top of page

How to advance composite manufacturing with Machine Learning, Computer Vision, and Digital Twin?

Updated: Apr 4, 2022

Table of Content

This article explores possible composite manufacturing advances using newfound technologies such as machine learning, computer vision, and digital twin.

The key idea here is to understand the benefits and roadblocks of each of the technologies from a composite manufacturing SME’s perspective, and provide some food for thought for future roadmaps!

The focal elements of composite manufacturing are engineering design, quality, and process optimization.

Design and Structural Simulation - Combining knowledge from composite design engineers and manufacturers at no cost

What if we can input the constraints, the loads, and manufacturing technique as input, and let the Machine Learning BlackBox design the part as a structural designer?

The entire design running through numerous neurons in a few seconds produces a design with the best manufacturability and structure without input from human structural designers.

The ML experiments in the area of structural simulation are accurately trying to achieve this by creating a self-learning finite element method and engineering applications.

Flow chart of EPR-based self-learning FEM

80–90% of engineering schools do not provide composite design courses, let alone the new advancements in the analysis.

ML tool has the possibility to learn from the knowledge of existing engineers and FEA software to generalize the learning and create a tool that can be used by SMEs to exploit the benefit of advanced composites design at a very low cost.

This would solve the present scenarios for SME manufacturers:

  • Can not hire or find engineers with relevant design engineering know-how.

  • Available FEA software capable of handling composites is too expensive, as they are built on the legacy of traditional FEA or provide very partial composite simulation capability.