Model-Based Systems Engineering : Mechanical Product Design
Published 8/2025
Created by Aditya Bharat Patil
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 59m ) | Size: 613 MB
Model-Based Systems Engineering (MBSE) for Mechanical Product Design: From Requirements to Digital Twin

What you'll learn
Understand the core principles of Model-Based Systems Engineering (MBSE) in a mechanical engineering context.
Apply MBSE to integrate CAD, simulation, and system-level requirements into a unified engineering model.
Establish secure, compliant, and data-driven feedback loops for continuous mechanical product improvement.
Integrate MBSE with Industry 4.0 workflows, IoT data streams, and predictive analytics.
Requirements
Bachelor's degree (or equivalent knowledge) in mechanical engineering, mechatronics, or a related field.
Familiarity with CAD tools and basic engineering simulations (FEA/CFD).
Understanding of engineering design processes and requirement management.
No prior MBSE experience is required as concepts are introduced progressively from fundamentals to advanced applications
Description
This postgraduate-level online course provides a comprehensive, industry-ready introduction to Model-Based Systems Engineering (MBSE) specifically for mechanical product design, simulation integration, and lifecycle management. You will learn how to transition from document-heavy, disconnected processes to integrated, model-centric engineering workflows. MBSE serves as a single source of truth that unifies CAD design, FEA/CFD simulation data, system requirements, and verification activities. This shift enables faster design iterations, higher product quality, and improved traceability across engineering teams.Key skills and outcomes include:Simulation-driven MBSE - integrating Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and Multibody Dynamics (MBD) directly into system models for continuous verification.Digital twin development - creating living, evolving models connected to real-time IoT sensor data for predictive maintenance and operational optimization.Industry 4.0 integration - connecting MBSE to smart factories, cloud collaboration, and interoperability standards for seamless manufacturing alignment.AI-augmented MBSE & generative design - leveraging machine learning and optimization algorithms to accelerate innovation and reduce time-to-market.Autonomous mechanical systems design - embedding control logic, sensor fusion, and safety constraints directly into the engineering model.Throughout the course, you will gain hands-on conceptual knowledge of how MBSE integrates with digital engineering tools to create robust, high-performance mechanical products. By connecting design, simulation, manufacturing, and operational data into a single digital ecosystem, you will master data-driven mechanical engineering at a level required by Industry 4.0 and beyond.By the end of this course, you will be able to:Apply MBSE methods to complex mechanical systems from concept to deployment.Build and maintain digital twins throughout the product lifecycle.Use real-time IoT feedback to drive continuous design improvement.Employ AI-assisted design and generative algorithms to create innovative solutions rapidly.This course is designed for mechanical engineers, systems engineers, product designers, and engineering managers aiming to upgrade their skills for the next generation of model-driven engineering. Whether you're working in aerospace, automotive, robotics, or industrial equipment, the principles and workflows you'll learn here will future-proof your engineering capabilities.
Who this course is for
Mechanical engineers seeking to upgrade their skills to Industry 4.0 standards.
Systems engineers integrating mechanical domains into multi-disciplinary MBSE frameworks.
R&D engineers and product designers aiming to adopt model-based approaches for faster and higher-quality product development.
Engineering managers who want to implement digital workflows for product lifecycle efficiency.
Postgraduate students and researchers exploring advanced mechanical system design and simulation integration.