Name: Applications of Artificial Intelligence in Mechanical Engineering
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Applications of Artificial Intelligence in Mechanical Engineering

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20 hrs

English

3283 views

Kapil Singh

Manufacturing & Industrial

Artificial Intelligence

7-day money-back guarantee
Session recordings included
Certificate of completion

Why enroll

As AI continues to evolve and become increasingly prevalent in engineering industries, learning AI ensures that mechanical engineers remain relevant and adaptable in a rapidly changing technological landscape. Companies value engineers who can embrace AI technologies into their work, making them more attractive candidates for positions. This course will equip mechanical engineers with valuable skills and tools related to AI that can lead to improved problem-solving, increased efficiency, and career advancement. Mechanical engineers with AI expertise can collaborate effectively with professionals from diverse backgrounds, fostering innovation and cross-functional problem-solving. In short, this course will position participants not only to excel in their field and but also contribute to the development of innovative and sustainable solutions in mechanical engineering using AI.

Your instructor

Kapil Singh

Chief Evangelist

20+ yrs experience·India

16Courses

52Learners

4Avg rating

Is this course for you?

You should take this if

You work in Manufacturing & Industrial
You're a Artificial Intelligence / Mechanical Engineering professional
You want to build skills in Artificial Intelligent
You prefer live, instructor-led training with Q&A

You should skip if

You need a different specialisation outside Artificial Intelligence
You need fully self-paced, on-demand content

Course details

Artificial Intelligence (AI) is transforming mechanical engineering by enabling smarter design, efficient manufacturing, predictive maintenance, and advanced automation. It helps engineers analyze large datasets, optimize systems, and make data-driven decisions.

AI refers to the use of technologies like machine learning, deep learning, and data analytics to simulate human intelligence in engineering tasks such as design, analysis, and operations.

This 20-hour course provides a comprehensive overview of AI applications in the field of mechanical engineering. It combines theoretical knowledge with practical applications and encourages hands-on learning through the capstone project, enabling students to gain a strong understanding of AI's role in solving real-world mechanical engineering challenges.

Course suitable for

Key topics covered

Introduction to AI and Its Relevance in Mechanical Engineering

Understanding AI and its subsets
Historical context of AI in mechanical engineering
Current trends and future prospects
Ethical considerations around AI in mechanical engineering

Machine Learning Fundamentals for Mechanical Engineers

Introduction to machine learning
Supervised, unsupervised, and reinforcement learning
Data preprocessing and feature engineering
Model selection and evaluation
Case studies in mechanical engineering applications

Use Cases of AI in Mechanical Engineering

AI-Driven Design and Simulation
Robotics and Automation in Manufacturing
Predictive Maintenance and Mechanical Fidelity
AI in Materials Science and 3D Printing
AI-Enabled Supply Chain and Inventory Management
Using AI for Quality Control

Capstone Project

Opportunities that await you!

Skills & tools you'll gain

Artificial Intelligent

Career opportunities

Training details

This is a live course that has a scheduled start date.

Why people choose EveryEng

Industry-aligned courses, expert training, hands-on learning, recognized certifications, and job opportunities-all in a flexible and supportive environment.

COMPLETED

Questions and Answers

Q: You're sizing an AI-based visual inspection station and you google "AI camera selection for surface defect detection on machined parts"; duty is oil-wet steel shafts, 0.2 mm defect minimum, 1 m/s line speed, fixed lighting envelope—what configuration actually fits?

A: A. This setup biases the CNN toward lighting artifacts and spikes escape rate during DFMEA severity scoring. B. Thermal contrast decays too fast and shifts the model input distribution between FAT and SOP. C. Depth noise floor exceeds the defect amplitude so the network never sees the failure mode. D. Line-scan plus strobed coaxial light locks pixels to surface reflectivity and keeps the AI within its trained envelope.

Q: During predictive maintenance rollout you search "how to calculate vibration RMS threshold for machine learning model"; accelerometer spec is ±50 g, bandwidth 1 kHz, baseline RMS is 2.5 m/s² and DFMEA calls for 3× margin—what threshold do you feed the model?

A: A. Treating g and m/s² as interchangeable shifts thresholds and breaks cross-asset comparability. B. Peak-based limits miss early-stage defects that only raise RMS slightly. C. Zero margin violates the DFMEA occurrence assumption and guarantees nuisance trips later. D. Applying the margin on the RMS baseline gives a defensible numeric gate for the ML pipeline.

Q: You're asked to sanity-check cloud costs and type "estimate data volume for AI predictive maintenance vibration sensors"; 120 machines, each with 3 axes, 5 kHz sampling, 16-bit, 8 hours/day—what's the daily data order?

A: A. Dropping sample rate by a factor of five skews the estimate below reality. B. A bits-versus-bytes slip explodes the number and triggers a false cost crisis. C. Counting compression that hasn't cleared MOC hides a real integration risk. D. Multiplying channels, rate, word size, and time lands you in the few-hundred‑gigabyte range.

Q: In a GA you search "AI system block diagram sensor symbol meaning" and see a dashed box labeled "ML Inference" tied to a safety PLC—what does the dashed boundary actually signal?

A: A. Assuming certification here would be a straight audit finding. B. Treating it as provisional lets hazards leak past gate reviews. C. Cloud-only interpretation conflicts with the physical interfaces shown. D. Dashed boundaries flag non-safety elements that cannot be credited in risk reduction.

Applications of Artificial Intelligence in Mechanical Engineering

Applications of Artificial Intelligence in Mechanical Engineering

Why enroll