can use “on prem” Cloud servers where the Fog phase is skipped, the data never leaves the physical site, and AI/ML applications are run only on the user’s data. This is often the case for businesses with protected IP, like manufacturers.

Otherwise, common applications like Spotify gather billions of datapoints from millions of users for AI/ML analysis in the Cloud to help improve Spotify’s recommendation engines and user experience.

Let’s take a closer look at the Edge-to-Cloud Continuum through the lens of Spotify.

Edge-to-Cloud Continuum in the Real World: Spotify

Spotify is a useful lens because it’s familiar, but it also reflects how many applied AI systems work in practice.

How does Spotify seem to know what you want to hear next, even when it’s a different genre, a new artist, or a song you’ve never heard before? Sure, it’s a “recommendation engine” – but what does that even mean? It means it’s the Edge-to-Cloud Continuum and applied AI working together as a full system.

Graphic representation from Edge-to-Cloud from the Discover AI Intro to Applied AI Course.

When you open Spotify on your phone and press play, the Edge device is your phone. The app itself is the environment you’re interacting with, and it immediately starts generating data through your actions: play, skip, replay, search, like, playlist creation, session length, and even patterns like what time of day you listen.

Those interactions are the Sensor layer in action. In Spotify’s case, the “sensors” aren’t just physical sensors in your phone, they also include your behavioral signals inside the app. Every tap, swipe, and click becomes a data point that helps Spotify understand your preferences and listening habits.

The Control system is the app logic on your device that turns those signals into immediate actions. When you tap play, Spotify starts playback. When you skip a track, it responds instantly. This is where the user experience has to feel seamless and immediate. Some decisions and processing happen locally so the app feels responsive, even before deeper AI/ML processing happens elsewhere in the continuum.

From there, the data moves through Transmission: Wi-Fi, cellular, and internet infrastructure that carries your listening activity and content requests through the network. This layer matters because Spotify has to deliver a consistent experience even under real-world conditions like variable bandwidth, congestion, or changing signal strength.

Before everything reaches the central Cloud environment, Spotify also relies on Fog-layer infrastructure (nearby servers, caching systems, and regional delivery networks). This is what helps songs load quickly and stream smoothly without constant buffering. Fog systems reduce latency by storing and serving content closer to the user, which is especially important when millions of people are streaming at the same time.

Finally, the Cloud is where Spotify’s large-scale AI and machine learning operations happen. This is where data from millions of users is aggregated and analyzed, where recommendation models are trained and refined, and where Spotify learns broader patterns across users, genres, behaviors, and contexts. The cloud doesn’t just help Spotify respond in the moment, it helps Spotify’s recommendation engine improve over time.

That’s what makes Spotify such a strong example of applied AI. It’s a full edge-to-cloud system that continuously captures signals, delivers real-time experiences, and uses cloud-scale AI/ML to improve what happens next.

The applied AI loop in Spotify

• Edge + Sensors capture behavior and delivers the immediate user experience
• Transmission + Fog move and optimize data/content delivery for speed and reliability
• Cloud trains and improves AI/ML models using large-scale listening data
• Updated intelligence gets deployed back into the experience, improving future recommendations and performance

The Edge-to-Cloud Continuum in Every industry

Once you have the E2C lens, it’s easy to translate beyond consumer apps into the industries where applied AI is becoming foundational.

Nearly every industry, from manufacturing to transportation to healthcare, follows this E2C Continuum infrastructure. Once you understand how it works in one area, you can translate it to other fields:

Why this matters, and why we built Discover AI

For professionals and learners in STEM and CTE pathways, the most durable AI literacy won’t come from treating AI as a single tool. It comes from understanding systems; how data becomes decisions, how decisions become actions, and how those actions improve over time across real constraints.

That’s exactly what Discover AI is designed to teach: not “how to get better at using ChatGPT” but how to truly understand embedded, applied AI across the industries and roles where it’s already changing what “competent” looks like.

If you want to understand applied AI in a way that translates beyond chatbots, and into the systems shaping modern work, the edge-to-cloud continuum is the right starting point.

Learn more about Discover AI. 

The post AI and the Edge to Cloud Continuum appeared first on ATS Midwest.

Electrification Is an Infrastructure Shift

What do you picture when you hear “electrification”? Most people jump right to mobility – electric vehicles, buses, etc. But electrification today is a much broader topic whose impact will trickle back into what we’re teaching in our technical programs.

For more than a century, industrial systems relied heavily on mechanical power transmission, hydraulics, and pneumatics. Today, those systems are increasingly replaced by electrically actuated motion, variable frequency drives, embedded sensors, and digitally monitored control systems.

Consider this: Buildings are becoming energy management systems. Factories are becoming power-dense automation environments. Logistics networks are becoming electrified and autonomous. Even heating systems are shifting from combustion to electric heat pumps.

Electrification touches generation, transmission, storage, distribution, and end-use applications simultaneously. It connects infrastructure, industrial equipment, mobility, and digital control systems into a single integrated ecosystem.

And all this requires new skills.

AI and the Energy Multiplier

In a recent episode of The TechEd Podcast, U.S. Interior Secretary Doug Burgum described AI data centers as “intelligence factories.” The phrase is telling. These facilities are not incremental consumers of electricity; they represent exponential load growth. It’s a heated topic across the news, social media and communities today. And while this article won’t get into the shoulds or should nots of data centers and AI energy consumption, the reality is that AI is and will continue to alter our energy landscape.

Artificial intelligence accelerates electrification in two ways:

1. AI systems themselves require immense computing power and therefore massive electrical load.

2. AI-driven manufacturing, automation, and robotics increase electrified industrial demand.

Data centers, advanced manufacturing plants, and automated logistics facilities are some of the most energy-intensive assets being built today. Electrification is no longer just about replacing engines with motors. It is about sustaining a power infrastructure capable of supporting an AI-driven economy.

This introduces new pressures on generation capacity, grid resilience, storage technology, and energy distribution systems.

Mobility Is Visible — But It’s Not the Whole Story

Electric vehicles often dominate public conversations about electrification. EV charging infrastructure, battery manufacturing, and lithium-ion chemistry are highly visible examples of the shift.

But mobility is only one layer of the transformation.

Battery manufacturing, for example, has applications far beyond EVs. And the skills required in a battery manufacturing setting go beyond a traditional manufacturing facility. More advanced safety protocols and tighter tolerance needs, for starters. Beyond that, there’s:

• Precision inspection systems
• Smart automation
• Statistical process control
• Digital data collection
• PLC network communication
• Mechatronics integration
• Advanced safety standards

Programs like the EV-Battery Manufacturing curriculum developed by Amatrol illustrate how electrification intersects with smart manufacturing, quality control, robotics, and cybersecurity.

Electrification increases power density inside factories. It increases demand for motor control systems, inverters, converters, embedded control devices, and high-voltage safety protocols. The implications extend well beyond transportation.

Power Is the Constraint Beneath Electrification

Electrification only works if power systems scale with it. As load increases from AI data centers, electrified manufacturing, and mobility infrastructure, the conversation inevitably turns to generation mix, storage capacity, and grid modernization.

Renewables play a role. Storage plays a role. Distributed energy systems play a role.

Nuclear energy is also re-entering the discussion. In another episode of The TechEd Podcast, Patrick O’Brien of Holtec International discussed the renewed focus on small modular reactors and advanced nuclear technologies as part of long-term grid stability and clean base-load generation.

Nuclear is not the sole answer. But it is part of the broader systems conversation about reliable power in an electrified economy.

Electrification is not simply about replacing one energy source with another. It is about managing a complex interplay of:

• Generation
• Transmission
• Storage
• Load balancing
• Digital monitoring
• Cybersecurity
• Resilience

This complexity fundamentally changes the skills required to design, maintain, and operate modern systems.

The Technician Role Is Expanding

Traditional electrical programs often focus on wiring, circuit fundamentals, and component troubleshooting. Those foundations remain critical. But they are no longer sufficient on their own.

Modern technical roles increasingly require the ability to:

• Understand power electronics and variable frequency drives
• Interpret digital monitoring data
• Integrate distributed energy systems
• Work across electrical and automation platforms
• Maintain electrified motion systems
• Operate within smart manufacturing environments

Industrial electrical training systems from companies like DAC Worldwide build foundational competencies in transformers, motor control, and power distribution. Engineering-focused energy systems platforms from Amatrol help students understand energy conversion and system dynamics. Renewable integration systems from leXsolar expose learners to distributed generation and storage concepts.

Individually, each of these addresses a portion of the electrification landscape.

Collectively, they form a systems-level education model.

From Isolated Labs to Integrated Energy Ecosystems

The central question for technical programs is no longer, “Do we teach electricity?”

It is:

• Do students understand how power generation connects to industrial load?
• Do they work with digitally monitored energy systems?
• Are automation, robotics, and power electronics integrated in the same lab environment?
• Do they see the relationship between battery manufacturing, smart factories, and grid stability?

Electrification does not occur in silos. It is inherently interdisciplinary. Programs that isolate electrical theory from automation, or automation from energy systems, risk preparing students for an industrial model that no longer exists.

Electrification Is a Systems Redesign

Let’s pan out even further. Electrification is still not limited to EVs, renewables, or data centers. It is a redesign of infrastructure and industry at every level.

It touches Mobility, Manufacturing, AI, Critical Minerals, Grid Modernization, Energy Storage, Smart Automation, Robotics, Industrial Controls.

As the economy electrifies, education must reflect that interconnected reality.

Programs that align generation, distribution, automation, inspection, manufacturing systems, and digital control into cohesive learning ecosystems will be positioned to lead. Those that treat electrification as a niche specialization may struggle to keep pace with infrastructure and industrial change.

If your institution is evaluating how electrification should shape your labs, curriculum, or long-term program strategy, we welcome the conversation.

Schedule a discussion with our team to assess how your technical programs can align with the systems-level transformation underway.

The post The Electrification of Everything and Its Impact on Technical Education appeared first on ATS Midwest.

Across universities, 3D printing has moved well beyond prototyping. In research labs, it is increasingly used to fabricate functional components, experimental devices, and custom tooling that would be impractical – or impossible – to produce using traditional manufacturing methods. As research pushes into smaller scales, softer materials, and more complex geometries, the capabilities of the printing technology itself begin to matter far more than the novelty of additive manufacturing.

For many faculty and graduate researchers, the real value of advanced 3D printing is not that it exists, but that it enables three critical outcomes: faster iteration, higher precision, and material freedom.

Accelerating Research Through Speed and Iteration

In a university research environment, time is rarely abundant. Graduate students balance coursework, teaching responsibilities, and grant-driven research timelines. Waiting hours or days for a single print iteration slows progress and limits how aggressively ideas can be tested.

DLP (Digital Light Processing) 3D printing addresses this challenge by curing an entire layer at once, rather than tracing each feature with a laser or extruding material line by line. In practice, this means print time is largely independent of the number of parts on the build plate. Printing one part or twenty often takes the same amount of time.

For researchers, this enables:

• Same-day design–print–test cycles
• Batch fabrication of experimental cohorts with identical geometry
• Faster convergence on viable designs

B9Creations’ DLP printers, for example, are capable of printing hundreds of small, high-resolution parts within a typical 8–10-hour workday, and in many cases within a single class or lab period. This speed fundamentally changes how iterative research can be conducted in an academic setting.

Precision That Holds Up at the Microscale

As research moves into micro-devices, microfluidics, biomedical interfaces, and advanced materials, nominal resolution specifications are no longer sufficient. What matters is repeatable, effective resolution – the ability to produce the same fine features reliably across prints and across machines.

DLP technology excels here because projected light cures uniform layers with sharp edge definition. B9Creations 3D printers operate at an effective resolution range of approximately 10-50 microns, with validated dimensional tolerances of ±50 microns (±0.002 inches) across fleets of machines. In third-party testing, dimensional variation between printed parts and their CAD models has been measured at scales comparable to a single human cell.

For research, this level of precision means:

• Fine channels and small features remain open and functional
• Parts behave consistently across experimental runs
• Researchers can trust geometry as a controlled variable

Material Freedom for Experimental 3D Printing

Perhaps the most overlooked requirement in university research is material flexibility. Many research projects depend on materials that are not commercially standardized: custom photopolymers, experimental elastomers, or biocompatible resins with specific mechanical properties.

Closed material ecosystems force researchers to compromise their experiments to fit the printer. Open systems allow the printer to adapt to the research.

B9Creations’ platforms are designed to support third-party and custom materials, with software-level control over exposure, curing profiles, and release forces. This capability is particularly important in fields such as biomedical engineering, chemistry, and materials science, where resins may be produced in small batches, cost thousands of dollars per kilogram, or require highly specific curing behavior.

3D Printing in Research: Neural Interfaces at the University of Colorado

These benefits come together clearly in research conducted at the Weir Biomechatronics Development Lab at the University of Colorado, where doctoral researcher Tyler Currie was developing a neural interface for optogenetics applications in amputees. Optogenetics is a revolutionary neuroscientific technique that uses genetic engineering to introduce light-sensitive proteins (opsins) into specific neurons, allowing researchers to activate or inhibit neural activity with high spatial and temporal precision using light.

University of Colorado researchers were seeking to develop a way to use optogenetics to enable amputees to control a prosthetic hand using the same brain signals we use to control our natural hands. A nerve cuff added to a peripheral nerve and using optogenetics technology would translate neural activity into physical movement.

For the time being, the researchers’ experiments are being carried out on mice. Designing and fabricating this nerve cuff required extreme fabrication precision. The target nerve measured approximately 180 microns in diameter, leaving almost no margin for error. The device also needed to be soft enough to match nerve tissue, flexible enough to allow implantation, and biocompatible for in vivo testing.

Traditional manufacturing methods were quickly ruled out. Early 3D printing attempts introduced their own problems. Some systems could not resolve the fine features. Others over-polymerized soft materials, closing internal channels and compromising function. Variability between prints made it difficult to determine whether failures were caused by the design or the fabrication process.

Why DLP Printing Made the Difference

The research team ultimately transitioned to a DLP system from B9Creations, selected for its ability to reliably produce micro-scale features and support advanced materials.

Using a silicone-based, biocompatible resin, the team was able to print nerve cuffs with:

• Feature resolution on the order of 15 microns
• Mechanical stiffness of approximately 1.6 MPa, softer than peripheral nerve tissue
• Elongation at break exceeding 200%, allowing safe implantation
• ISO 10993 skin-contact biocompatibility, exceeding basic cytotoxicity requirements

Equally important, the prints were consistent. Internal features remained open, geometry held from part to part, and post-processing could be refined rather than reinvented with each iteration.

From Fabrication to Biological Validation

With fabrication stabilized, the research could focus on function. The printed nerve cuffs were implanted onto the vagus nerve in animal models engineered to respond to light. When the light source was activated, researchers observed an immediate and repeatable reduction in heart rate, confirming successful neural stimulation.

The devices were also evaluated over extended implantation periods. After seven days in vivo, there were no signs of tissue damage, weight loss, or adverse biological response. Across multiple trials, the results were consistent, allowing the team to validate the design rather than question the manufacturing process.

At that point, 3D printing had effectively disappeared from the research narrative. It was no longer a variable to manage—it was simply the method that enabled the work.

Why This Matters for Universities More Broadly

This example reflects a broader reality across higher education. Many universities already have 3D printing capabilities, often centered in makerspaces or instructional labs. These systems are valuable for teaching and early-stage prototyping. Research environments, however, place fundamentally different demands on fabrication technology.

Advanced research requires:

• Predictable, repeatable precision
• Rapid iteration without compounding variability
• Freedom to work with non-standard materials
• Support and expertise that treats the printer as a research instrument

More than 220 research papers worldwide cite B9Creations technology for applications ranging from microfluidics and biomedical research to materials science and advanced manufacturing, underscoring how these requirements are becoming mainstream rather than exceptional.

The true value of advanced DLP 3D printing in university research is not that it produces parts quickly or with high resolution – though it does both. Its value lies in removing fabrication as a constraint on scientific thinking.

When researchers can trust that a design will print accurately, consistently, and in the right material, they are free to focus on hypotheses, validation, and discovery. In that context, 3D printing stops being a tool to experiment with and becomes infrastructure that accelerates progress.

For universities investing in research-grade additive manufacturing, that distinction is the one that matters most.

Bring B9Creations DLP 3D Printers to Your University

Explore select 3D printers from B9Creations, or work with ATS Midwest to find the right printer for your research needs.

The post How DLP 3D Printing Accelerates University Research appeared first on ATS Midwest.

February is CTE month, a time to celebrate the value of Career and Technical Education in preparing students for high-wage, high-demand careers. Today, 11.2 million students nationwide are involved in CTE programs across the country. Here are 6 reasons why every student will benefit from enrolling in CTE courses, along with the stories of schools who are getting it right:

1. CTE Builds Job-Ready Skills Employers Expect

The “C” of Career and Technical Education is a huge differentiator for the typical secondary school experience. Woven into these programs are the fundamental workplace skills students will need to succeed in their careers. Time management, dressing for the job, written and verbal communication, project management, teamwork, problem-solving and collaboration are just a few of the skills CTE students learn that others might not get in their traditional academic courses.

CTE workplace readiness in action

The Michigan Career and Technical Institute (MCTI) is a one-year program for learners with barriers to employment to come learn technical and workplace skills that will kickstart a great career. But it’s not just the state-of-the-art industrial technology labs that make MCTI a great place to learn.

The program treats the classroom like a job site. Students are expected to arrive on time, punch in and out, and wear the uniform of the MCTI polo and jeans. They’re expected to conduct themselves with the attitudes and behaviors they’ll maintain on the job.

As a result, MCTI students leave the program with a combination of technical skills and workplace skills that set them apart from other candidates for high-demand positions.

2. CTE Connects Students with Local Employers

At its core, CTE is designed to mirror the kinds of technologies, skills and experiences students will encounter in various careers. The programs that find ways to partner with local businesses are often more industry-relevant. They’re more in-tune with the modern workplace and will align their learning outcomes to meet the needs of their employer partners.

CTE + businesses in action

New Berlin Eisenhower High School is a great example of this. In an effort to revitalize their CTE program, they brought dozens of local employers together in listening sessions to understand the workforce needs in the region. It also gave them insight into modern technologies they could mirror in the classroom. Those listening sessions turned into strong local support in the form of financial donations, guest speakers, plant tours and more.

With a classroom of new equipment and the backing of these organizations, Eisenhower launched their aptly-named “How Machines Work” course, which drew so many students the first year they had to expand into several course sections as the program grew. Students learn foundational manufacturing skills – everything from measurement and shop math to hand tools and basic power tools – then progress into mechatronics, industrial controls and industrial robotics.

That level of industry-relevant exposure helped a group of high school students win a statewide industrial robotics competition. One of those competitors – Aren Schiek – also spent time working for a local advanced manufacturing company, where he got to apply his skills in an authentic industrial setting.

3. CTE Sparks Career Interest in Students

Studies show that a student’s own interests in experiences in middle and high school are a stronger determinant of what career path they’ll end up on, even more influential than parents, friends, teachers and social media.

Students CTE classes give students a hands-on experience in many of these fields, like engineering, manufacturing, automotive, welding, culinary arts, marketing, business, teaching, nursing and so much more. In many cases, that hands-on learning can spark an interest a student didn’t realize they had. In other cases, they learn early on what they’re not interested in (which is just as important).

CTE sparking career interest in action

At Pathways High School, juniors and seniors participate in an independent study program where they pursue a topic of interest in great depth, doing research, building a project, and presenting it to a review board.

Junior Miles Meaux discovered a passion in video game design. Throughout his project, he learned how to code and build video games. He enjoyed it so much, he actually launched a multi-developer project to build a mod of a popular video game. At 16 years old, Miles was leading a team of coders from across the country to work together on the game.

The experience inspired him to want to launch his own game design company in the future. These are the experiences that might not be possible without Career and Technical Education.

4. CTE Gives University-Bound Students a Head Start

CTE is all about hands-on, career-connected learning. Does that mean all students who take CTE courses end up going direct-to-work or to a community or technical college after high school?

Certainly not!

That’s a misconception about CTE – that it’s just for a certain population of students – that we want to dispel. University-bound students benefit greatly from taking CTE courses in high school. It’s a both/and.

In fact, when university students have a background in CTE, they often have a context for their university coursework that gives them an advantage over their peers.

CTE for university students in action

Take this trio of students from Plymouth High School for example. Jacob Ashworth, Kyle Kraus, and Alex Oty all took CTE course work in high school. They competed in the SkillsUSA industrial robotics competition. They spent lunch breaks and mornings in the lab learning how to program their robot and integrate it with other technologies.

After graduating high school, all three students pursued engineering and computer science degrees at well-known universities. After a single semester at college, they were astounded at how much more they knew than their peers. All the calculus and advanced physics couldn’t compare to the application of math & science the group had in their CTE courses. Not only did they know the information, they knew why it mattered, how it applies in the real world. That’s what CTE can do for any student.

Today, all three are into their careers as mechanical engineers, computer scientists, and even a Starship engineer!

5. CTE’s Hands-on Approach Drives Engagement in Learning

It’s no secret that not every learner learns the same way. But the traditional sit-and-get model of education only serves a small portion of students who thrive in a classroom of lectures and memorization.

CTE is, at its core, hands-on, applied and career-relevant. It can engage a much broader population of students, especially those who ask, “when am I ever going to use this?”

CTE’s career-connected outcomes in action

Students at the Northview Next Career Center experience this firsthand. For many of them, school was always a struggle. But going to the Career Center gave them an opportunity to learn in a new way – one that captured their interest, allowed them to pursue their passions and even get a sense of what they want to do for a career.

“99% of our students come here because they want to work with their hands. They’re not four-year university students. They’re looking to either go right into the workforce or go into a trade school or maybe get an associate’s degree,” the Center’s Director, Drew Klopcic said.

In one program, students are taking manufacturing courses, getting exposure to real-world industrial technology, earning industry-recognized certifications from the Smart Automation Certification Alliance, building their resumes, and even working with local businesses that support the program.

While Northview Next is specifically designed as an alternative learning school for students who struggle at the academic high school, there’s no doubt that all students can benefit from hands-on, career-connected learning.

6. CTE lets students get involved in the most cutting edge technologies

Imagine going to school and getting to fly drones, program industrial robots, build AI systems, work on smart manufacturing systems, and operate a fleet of self-driving cars? That’s what modern Career and Technical Education programs look like.

For students who want to take a break from the textbooks and Chromebooks and dive into emerging technology for a few hours a day, CTE is a great fit.

Emerging tech learning in action

Whitehall School District’s Emerging Technologies Laboratory is a great example of a space where students take charge of their learning and get to independently explore cutting-edge tech. Students explore robotics, automation, applied AI, 3D scanning and 3D printing, drones, coding, and cutting-edge technologies while teachers act as facilitators.

Curiosity sparks innovation here, and these students are prepared to go into their post-secondary journey – whether higher education or direct to workforce – with knowledge and experience in futuristic systems.

Few other experiences in a high schooler’s journey will give them the opportunity to work with advanced technologies the way a modern CTE program does.

 Career and Technical Education Benefits All Students

Career and Technical Education is no longer a niche pathway or an alternative option. It’s a core strategy for preparing students for what comes next. Whether a student plans to enter the workforce, attend a technical college, or pursue a four-year degree, CTE provides context, relevance, and real skills that traditional coursework alone often cannot. The schools highlighted here prove that when CTE is treated as rigorous, modern, and career-connected, students don’t just stay engaged, they leave better prepared for work, further education, and a rapidly changing economy.

During CTE Month, the takeaway is simple: every student benefits when hands-on learning, industry relevance, and emerging technology are part of the educational experience.

The post 6 Reasons Every Student Benefits from Career and Technical Education appeared first on ATS Midwest.

For educators, this shift is more than a buzzword. It changes what your students need to know. They still need mechanical and electrical skills, but they also need to understand networks, smart sensors, data flows, and how AI helps factories run faster, safer, and more efficiently.

The good news: you can teach all of this in a classroom or lab using smart manufacturing trainers built for education, not just for industry.

This article explores how AI-powered smart manufacturing works, how it fits into the edge-to-cloud continuum, and how Discover AI’s Smart Manufacturing Experience—built on Amatrol’s 990-SD10, 990-SM10, and tabletop mechatronics smart factory with FANUC—gives students real, hands-on experience with the same concepts used in advanced facilities.

What AI in Manufacturing Actually Looks Like on the Floor

Smart manufacturing is what happens when traditional automation meets connected data and AI. Instead of running blind, equipment is instrumented with smart sensors that measure temperature, vibration, flow, position, power, and more. PLCs and controllers collect this data, execute control logic, and send key information to higher-level systems.

In a smart factory, you will see:

• Smart sensors continuously streaming real-time data about machine health and process conditions.
• PLCs and industrial controllers executing logic based on that data—starting conveyors, rejecting bad parts, adjusting speeds, or stopping equipment when something looks unsafe.
• Condition-monitoring systems tracking energy use, temperature, and vibration to spot early signs of failure.
• Manufacturing execution systems (MES) and cloud platforms analyzing data over hours, days, and weeks to find patterns and improve performance.

Artificial intelligence fits into this picture in several ways. AI models can detect anomalies in sensor data that indicate developing faults. Machine learning can help predict when a machine will fail so maintenance happens before downtime. AI-enabled analytics can highlight bottlenecks, optimize changeovers, and suggest better production schedules.

Tools like Squeaks by iGear add another layer: they connect machines, data, and people. Operators receive targeted alerts, dashboards, and “digital callouts” when something needs attention. Instead of walking the floor asking, “What’s wrong?”, teams see issues in real time and respond with data, not guesswork.

This combination—sensors, automation, analytics, and AI-driven insights—is what your students will encounter in real plants.

Smart Manufacturing Across the Edge-to-Cloud Continuum

A useful way to teach smart manufacturing is through the edge-to-cloud continuum. It helps students see where decisions happen and how data flows.

Edge ➡️ Control System ➡️ Fog ➡️ Cloud

Let’s break that down.

Edge

This is where the physical world meets the digital world. Smart sensors, photoeyes, ultrasonic sensors, vibration probes, RFID readers, and power meters sit on the machine. They measure what is happening and send signals instantly.

Control

PLCs, drives, HMIs, and robot controllers make real-time decisions. They take sensor input and run the ladder logic, function blocks, or structured text that controls actuators, valves, motors, and robots.

Fog

This layer aggregates data from multiple machines or cells. It might be an industrial PC, an on-premise server, or an edge gateway. It does local analytics, buffering, and routing so data is ready for higher-level systems without overloading the network.

Cloud

This is where long-term storage and deeper analysis live. Cloud platforms host dashboards, historical trends, AI models, and enterprise tools. They analyze energy use, quality trends, OEE, and downtime across the entire facility, or across multiple sites. This is where your students can learn how to identify and minimize waste and optimize throughput in a manufacturing business.

In a smart manufacturing environment, students need to understand how each layer behaves: why some decisions must be made at the edge in milliseconds, why controllers handle safety and core logic locally, how fog and cloud systems aggregate data to support AI, reporting, and continuous improvement.

When you map your lab equipment to this framework, students stop seeing “a trainer” and start seeing a real factory, just scaled down for learning.

What Students Should Learn About AI in Manufacturing

You do not need to turn every student into a data scientist, but they should leave your program with a grounded understanding of how AI and data are used in production environments.

Key concepts include:

• Smart sensing and connectivity: How IO-Link devices, RFID, and industrial sensors provide rich, structured data instead of simple on/off signals.
• Industrial control: How PLCs interpret inputs, execute control logic, and drive outputs in real time.
• Condition monitoring and dashboards: How tools like Amatrol’s smart manufacturing systems and Squeaks present live data, alerts, and performance metrics.
• Edge analytics and AI: How algorithms at the cell or line level can detect anomalies, support predictive maintenance, and adjust processes automatically.
• Cloud analytics and continuous improvement: How longer-term data (energy, throughput, scrap, downtime) supports improvement projects and strategic decisions.

Most importantly, students need to understand how humans stay in the loop. AI may flag an abnormal pattern in vibration, but a technician still decides what maintenance to perform. A dashboard might highlight a bottleneck, but a manufacturing engineer still chooses which process change to implement.

Smart manufacturing is not about replacing people. It is about giving them better information.

Inside the Smart Manufacturing Experience with Discover AI

Discover AI’s Smart Manufacturing Experience is built around Amatrol’s smart manufacturing platforms. With eLearning, curriculum and hands-on learning systems combined, students get a complete, connected picture—from individual sensors to an integrated smart factory cell.

Smart Sensors with the Amatrol 990-SD10

You begin at the edge with the 990-SD10 Smart Machine Sensor Learning System. Here, you work directly with smart sensors and communication protocols that are standard in industry.

You configure and calibrate IO-Link-enabled devices, RFID readers, and a variety of smart sensors—capacitive, inductive, ultrasonic, linear position, vibration, temperature, and photoelectric. Using the included software, you set parameters, read diagnostic data, and see how these devices transmit rich information, not just simple on/off states.

Students see firsthand how a smart sensor can report signal quality, temperature, and status codes in addition to the process variable. That is the starting point for any AI or analytics upstream: good data.

System-Level Monitoring with the Amatrol 990-SM10

Next, you move into the fog and early cloud layers with the 990-SM10 Smart Manufacturing Workstation. This portable system integrates BorgConnect, an Allen-Bradley Micro820 PLC, load cells, current sensors, and wireless temperature sensors into a complete smart manufacturing cell.

Here, you program the PLC using Rockwell’s Connected Components Workbench, build logic that responds to sensor data, and connect the system to BorgConnect for real-time monitoring. You see live dashboards for temperature, energy use, and production metrics. You experiment with alerts and thresholds. You begin to understand how system-level data feeds operator decisions.

This is where a tool like Squeaks by iGear fits perfectly into the story. Squeaks can take the data generated by smart sensors and PLCs and route it to the right people at the right time, via targeted messages and notifications. In an industrial setting, an operator might receive an alert about a rising motor temperature or a drop in output. In your program, students can see how data moves from sensors to dashboards to people—and how that loop supports smarter, safer operations.

Integrated Smart Factory with Tabletop Mechatronics and FANUC Robot

Finally, you bring everything together with an Amatrol tabletop mechatronics smart factory that includes a FANUC 6-axis industrial robot. This is where students see smart manufacturing in motion.

You program the robot with a teach pendant, jog axes, create motion programs, and integrate the robot into a larger automated sequence. You configure conveyors, actuators, and smart sensors around the cell. You connect the system to supervisory software and visualize what is happening in real time.

This tabletop factory becomes a small-scale digital thread:

• Sensors and actuators at the edge capture what is happening in the cell.
• PLCs and robot controllers handle motion and logic at the control layer.
• Smart manufacturing platforms and tools like BorgConnect and Squeaks aggregate data, visualize performance, and feed alerts at the fog and cloud layers.

Students learn how a real smart factory operates, but in a form factor that fits your lab.

How This Fits Inside Discover AI

Within the Discover AI program, smart manufacturing is one of several 45-hour Experiences designed to teach applied artificial intelligence through real technology, not just theory.

Students begin with an Intro to Applied AI course that covers core concepts: perception, decision-making, edge-to-cloud, data, and ethics. Then they step into the Smart Manufacturing Experience, where those concepts show up in a concrete way:

• Smart sensors demonstrate how data is captured at the edge.
• PLCs and controllers show how real-time decisions are made.
• Condition-monitoring tools, dashboards, and Squeaks illustrate how information becomes insight.
• The tabletop smart factory with FANUC reveals how all of it comes together in a real production scenario.

The learning is student-driven and project-based. Learners design experiments, configure sensors, develop control logic, and analyze data. They see how AI-ready data pipelines work in practice, and how human decision-makers stay in the loop.

By the end of the Experience, your students are no longer just “running a trainer.” They are thinking like technicians, engineers, and analysts in a smart factory—using real equipment, real data, and real workflows.

Smart manufacturing is already here in industry. With Discover AI and Amatrol’s smart manufacturing systems, you can bring that same intelligence into your classroom and prepare students for the factories they will actually work in.

If you are ready to add smart sensors, data, and AI-driven manufacturing to your program, explore how Discover AI’s Smart Manufacturing Experience can fit into your CTE or STEM pathway. Contact ATS Midwest to get started!

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