Mechanical + Mechatronics · machines · sensors · control

Programming for Mechanical and Mechatronics Engineers: Where Coding Fits

See exactly where code enters a real engineering system—from pulley calculations and machine measurements to sensors, PLCs, feedback control, CAD automation and robots.

Start with the engineering problem Follow real machine workflows Learn step by step

See practical examples See the learning sequence

machine_control.py

# measure the physical system temperature = 47 if temperature > 45: fan = "ON" # measurement → decision → action

Prepared byNewton Institute of Technology Editorial Team

Institutional / Technical reviewEngineer Simon Barongo

AudienceSchool leavers · engineering students · beginners

Last reviewed24 October 2026

Quick answer

Where does programming fit in mechanical and mechatronics engineering?

Programming becomes useful when an engineering task needs to be calculated, measured, analysed, simulated, controlled or automated. Mechanical engineers can use code for calculations, measurements, simulations and design automation. In mechatronics, code can also read sensors and control motors, valves, cylinders and other actuators. Current mechatronics criteria explicitly include embedded systems, control, automation, PLCs, programming, instrumentation and sensors. [1]

MechanicalCalculate + analyseAutomate equations, process measurements, simulate systems and streamline design work.

MechatronicsSense + controlRead sensors, apply logic and command motors, cylinders, valves and other actuators.

Core patternMeasure → decide → actStart with the physical engineering problem, then use code where it adds a clear function.

Six worked engineering examples

See all six practical examples before you start

Each example shows a different place where programming meets a physical engineering task. Open any example directly, or read them in order from calculation to sensing, automation, design and robotics.

Pulley speed comparison

Turn a belt-drive speed relationship into reusable code and compare several pulley sizes.

Open practical example 1

Temperature-controlled fan

Read a temperature sensor and make a cooling fan respond automatically.

Open practical example 2

PLC conveyor sequence

Translate a conveyor and pneumatic-cylinder sequence into industrial control logic.

Open practical example 3

CAD design automation

Use design parameters to create a family of mounting-plate variants consistently.

Open practical example 4

Motor speed feedback

Measure actual motor speed, compare it with the target and correct the error repeatedly.

Open practical example 5

Robot obstacle avoidance

Connect distance sensing, motor commands, traction and gearbox behaviour in one robot response.

Open practical example 6

Start with the engineering

Turn a familiar mechanical calculation into reusable code

Suppose an electric motor drives a machine through two pulleys and a belt. The motor runs at 1,440 rpm, the driving pulley is 80 mm, and the driven pulley is 160 mm.

Driven speed = Motor speed × Driving pulley diameter ÷ Driven pulley diameter

Identify the inputs

Motor speed = 1,440 rpm. Driving pulley = 80 mm. Driven pulley = 160 mm.

Apply the relationship

1,440 × 80 ÷ 160 gives an approximate driven speed of 720 rpm when belt slip is ignored.

Put it into code

Store the speed and pulley sizes as variables so the same calculation can be reused.

Compare designs

Change the driven-pulley size and calculate several possible machine speeds in seconds.

pulley_speed.py

motor_speed = 1440 drive_pulley = 80 driven_pulley = 160 output_speed = motor_speed * drive_pulley / driven_pulley print(output_speed) # output: 720 rpm

Practical example 1 · mechanical calculation

Compare several pulley sizes with one small program

Keep the motor speed at 1,440 rpm and the driving pulley at 80 mm. Compare driven pulleys of 120 mm, 160 mm and 200 mm.

Driven pulley	Approximate machine speed	What changes?
120 mm	960 rpm	Smaller driven pulley → higher speed
160 mm	720 rpm	Reference case
200 mm	576 rpm	Larger driven pulley → lower speed

On a small screen, swipe horizontally to compare all three columns.

Write the formula

Begin with the mechanical relationship you already understand.

Engineering rule

Store the inputs

Use variables for motor speed and both pulley diameters.

Inputs

Repeat the calculation

Run the same formula for a list of different driven-pulley sizes.

Automation

Compare the result

Use the output to see how the design choice changes machine speed.

Engineering decision

What programming adds

One familiar mechanical formula has become a tool that can compare several designs quickly and consistently.

Mechanical measurements

Use code to process what the machine is telling you

Mechanical engineers regularly work with temperature, speed, pressure, force, displacement, vibration and motor-current measurements. A program can process large groups of readings consistently.

Program

Handle the measurements

Store many readings.
Calculate averages and maximum values.
Compare operating periods.
Prepare values for tables or plots.

Engineer

Explain the behaviour

Is temperature still rising?
Does it stabilise?
Does higher speed produce more heat?
Does vibration change at the same time?

bearing_temperature.py

import numpy as np temperature = np.array([34.2, 34.8, 35.1, 35.9, 36.4, 37.0]) print(np.mean(temperature)) print(np.max(temperature))

NumPy provides array-based mathematical and statistical operations, including arithmetic means and maximum-value operations. [2]

Practical example 2 · sensor to actuator

Make a cooling fan respond to temperature

A motor operates inside an enclosure. The fan should start above 45°C and switch off after the temperature falls below 40°C.

Measure

The temperature sensor reports 47°C.

Compare

The controller checks the reading against the 45°C switching point.

Act

Because the measured value is high, the controller switches the fan on.

Measure again

At 39°C the controller switches the fan off and continues monitoring.

cooling_control.cpp

if (temperature > 45) { fanOn(); } if (temperature < 40) { fanOff(); }

Arduino’s official documentation covers analog and digital inputs, PWM and motor or servo control concepts used in this kind of sensor-controller-actuator learning. [3]

The reusable control pattern

Measure → decide → act → measure again. The same pattern can be used for temperature, pressure, speed, position and many other machine variables.

Practical example 3 · industrial automation

Program a conveyor sequence before writing the PLC code

A component moves along a conveyor. When it reaches a sensor, the conveyor stops, a pneumatic cylinder moves the component, the cylinder retracts, and the conveyor restarts.

Run

The conveyor moves the component toward the sensing position.

Motor ON

Detect

The part sensor detects the component and the controller stops the conveyor.

Sensor → decision

Move

The pneumatic cylinder extends, pushes the component, then retracts.

Actuator sequence

Reset

When the cylinder reaches home position, the conveyor starts again.

Cycle repeats

conveyor_sequence.txt

IF part_detected: stop conveyor extend cylinder IF cylinder_extended: retract cylinder IF cylinder_home: start conveyor

Current mechatronics criteria explicitly include industrial controls, automation and PLCs as part of the field. [1]

Conveyor extension · sorting by height

Let the controller decide where each component goes

Input

Measure the component

Conveyor moves the part.
Height sensor measures it.
Controller receives the reading.
Mechanical frame keeps the part aligned.

Output

Direct the component

If height is above 80 mm, activate the sorting cylinder.
Otherwise let the part continue.
The actuator performs the physical movement.
The cycle resets for the next part.

Mechatronics is integration.

The control logic works because the sensor position, mechanical frame, actuator movement and programmed sequence have been designed to operate together.

Practical example 4 · mechanical design automation

Generate a family of mounting plates from design parameters

Programming is also useful before a machine is built. Suppose the same mounting-plate geometry is used in several products, but the width, length and hole diameter change.

Version	Width	Length	Hole diameter
A	100 mm	150 mm	8 mm
B	120 mm	180 mm	10 mm
C	150 mm	220 mm	12 mm

On a small screen, swipe horizontally to compare all four columns.

Define the geometry

Decide which features remain common across every plate.

Design intent

Choose the parameters

Width, length, hole size and spacing become controlled inputs.

Parameters

Apply the script

The CAD API reads the selected values and updates the supported model operations.

Automation

Create variants

Reuse the same design logic for several product sizes instead of rebuilding each model.

Repeatable design

The Autodesk Fusion API supports scripts and add-ins, including Python-based workflows, for programmatic design operations. [4]

Practical example 5 · feedback control

Keep a motor close to its target speed when conditions change

A motor should run at 1,000 rpm, but a speed sensor reports 920 rpm. The error is therefore 80 rpm.

Set the target

Required speed = 1,000 rpm.

Measure

The speed sensor reports 920 rpm.

Calculate the error

1,000 − 920 = 80 rpm below the target.

Correct and repeat

The controller changes its command, then measures the new speed again.

Changing load

Real machines do not stay constant

A motor can slow under higher load.
A conveyor can meet extra resistance.
A robot can reach a rough surface.
A slope can increase the required wheel torque.

Feedback

The controller responds to the new condition

Measure the actual behaviour.
Compare it with the target.
Adjust the command.
Repeat while the system operates.

MATLAB and Simulink control-system tools support plant modelling, controller design, system analysis, verification and deployment of control algorithms. [5]

Practical example 6 · robotics

A robot avoids a box only when mechanics and software work together

A small mobile robot has motors, gearboxes, wheels, encoders, distance sensors and a controller. The distance sensor first reports 1.2 m, then 0.55 m, then 0.25 m.

Sense

At 1.2 m the obstacle is far enough away, so the robot continues.

Distance = 1.2 m

Prepare

At 0.55 m the obstacle is closer and the control logic prepares to slow.

Distance = 0.55 m

Decide

At 0.25 m the controller commands lower wheel speed and a right turn.

Decision

Move

Traction, motor torque and gearbox ratio determine how the physical robot performs the turn.

Mechanical response

Robotics is not software alone.

The robot moves properly only when its mechanics, electronics, sensing and software agree with one another.

O*NET describes mechatronics engineers as working with automation, intelligent systems and industrial controls, including the selection or application of sensors and control devices. [7]

Study the behaviour before building everything

Use simulation to test engineering ideas digitally

Consider a suspension represented by mass + spring + damper. The engineer wants to see how the system responds when the wheel passes over a bump.

Build the model

Represent the mass, spring, damper and road input mathematically.

Run the case

Calculate how displacement and velocity change with time.

Change a parameter

Adjust spring stiffness, damping, mass or input conditions.

Compare

Study several design cases before committing to a physical prototype.

Simulink supports multidomain dynamic-system modelling, simulation, Model-Based Design and code-generation workflows for embedded systems. [6]

Choose by engineering task

Programming tools follow the job the engineer needs to do

Engineering task	Typical programming direction	Where it fits
Numerical calculations	Python or MATLAB	Repeated engineering equations and numerical work
Engineering measurements	Python and numerical libraries	Arrays, summaries and analysis of recorded values
Modelling and simulation	MATLAB/Simulink or related tools	Dynamic systems, models and control studies
Microcontroller systems	C/C++ and embedded programming	Sensors, local logic and actuator control
Factory automation	PLC programming	Industrial sequences, machines and process control
Robotics	Python, C++ and robotics frameworks	Sensing, decisions and motion
CAD automation	CAD APIs and scripting	Parameter-driven design and repetitive model operations
Test automation	Data-acquisition and programming tools	Measurement, recording and repeatable test workflows

On a small screen, swipe horizontally to compare all three columns.

The task comes first.

A numerical-analysis problem needs a different tool from an embedded controller, production-line sequence or CAD-automation workflow.

Same code idea, different engineering emphasis

Mechanical engineering and mechatronics use programming differently

Mechanical engineering

Analyse, design and automate engineering work

Automate calculations.
Analyse measurements.
Run simulations.
Automate CAD work.
Compare design options.
Process machine data.

Mechatronics engineering

Make the physical system sense and respond

Program machine behaviour.
Read sensors directly.
Implement control logic.
Control motors and actuators.
Coordinate machine sequences.
Integrate mechanics, electronics and software.

Build the skill in engineering order

A practical seven-step learning sequence

Automate one calculation

Take a pulley-speed, power, efficiency, area or volume relationship you already understand.

Change the inputs

Run the same equation for several values and compare the outputs.

Work with measurements

Store temperature, speed or another physical measurement and calculate useful summaries.

Read a sensor

Move from typed numbers to a physical temperature, distance or position input.

Control an output

Use the measurement to operate an LED, fan, servo or small motor.

Input → logic → output

Build a sequence

Combine sensing, motor control and actuator actions into a complete machine cycle.

Automation

Specialise by engineering need

Move toward CAD automation, PLCs, robotics, testing, simulation or control according to the work you enjoy.

Engineering direction

First mechanical coding project

Build the pulley-speed calculator into a small engineering tool

Enter three inputs

Motor speed, driving-pulley diameter and driven-pulley diameter.

Inputs

Calculate output speed

Apply the mechanical speed relationship consistently.

Calculation

Compare several designs

Allow more pulley sizes and display the results in a table.

Comparison

Explain the result

Plot pulley size against output speed and describe the mechanical pattern.

Interpretation

First mechatronics coding project

Build a temperature-controlled fan

Read

Connect the temperature sensor and obtain the measured value.

Display

Show the temperature so you can confirm what the controller is reading.

Connect the fan

Add the output device that will change the physical condition.

Control it

Use the measured temperature to decide when the fan should run.

One project combines measurement + electronics + programming + mechanical action.

Quick questions

Programming for mechanical and mechatronics engineers: FAQs

Do mechanical engineers use programming?

Yes. Mechanical engineers use programming for calculations, simulation, test-data processing, machine monitoring and design automation.

Where is coding used in mechanical engineering?

Common uses include numerical calculations, experimental-data analysis, CAD automation, simulation and machine-data processing.

Does mechatronics require programming?

Programming is an important part of modern mechatronics because the field integrates mechanics with sensors, controllers, electronics and automation. [1]

How does programming control a machine?

A sensor provides a measurement. The controller processes it and sends a command to an actuator such as a motor, fan, valve or cylinder.

Is Python useful for mechanical engineering?

Yes. Python is useful for calculations, numerical analysis, processing engineering measurements and automating repeatable computational work. NumPy provides numerical and statistical operations for this type of task. [2]

What is PLC programming used for?

PLC programming controls industrial machines and automated sequences involving equipment such as conveyors, motors, cylinders, pumps and manufacturing systems.

Where does programming fit in robotics?

Programming connects sensing, decision-making and motion. Software interprets measurements and sends commands to motors and other actuators.

The main idea

The most useful code starts with a clearly understood engineering problem

Programming can calculate, measure, analyse, simulate, decide, control and automate. Mechanical engineers often use code to analyse, design and improve physical systems. Mechatronics engineers also use it to make machines respond to measurements and commands. The most useful code is usually written after the engineering problem is understood.

Build practical programming skills

Register for Full-Stack Software Development at Newton Institute of Technology

Programming can add a useful digital layer to mechanical engineering, mechatronics, automation, robotics and other technical fields. Newton Institute of Technology’s Full-Stack Software Development programme develops practical software-development skills through structured learning, projects and applied problem-solving. The programme includes 8 months of institutional training followed by a compulsory industrial attachment of at least 3 months. [8]

Institutional training8 months

Industrial attachmentCompulsory · 3+ months

ApplicationEngineering + software skills

References

ABET. Criteria for Accrediting Engineering Technology Programs, 2026–2027 — Mechatronics Engineering Technology and Similarly Named Programs. Used for mechatronics curriculum coverage of embedded systems and control, mechanics, industrial controls, automation, PLCs, programming, instrumentation and sensors. ABET.
NumPy Developers. NumPy Statistics. Used for numerical-array operations including arithmetic means and other statistical functions. NumPy documentation.
Arduino. Arduino Documentation — Learn and Language Reference. Used for microcontroller input/output, PWM, programming and actuator-control concepts. Arduino Learn.
Autodesk. Fusion API User’s Manual. Used for scripting, add-ins and programmatic CAD workflows, including Python-based API use. Autodesk Fusion API.
MathWorks. Control Systems — MATLAB & Simulink. Used for plant modelling, controller design, control-system analysis, verification and deployment of control algorithms. MathWorks Control Systems.
MathWorks. Simulink Documentation. Used for multidomain dynamic-system modelling, simulation, Model-Based Design and embedded-system workflows. Simulink documentation.
O*NET OnLine. Mechatronics Engineers — 17-2199.05. Updated 2026. Used for current occupational information on automation, intelligent systems, industrial controls, sensors and control devices. O*NET OnLine.
Newton Institute of Technology. Full-Stack Software Development Course. Used for the current NIT programme duration and compulsory industrial attachment. Newton Institute of Technology.

Prepared by: Newton Institute of Technology Editorial Team · Institutional / Technical review: Engineer Simon Barongo · Last reviewed: 24 October 2026.