TY - JOUR
T1 - Classification and Selection of Sheet Forming Processes with Machine Learning
AU - Hamouche, Elia
AU - Loukaides, Evripides
PY - 2018
Y1 - 2018
N2 - Sheet metal forming is a critical component of modern manufacturing. The procedure for selecting a suitable manufacturing process to achieve the final geometry of a metal part is unstructured and heavily reliant on human expertise. Similarly, classification and design of new metal forming processes has yet to be automated. In this study, a Machine Learning approach was used for the first time to identify the manufacturing process that formed a part solely from the final geometry. Several Neural Network configurations were tested with different geometry representation methods. The best performing classifier employed a deep Convolutional Neural Network and achieved an accuracy of 89%, namely when the geometry was given through a mapping of the Mean and Gaussian curvatures. The high accuracy rate establishes that automated methods can perform this step between design and manufacture, thus eliminating the need for human experts in matching each product to a suitable forming method.
AB - Sheet metal forming is a critical component of modern manufacturing. The procedure for selecting a suitable manufacturing process to achieve the final geometry of a metal part is unstructured and heavily reliant on human expertise. Similarly, classification and design of new metal forming processes has yet to be automated. In this study, a Machine Learning approach was used for the first time to identify the manufacturing process that formed a part solely from the final geometry. Several Neural Network configurations were tested with different geometry representation methods. The best performing classifier employed a deep Convolutional Neural Network and achieved an accuracy of 89%, namely when the geometry was given through a mapping of the Mean and Gaussian curvatures. The high accuracy rate establishes that automated methods can perform this step between design and manufacture, thus eliminating the need for human experts in matching each product to a suitable forming method.
UR - http://www.scopus.com/inward/record.url?scp=85041201885&partnerID=8YFLogxK
U2 - 10.1080/0951192X.2018.1429668
DO - 10.1080/0951192X.2018.1429668
M3 - Article
VL - 31
SP - 921
EP - 932
JO - International Journal of Robotics and Computer Integrated Manufacturing
JF - International Journal of Robotics and Computer Integrated Manufacturing
SN - 0951-192X
IS - 9
ER -