Session: CIE-24-02 - AMS-CAPPD-SEIKEM: Artificial Intelligence and Machine Learning in Design and Manufacturing

Paper Number: 91115

91115 - Learning the Part Shape and Part Quality Generation Capabilities of Machining and Finishing Processes Using a Neural Network Model 

The future of manufacturing is rapidly shifting from mass production to decentralized production systems, which enable mass customization of highly individualized products. Considering this trend, recent developments in digital design and manufacturing are enabling cyber manufacturing services, which allow users to access on-demand, geographically distributed manufacturing services through the internet. Automatically acquiring a scalable and easy-to-update knowledge of manufacturing process capabilities from existing data is essential for enabling automated process selection, which is key for cyber manufacturing. In this work, we present a neural network model to learn the capabilities of discrete manufacturing processes such as machining and finishing from existing design and manufacturing data. Concatenating a 3D Convolutional Neural Network (3D CNN) with an artificial neural network, the combined model can learn the part shape and part quality generation capabilities of the manufacturing processes. Specifically, the proposed method takes the voxelized part geometry and part quality information as inputs and utilizes a mixed neural network model (3D CNN + artificial neural network) to predict the manufacturing process label as output. The manufacturing process capability knowledge embedded in the neural network model is scalable and updatable as new manufacturing data becomes available. We present an example implementation of the proposed method with a synthesized manufacturing dataset to illustrate how the method enables automatic manufacturing process selection. The high prediction accuracy shows its predictive strength for use in Computer Aided Process Planning (CAPP).

Presenting Author: Changxuan Zhao Georgia Tech

Presenting Author Biography: Changxuan Zhao is a Ph.D. Candidate in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He is a member of the Precision Machining Research Consortium at the Georgia Tech Manufacturing Institute where his Ph.D. work focuses on developing data-driven methods for learning the manufacturing process capabilities and utilizing the capabilities in manufacturing process selection and sequencing. Prior to his Ph.D. study, Changxuan was a master's student at Georgia Institute of Technology where he developed a real-time measurement system for the stereolithography process. He received his undergraduate degree at Georgia Tech in Mechanical Engineering. He was the 1st Place Winner of the 2020 ASME-CIE Hackathon.

Authors:

Changxuan Zhao Georgia Tech
Shreyes Melkote Georgia Institute of Technology