The Full Mechanical Engineer’s AI Dictionary (2025 Edition)
The Full Mechanical Engineer’s AI Dictionary (2025 Edition)
The Full Mechanical Engineer’s AI Dictionary (2025 Edition)
Dr. Maor Farid, Co-Founder & CEO at Leo AI
Introduction: A New Era for Mechanical Engineers
Every major leap in mechanical engineering began with mastering a new language.
CAD gave us the language of digital geometry, PLM reshaped how we manage complexity, and FEA brought simulation into everyday workflows.
Now, in 2025, cutting-edge AI methodologies are redefining how mechanical engineering professionals design, build, and innovate - addressing energy and biomedical challenges, accelerating the manufacturing process, and driving transformative change across mechanical systems.
But here’s the catch: AI isn’t just another tool. It’s a new language - one that connects vast datasets, unlocks knowledge from research papers, and enables AI-powered workflows that deliver optimized solutions and expand design possibilities.
To truly harness its power, engineers must understand that language.
That’s exactly why we created The Full Mechanical Engineer’s AI Dictionary - a practical guide for professionals navigating the rapidly evolving landscape of AI applications essential to mechanical engineering domains. It bridges the gap between traditional engineering thinking and the next era of intelligent tools, giving you back valuable time to focus on innovation and solve harder problems.
(1) Foundations: The Building Blocks of AI
✔️Artificial Intelligence (AI)
AI is the science of building systems that perform tasks requiring human-like intelligence - solving problems, making decisions, and recognizing patterns.
In mechanical engineering, AI is used to automate repetitive workflows, analyze vast datasets, accelerate decisions, and optimize design processes.
Analogy: Think of designing a CNC machine - but instead of machining metal from drawings, you’re machining decisions from data.
✔️ Machine Learning (ML)
Machine Learning is a branch of AI where systems learn patterns from examples rather than hard-coded rules.
Example: Detecting bearing wear from vibration signals or predicting fatigue before failure. As more data is collected, models improve, enabling engineers to reduce waste, meet performance goals, and improve quality.
✔️ Deep Learning (DL)
Deep Learning is a subset of ML that uses multi-layer artificial neural networks to extract progressively higher-level features from raw data - like refining a design from sketch → components → tolerances.
It powers quality control systems, processes sensor data in real time, detects surface defects, and evaluates material properties.
✔️ Reinforcement Learning (RL)
Reinforcement Learning is about trial and error - models learn strategies by receiving rewards for good decisions and penalties for poor ones.
RL is revolutionizing autonomous systems, robotics, vehicles, and factory automation - optimizing navigation, energy efficiency, and safety.
✔️ Model (Learning Model)
A model is a mathematical function mapping inputs to outputs.In mechanics, stress = force / area is a simple model. AI models approximate much more complex relationships - like mapping an image to the probability it contains a crack or linking design parameters to performance requirements.
(2) Next, How Models Work: From Inputs to Outputs
✔️ Neural Networks (NN)
A neural network is a system of interconnected nodes (“neurons”) that can represent complex relationships - much like simple linkages in a mechanism can create surprisingly rich motion.
Weights and Biases: Like spring constants and preloads, they define how each neuron responds.
Forward Pass: Inputs (e.g., an image of a gear) are transformed layer by layer into outputs (“Probability = 0.90 this is a spur gear”).
In this shallow neural network, xix_ixi > x_i represents the inputs to the network. These elements are scalars and are stacked vertically, forming the input layer. Variables in the hidden layer are not visible in the input set. The output layer consists of a single neuron, and y^\hat{y}y^ > ŷ is the output of the neural network
A deeper neural network with four hidden layers. Think of it like a polynomial with more parameters - it can capture and learn more complex relationships between the input variables and the output value.
✔️ Loss Function
The loss function measures error between predictions and reality - like comparing simulation results to experimental data.
A common choice is Mean Squared Error (MSE): the squared differences between predicted and actual values averaged across the dataset.
Think of it as measuring squared deviation between predicted and actual stress at each mesh node and averaging the result.
L2 loss function - the error is calculated as the mean of the squared differences between the predicted (learned) and actual output values, also known as the ground truth (GT).
✔️ Backpropagation
The algorithm that lets models learn from mistakes. The error is propagated backward through the network to update weights and biases - like tracing a robotic arm’s error back through each joint to recalibrate it.
✔️ Optimizers
Optimizers adjust weights to minimize loss.
Gradient Descent is like walking downhill on an error surface.
Adam builds on this with momentum and adaptive step sizes for faster convergence.
(3) Next, Training and Evaluation: From Data to Decisions
Training Set: Historical data (sensor logs, simulations, past designs) used for learning.
Validation Set: Fine-tunes hyperparameters (learning rate, layers) and improves generalization.
Test Set: Unseen data that confirms the model learned principles, not just memorized.
Typical splits: 70% training / 15% validation / 15% test.
Epochs: Full passes over the training set.
Batches: Subsets for efficiency - like testing samples instead of full stock.
Overfitting: Memorizing noise - like memorizing every stress-strain curve but failing on a new alloy.
Underfitting: Too simple - like approximating a curved beam with a straight line.
Regularization: refers to techniques that help prevent overfitting, such as limiting model complexity, adding noise, or stopping training early. In mechanical terms, it’s similar to adding damping to prevent a structure from vibrating excessively at resonance.
Loss Function with Regularization:
First term: Mean Squared Error (MSE)
Second term: L2 penalty on large weights
λ (lambda): Regularization strength
By discouraging large weights, the model learns smoother, more generalizable patterns - rather than simply memorizing noise.
(4) Problems Models Solve Well
Classification: Predicting categories - e.g., normal vs faulty vibration signals.
Regression: Predicting continuous values - e.g., surface roughness from spindle speed and tool geometry.
Clustering: Discovering patterns in unlabeled data - e.g., bearing wear modes.
Dimensionality Reduction: Compressing data while preserving essentials - like reducing a 3D CAD to a few key dimensions.
Predictive Maintenance: Forecasting failures from sensor trends, shifting maintenance from reactive to proactive.
(5) Architectures Driving Change
✔️ Fully Connected Neural Networks (FC-NN / MLP)
Every neuron connects to every neuron in the next layer. These general-purpose networks are powerful but sometimes inefficient - like brute-force FEA methods. They’re widely used in early design exploration, simulation surrogates, and optimization workflows.
✔️ Convolutional Neural Networks (CNNs) - The Eyes of Quality Control
CNNs are specialized for spatial data like images. They detect features in surfaces and structures - essential in automated surface defect detection, weld seam inspection, and real-time quality control during the manufacturing process.
✔️ Recurrent Neural Networks (RNN, LSTM, GRU) - Time-Series Masters
RNNs are built for sequences. They “remember” previous steps, making them ideal for vibration analysis, fatigue cycles, and other time-dependent behaviors. LSTMs and GRUs extend this to capture long-term dependencies, much like accounting for hysteresis in materials over time.
✔️ Transformers - Unlocking Engineering Knowledge
Transformers use attention mechanisms to focus on the most relevant parts of input - like prioritizing critical load paths in a structure.They excel at reading research papers, summarizing technical documents, and surfacing design guidelines from vast datasets. Tools like Leo AI extend this by connecting transformer capabilities to engineering-specific data - linking decades of tribal knowledge from CAD models, PLM records, and structured documentation.
✔️ Tokens
Tokens are the smallest units a language model processes - words, subwords, or punctuation. More tokens mean more detail - but also more compute.Example: “Bolted connection” → “Bolted”, “connection”, and the space between them are three tokens.
(6) Generative AI: Expanding What’s Possible
✔️ Generative Design - AI as a Creative Partner
You define performance goals, constraints, and materials - and AI explores thousands of design possibilities.The result? Lightweight, non-intuitive geometries (like lattice structures) that meet mechanical requirements and manufacturing constraints while optimizing for weight, strength, and cost.
✔️ Digital Twins - Bridging Real and Virtual Worlds
A digital twin is a continuously updated, data-driven virtual model of a physical system - a turbine, robot, or vehicle.It helps engineers simulate wear, predict failures, and address energy and biomedical challenges long before they happen - all while refining mechanical systems for optimized solutions.
(7) LLMs vs LMMs: Two Different Species of AI
✔️ Large Language Models (LLMs)
LLMs like GPT, Claude, and Gemini are trained on massive amounts of general-purpose text. They’re excellent at writing, summarizing, and explaining - but they have major limitations in engineering:
No understanding of geometry or assemblies
No access to PDM/PLM systems or internal standards
Potential data security risks depending on vendor policies
They’re like very well-read junior engineers: great for context and communication but not built for engineering decisions.
✔️ Large Mechanical Models (LMMs) - Built for Mechanical Engineers
LMMs, like the one behind Leo AI, are different. They’re designed for mechanical engineering professionals and product teams. They process structured engineering data and CAD-aware context - like part relationships, assembly constraints, and performance requirements.
They don’t replace CAD or FEA tools - but they transform workflows by reducing overhead:
Answering technical questions using vetted sources
Suggesting parts and materials based on intent
Understanding assembly context from exported models
Surfacing internal standards and supplier data securely
Security is built in: LMMs can run in private environments with no external data transfer - critical for regulated industries and proprietary projects.
(8) Other Architectures and Approaches
Autoencoders: Compress and reconstruct data - like reducing a CAD assembly to key parameters.
GANs: Competing models that generate realistic designs over time.
Diffusion Models: Generate data by “denoising” noise - like machining material away to reach a final shape.
(9) Practical Extras
Hyperparameters: Settings like learning rate and layers, similar to mesh density or tolerance choices.
Bias & Variance: Balancing under- and overfitting - like stiffness vs. flexibility in design.
Embeddings: Numerical representations of objects (words, parts, signals) enabling smarter search across part libraries and documentation.
Transfer Learning & Fine-Tuning: Reusing and adapting pre-trained models to new tasks, saving valuable time and compute.
(10) How AI Drives Transformation
AI is reshaping every layer of mechanical engineering:
Energy: Optimizing turbines, improving efficiency, and addressing energy sustainability challenges.
Autonomous Systems: Reinforcement learning is revolutionizing autonomous systems with safer navigation and smarter control.
Biomedical: Designing lighter, smarter prosthetics and devices.
Manufacturing Process: Enhancing quality control, predictive maintenance, and material usage.
Documentation: Surfacing insights from thousands of archived research papers and standards.
Integration: Connecting CAD, PLM, and engineering data into continuous, AI-powered workflows.
And most importantly, AI gives engineers the one thing we always need more of: time - to design, innovate, and build better systems.
(11) Bringing AI Into Your Workflow
Leo AI is a purpose-built AI for mechanical engineers. It doesn’t just generate text - it understands mechanical context, interprets exported models, surfaces linked documentation, and connects relevant engineering knowledge without needing native plug-ins.
What it helps with:
CAD-aware Q&A and engineering context retrieval
Part and material discovery with engineering constraints in mind
Drafting BOMs, reports, and specifications from project data
Onboarding and knowledge reuse - making best practices accessible
Security-first deployments with zero-retention setups
It doesn’t replace CAD or FEA - it enhances them. Leo handles the heavy lifting so you can focus on solving complex problems and pushing the boundaries of what’s possible.
Feature | General AI (ChatGPT, Gemini, Claude) | Leo AI |
Context understanding | No awareness of CAD, assemblies, or mechanical constraints | Deep knowledge of mechanical design, CAD, tolerances, and workflows |
Engineering calculations | Limited or requires manual checking | Built-in validation with Python and references |
CAD integration | None | CAD-aware and designed to work alongside engineering workflows |
Data security | Prompts may be used to train models | Sensitive information stays secure inside your organization |
Workflow support | Text generation only | Assists with part search, onboarding, documentation, and repetitive tasks |
Final Thoughts
The evolving landscape of mechanical engineering is defined by cutting-edge AI methodologies that extend what’s possible - from automating routine tasks to unlocking entirely new design possibilities.
Those who learn how to harness AI-powered tools today will lead the next era of engineering tomorrow.
👉 Ready to step into the future of engineering? Try Leo AI today
👉 Join the MI Community - a global hub where engineers explore new AI tools, share workflows, and shape the future of mechanical design together.
Introduction: A New Era for Mechanical Engineers
Every major leap in mechanical engineering began with mastering a new language.
CAD gave us the language of digital geometry, PLM reshaped how we manage complexity, and FEA brought simulation into everyday workflows.
Now, in 2025, cutting-edge AI methodologies are redefining how mechanical engineering professionals design, build, and innovate - addressing energy and biomedical challenges, accelerating the manufacturing process, and driving transformative change across mechanical systems.
But here’s the catch: AI isn’t just another tool. It’s a new language - one that connects vast datasets, unlocks knowledge from research papers, and enables AI-powered workflows that deliver optimized solutions and expand design possibilities.
To truly harness its power, engineers must understand that language.
That’s exactly why we created The Full Mechanical Engineer’s AI Dictionary - a practical guide for professionals navigating the rapidly evolving landscape of AI applications essential to mechanical engineering domains. It bridges the gap between traditional engineering thinking and the next era of intelligent tools, giving you back valuable time to focus on innovation and solve harder problems.
(1) Foundations: The Building Blocks of AI
✔️Artificial Intelligence (AI)
AI is the science of building systems that perform tasks requiring human-like intelligence - solving problems, making decisions, and recognizing patterns.
In mechanical engineering, AI is used to automate repetitive workflows, analyze vast datasets, accelerate decisions, and optimize design processes.
Analogy: Think of designing a CNC machine - but instead of machining metal from drawings, you’re machining decisions from data.
✔️ Machine Learning (ML)
Machine Learning is a branch of AI where systems learn patterns from examples rather than hard-coded rules.
Example: Detecting bearing wear from vibration signals or predicting fatigue before failure. As more data is collected, models improve, enabling engineers to reduce waste, meet performance goals, and improve quality.
✔️ Deep Learning (DL)
Deep Learning is a subset of ML that uses multi-layer artificial neural networks to extract progressively higher-level features from raw data - like refining a design from sketch → components → tolerances.
It powers quality control systems, processes sensor data in real time, detects surface defects, and evaluates material properties.
✔️ Reinforcement Learning (RL)
Reinforcement Learning is about trial and error - models learn strategies by receiving rewards for good decisions and penalties for poor ones.
RL is revolutionizing autonomous systems, robotics, vehicles, and factory automation - optimizing navigation, energy efficiency, and safety.
✔️ Model (Learning Model)
A model is a mathematical function mapping inputs to outputs.In mechanics, stress = force / area is a simple model. AI models approximate much more complex relationships - like mapping an image to the probability it contains a crack or linking design parameters to performance requirements.
(2) Next, How Models Work: From Inputs to Outputs
✔️ Neural Networks (NN)
A neural network is a system of interconnected nodes (“neurons”) that can represent complex relationships - much like simple linkages in a mechanism can create surprisingly rich motion.
Weights and Biases: Like spring constants and preloads, they define how each neuron responds.
Forward Pass: Inputs (e.g., an image of a gear) are transformed layer by layer into outputs (“Probability = 0.90 this is a spur gear”).
In this shallow neural network, xix_ixi > x_i represents the inputs to the network. These elements are scalars and are stacked vertically, forming the input layer. Variables in the hidden layer are not visible in the input set. The output layer consists of a single neuron, and y^\hat{y}y^ > ŷ is the output of the neural network
A deeper neural network with four hidden layers. Think of it like a polynomial with more parameters - it can capture and learn more complex relationships between the input variables and the output value.
✔️ Loss Function
The loss function measures error between predictions and reality - like comparing simulation results to experimental data.
A common choice is Mean Squared Error (MSE): the squared differences between predicted and actual values averaged across the dataset.
Think of it as measuring squared deviation between predicted and actual stress at each mesh node and averaging the result.
L2 loss function - the error is calculated as the mean of the squared differences between the predicted (learned) and actual output values, also known as the ground truth (GT).
✔️ Backpropagation
The algorithm that lets models learn from mistakes. The error is propagated backward through the network to update weights and biases - like tracing a robotic arm’s error back through each joint to recalibrate it.
✔️ Optimizers
Optimizers adjust weights to minimize loss.
Gradient Descent is like walking downhill on an error surface.
Adam builds on this with momentum and adaptive step sizes for faster convergence.
(3) Next, Training and Evaluation: From Data to Decisions
Training Set: Historical data (sensor logs, simulations, past designs) used for learning.
Validation Set: Fine-tunes hyperparameters (learning rate, layers) and improves generalization.
Test Set: Unseen data that confirms the model learned principles, not just memorized.
Typical splits: 70% training / 15% validation / 15% test.
Epochs: Full passes over the training set.
Batches: Subsets for efficiency - like testing samples instead of full stock.
Overfitting: Memorizing noise - like memorizing every stress-strain curve but failing on a new alloy.
Underfitting: Too simple - like approximating a curved beam with a straight line.
Regularization: refers to techniques that help prevent overfitting, such as limiting model complexity, adding noise, or stopping training early. In mechanical terms, it’s similar to adding damping to prevent a structure from vibrating excessively at resonance.
Loss Function with Regularization:
First term: Mean Squared Error (MSE)
Second term: L2 penalty on large weights
λ (lambda): Regularization strength
By discouraging large weights, the model learns smoother, more generalizable patterns - rather than simply memorizing noise.
(4) Problems Models Solve Well
Classification: Predicting categories - e.g., normal vs faulty vibration signals.
Regression: Predicting continuous values - e.g., surface roughness from spindle speed and tool geometry.
Clustering: Discovering patterns in unlabeled data - e.g., bearing wear modes.
Dimensionality Reduction: Compressing data while preserving essentials - like reducing a 3D CAD to a few key dimensions.
Predictive Maintenance: Forecasting failures from sensor trends, shifting maintenance from reactive to proactive.
(5) Architectures Driving Change
✔️ Fully Connected Neural Networks (FC-NN / MLP)
Every neuron connects to every neuron in the next layer. These general-purpose networks are powerful but sometimes inefficient - like brute-force FEA methods. They’re widely used in early design exploration, simulation surrogates, and optimization workflows.
✔️ Convolutional Neural Networks (CNNs) - The Eyes of Quality Control
CNNs are specialized for spatial data like images. They detect features in surfaces and structures - essential in automated surface defect detection, weld seam inspection, and real-time quality control during the manufacturing process.
✔️ Recurrent Neural Networks (RNN, LSTM, GRU) - Time-Series Masters
RNNs are built for sequences. They “remember” previous steps, making them ideal for vibration analysis, fatigue cycles, and other time-dependent behaviors. LSTMs and GRUs extend this to capture long-term dependencies, much like accounting for hysteresis in materials over time.
✔️ Transformers - Unlocking Engineering Knowledge
Transformers use attention mechanisms to focus on the most relevant parts of input - like prioritizing critical load paths in a structure.They excel at reading research papers, summarizing technical documents, and surfacing design guidelines from vast datasets. Tools like Leo AI extend this by connecting transformer capabilities to engineering-specific data - linking decades of tribal knowledge from CAD models, PLM records, and structured documentation.
✔️ Tokens
Tokens are the smallest units a language model processes - words, subwords, or punctuation. More tokens mean more detail - but also more compute.Example: “Bolted connection” → “Bolted”, “connection”, and the space between them are three tokens.
(6) Generative AI: Expanding What’s Possible
✔️ Generative Design - AI as a Creative Partner
You define performance goals, constraints, and materials - and AI explores thousands of design possibilities.The result? Lightweight, non-intuitive geometries (like lattice structures) that meet mechanical requirements and manufacturing constraints while optimizing for weight, strength, and cost.
✔️ Digital Twins - Bridging Real and Virtual Worlds
A digital twin is a continuously updated, data-driven virtual model of a physical system - a turbine, robot, or vehicle.It helps engineers simulate wear, predict failures, and address energy and biomedical challenges long before they happen - all while refining mechanical systems for optimized solutions.
(7) LLMs vs LMMs: Two Different Species of AI
✔️ Large Language Models (LLMs)
LLMs like GPT, Claude, and Gemini are trained on massive amounts of general-purpose text. They’re excellent at writing, summarizing, and explaining - but they have major limitations in engineering:
No understanding of geometry or assemblies
No access to PDM/PLM systems or internal standards
Potential data security risks depending on vendor policies
They’re like very well-read junior engineers: great for context and communication but not built for engineering decisions.
✔️ Large Mechanical Models (LMMs) - Built for Mechanical Engineers
LMMs, like the one behind Leo AI, are different. They’re designed for mechanical engineering professionals and product teams. They process structured engineering data and CAD-aware context - like part relationships, assembly constraints, and performance requirements.
They don’t replace CAD or FEA tools - but they transform workflows by reducing overhead:
Answering technical questions using vetted sources
Suggesting parts and materials based on intent
Understanding assembly context from exported models
Surfacing internal standards and supplier data securely
Security is built in: LMMs can run in private environments with no external data transfer - critical for regulated industries and proprietary projects.
(8) Other Architectures and Approaches
Autoencoders: Compress and reconstruct data - like reducing a CAD assembly to key parameters.
GANs: Competing models that generate realistic designs over time.
Diffusion Models: Generate data by “denoising” noise - like machining material away to reach a final shape.
(9) Practical Extras
Hyperparameters: Settings like learning rate and layers, similar to mesh density or tolerance choices.
Bias & Variance: Balancing under- and overfitting - like stiffness vs. flexibility in design.
Embeddings: Numerical representations of objects (words, parts, signals) enabling smarter search across part libraries and documentation.
Transfer Learning & Fine-Tuning: Reusing and adapting pre-trained models to new tasks, saving valuable time and compute.
(10) How AI Drives Transformation
AI is reshaping every layer of mechanical engineering:
Energy: Optimizing turbines, improving efficiency, and addressing energy sustainability challenges.
Autonomous Systems: Reinforcement learning is revolutionizing autonomous systems with safer navigation and smarter control.
Biomedical: Designing lighter, smarter prosthetics and devices.
Manufacturing Process: Enhancing quality control, predictive maintenance, and material usage.
Documentation: Surfacing insights from thousands of archived research papers and standards.
Integration: Connecting CAD, PLM, and engineering data into continuous, AI-powered workflows.
And most importantly, AI gives engineers the one thing we always need more of: time - to design, innovate, and build better systems.
(11) Bringing AI Into Your Workflow
Leo AI is a purpose-built AI for mechanical engineers. It doesn’t just generate text - it understands mechanical context, interprets exported models, surfaces linked documentation, and connects relevant engineering knowledge without needing native plug-ins.
What it helps with:
CAD-aware Q&A and engineering context retrieval
Part and material discovery with engineering constraints in mind
Drafting BOMs, reports, and specifications from project data
Onboarding and knowledge reuse - making best practices accessible
Security-first deployments with zero-retention setups
It doesn’t replace CAD or FEA - it enhances them. Leo handles the heavy lifting so you can focus on solving complex problems and pushing the boundaries of what’s possible.
Feature | General AI (ChatGPT, Gemini, Claude) | Leo AI |
Context understanding | No awareness of CAD, assemblies, or mechanical constraints | Deep knowledge of mechanical design, CAD, tolerances, and workflows |
Engineering calculations | Limited or requires manual checking | Built-in validation with Python and references |
CAD integration | None | CAD-aware and designed to work alongside engineering workflows |
Data security | Prompts may be used to train models | Sensitive information stays secure inside your organization |
Workflow support | Text generation only | Assists with part search, onboarding, documentation, and repetitive tasks |
Final Thoughts
The evolving landscape of mechanical engineering is defined by cutting-edge AI methodologies that extend what’s possible - from automating routine tasks to unlocking entirely new design possibilities.
Those who learn how to harness AI-powered tools today will lead the next era of engineering tomorrow.
👉 Ready to step into the future of engineering? Try Leo AI today
👉 Join the MI Community - a global hub where engineers explore new AI tools, share workflows, and shape the future of mechanical design together.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2025 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2025 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2025 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2025 Leo AI, Inc.