Special Issue: Machine Learning and Representation Issues in CAD/CAM

Abstract

Machine learning (ML), a sub-field of artificial intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it also introduces new questions. While the potential of ML is undeniable, integrating existing ML methods into computer-aided design and manufacturing (CAD/CAM) presents distinctive challenges. These challenges encompass representation, adaptation, and the development of novel ML techniques to enhance CAD/CAM systems for diverse design and manufacturing solutions.This special issue aims to explore and resolve issues related to effective engineering model representation for ML, the integration of neural networks and deep generative models into CAD/CAM, mathematical frameworks combining ML with CAD/CAM in geometry and topology, data interpretation, and physics-based learning. The issue features ten papers that delve into various topics, including material prediction for assemblies, robotics mechanism design, multi-modal ML in engineering design, data-driven component segmentation in engineering drawings, evaluating assembly-part semantic knowledge in language models, three-dimensional slice reconstruction from high-resolution 2D images, physics-informed neural networks to expedite thermal simulations in additive manufacturing, transfer learning for defect detection in steel strips, AI-aided hull form design for energy-efficient unmanned underwater vehicles, and real-time high-precision calibration of quadruped robots using machine vision and artificial neural networks. Below, we provide concise summaries of each of the ten papers published in this special issue.The representation of mechanisms and their attributes for ML applications within engineering has hitherto remained unexplored. In their paper entitled “An Invariant Representation of Coupler Curves Using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms,” Nurizada and Purwar address this void by investigating the representation and synthesis of coupler curves in planar mechanisms using a deep neural network. They emphasize that the effective representation of coupler curves in neural networks remains an underexplored domain. The study conducts a comparative analysis of different representations of four-bar coupler curves and illustrates the capacity of a variational autoencoder (VAE) to provide a unified representation for coupler curves, irrespective of the initial input representation. Furthermore, the paper introduces an innovative approach that combines VAE with a fully connected neural network to generate dimensional parameters for four-bar linkage mechanisms, offering potential for automated mechanism design in robotics and machinery.In a similar vein, a distinct challenge persists in ML-based representation when predicting material properties for assembly bodies. In the paper titled “HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design,” authored by Bian et al., the authors introduce HG-CAD, a machine learning architecture utilizing a hierarchical graph representation for automated material prediction in assembly bodies. This prediction is based on recommendations derived from previous designs. The study underscores that a learning approach that combines geometry with topology through graphs to capture neighborhood context provides enhanced predictions compared to methods relying solely on geometric attributes.Additionally, Song et al. present a comprehensive review paper titled “Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions,” discussing the integration of multi-modal machine learning (MMML) into engineering design. This involves the amalgamation of various data modalities, such as text, 2D images, and 3D shapes, along with their associated processing methods. The review covers a wide spectrum of aspects, including methodologies for ha