Step onto a modern production floor and you see it immediately: sensors on every machine, data dashboards glowing on the wall, and lines that adjust themselves in real time. Parts still move, robots still weld, but the real work is happening in the data. That is smart manufacturing—and artificial intelligence is woven through every layer.
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.
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:
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.
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.
Let’s break that down.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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:
Students learn how a real smart factory operates, but in a form factor that fits your lab.
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:
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!
