Gains:
- Ability to distinguish at which stages of the mechatronics workflow (design, code, analysis) AI adds value and which decisions should remain with the engineer
- Ability to apply the principles of functional safety (SIL/PL), pre-run verification in hardware and testing in simulation
- Ability to identify the risk of AI output damaging the physical system and the layered verification discipline that reduces this risk
Mechatronics stands at the intersection of mechanics, electronics, control and software. Your day as an engineer; It involves writing the driving code of a servo motor, filtering the noise of a sensor, tuning a PID controller, establishing a PLC logic, and verifying that all of this works safely in the physical world. AI can be an accelerator in each of these tasks: generating draft code, helping you solve an equation, extracting a pattern from a data set, guiding you to debug a bug. But there is one critical difference in mechatronics: what you produce does not stay on the screen, it turns a motor, opens a valve, moves a lever in the physical world. So the rules for using AI here are stricter than in pure software. In this unit we establish how to safely embed AI into the mechatronics workflow and what decisions should never leave the engineer.
Where Does AI Add Value in Mechatronics and Where Does It Not?
Clearly delineating the role of AI in mechatronics is the first step towards both efficiency and safety. The table below shows the position of AI in a typical mechatronics project.
Stage
The role of AI
The decision left to the person
concept/design
Generating alternatives, establishing equations, literature summary
Architecture selection, security goal
writing code
Draft driving/reading code, skeleton
Register accuracy, timing, testing
Analysis
Data summary, pattern, anomaly suggestion
Physical interpretation, decision threshold
verification
Test scenario suggestion, checklist
Field operation approval
Documentation
Report draft, comment line
Technical accuracy, signature
The pattern here is one: AI gives speed, engineer ensures accuracy and security. AI can write an engine driving code in 30 seconds; but it is the engineer who decides whether that code will burn the driver due to a wrong PWM frequency or an incorrect direction bit.
Tip: Think of AI like a “senior intern who hasn’t seen the field.” His ideas are quick and often good; But before you touch the board, you test every output.
Physical Risk: Difference from Software
In a web application, faulty code crashes a page; user refreshes, continues. In mechatronics, faulty code hits an actuator against a limit switch, breaks a gearbox, hurls a robot arm toward the operator. The risks are concrete:
- Overcurrent/voltage: Wrong PWM or missing current limit will burn the driver and motor.
- Runaway: Wrong signal or distorted feedback leads to uncontrolled acceleration.
- Timing violation: If a real-time loop is delayed, the control becomes unstable.
- Safety bypass: AI may unknowingly suggest code that bypasses interlock logic.
None of these risks are completely eliminated by "reading the code once". That's why verification in mechatronics is not a single step, but a layered process.
Layered Authentication Framework
Pass the AI output through the following layers before receiving it into the physical system. Each layer is there to capture what the previous one missed.
1. Static review: Read the code/logic line by line; register, unit, sign control.2. Unit/logic testing: Test functions in isolation (e.g. kinematic calculation with known value).3. Simulation (pre-HIL): Run on model; Observe step response, stability, limit violation.4. Limited hardware testing: Current/speed limited, E-stop accessible, low power start-up.5. Gradual activation: Increase load and speed step by step; measure and compare at every step.
For example, for a servo position control: first you verify the calculation with a known angle in the hand (layer 2), then you simulate the motor model in Python and see the overshoot (layer 3), then you fix the motor on the table and try a small movement with a low current limit (layer 4), lastly you attach the load and accelerate to full speed (layer 5). AI can assist with each of these steps; but the engineer presses the "run" button.
Functional Safety: SIL and PL in Brief
You need to know two standard concepts in security critical systems. SIL (Safety Integrity Level, 1-4) under IEC 61508 / IEC 62061 and PL (Performance Level, a-e) under ISO 13849 in machinery safety quantify how reliable a safety function should be.
concept
scale
what does it say
DELETE
1 (low) – 4 (high)
Hazardous failure probability target of the safety function
P.L.
a (low) – e (high)
Required performance level of the machine safety function
The key point is that if a safety function (e.g. stopping the engine with E-stop) carries a specific SIL/PL target, the design, verification and documentation of that function is carried out according to the requirements of the standard. AI cannot make this assessment for you and cannot take responsibility. AI can summarize relevant items or produce a draft checklist; but the declaration of conformity is the responsibility of the engineer and the organization.
Caution: Always verify the substance number, threshold value or formula that AI gives about its safety standards from the official standard text. AI can plausibly hallucinate standard items; Basing a security-critical decision on an unverified AI output is unacceptable.
Weak Prompt / Strong Prompt
In mechatronics, the quality of the prompt directly affects the security of the output. A contextless request produces generic code that doesn't know your hardware.
WEAK:"Write me a motor control code."(Result: which board? Which driver? Which voltage? Unknown; blind code.)STRONG:"On STM32F103 (HAL library), write code to control a DRV8825 stepper motor driver. NEMA17 motor, 200 steps/rev, 1/16 microstep.STEP pin PA0, DIR pin PA1, EN pin PA2. Limit it to a maximum of 3000 steps/sec. Be non-blocking (do not use delay), generate steps with TIM2 interrupt. Explain what each function does with a comment line and list your assumptions at the beginning."
Powerful prompt; It gives the card, driver, pins, limits and architectural constraint (non-blocking). This narrows down the space the AI has to “guess” and the output becomes verifiable.
Mini Case
Deniz, an R&D engineer, has the AI write the speed control code for a new conveyor. The AI produces clean-looking code and directly accelerates the engine to full speed in the main loop. Instead of loading the code as is, Deniz applies layered verification: first it reads the code and notices that there is no ramp-up; If the motor suddenly accelerates to full speed, there will be mechanical shock and current surge. “Add S-curve speed profile and limit maximum current to 4A,” it gives feedback to the AI. Then, it checks the current profile with a simple simulation in Python, then runs the motor with no load and current limit. On the first try, he discovers that the encoder direction is connected in reverse; Limited hardware testing, not simulation, captures this. Result: The AI returned a quick sketch, but three separate layers of verification debugged three separate issues and the hardware was not damaged at all.
Common Mistakes
- Loading AI output directly into hardware without simulation or limited testing.
- Requesting generic code without giving context of card, driver, pin and limit.
- Accepting safety standard items/thresholds without verifying from the AI's memory.
- Postponing e-stops and lockings as "I'll add them later" and conducting the first test without security.
- Considering AI-generated code as validated because it “seems to work.”
- Forgetting to put physical constraints such as acceleration, current/speed limit on the prompt.
In summary
- AI adds speed in mechatronics; Accuracy, safety and fielding approval remain with the engineer.
- Physical risk (overcurrent, reverse direction, timing violation) is different and concrete than software failure.
- Layered verification (static → volume → simulation → limited hardware → gradual deployment) is mandatory.
- Evaluation and documentation of functional safety objectives such as SIL/PL is the human responsibility.
- Powerful prompt; Contains the board, driver, pins, limits, and architectural constraint as context.
- Security standard information is always verified from official source; AI's memory cannot be trusted.
Application task
For a real mechatronic component you have (e.g. a stepper motor + driver), fill out the "strong prompt" template above: write down the board, driver, pins, voltage, current and speed limits. Have the AI generate a non-blocking driving code with this context. Then pass the output through the first three layers of the layered verification framework: (1) read the code line by line and find at least two potential risks, (2) manually verify a calculated value (e.g. step period at a given speed), (3) do a simple simulation or dry run if possible. Note which layer captures which problem.