MPS-GAN: A multi-conditional generative adversarial network for simulating input parameters' impact on manufacturing processes

Abstract

Identifying the right combination of process parameters is crucial to ensure a high quality of the manufactured products. Nevertheless, this task is not always straightforward, as it usually requires a lot of experimental trials and a deep understanding of the physical laws governing the process. This study presents an efficient way of dealing with this problem using a generative adversarial network (GAN) model. The proposed Multi-Parameter Simulation GAN (MPS-GAN) model can synthesize thermal and X-ray computed tomography (XCT) images conditioned on different combinations of build parameters. The study also proposes a model variant, named MPS-GAN-IR, that uses the content loss to generate large images with improved perceptual quality and resolution. The performance of the MPS-GAN and MPS-GAN-IR was tested on real datasets taken from two different manufacturing processes, mainly resistance spot welding and additive manufacturing. The image-generation capability of both models was also evaluated for various combinations of build parameters for each process. The “quality measure” for each process was considered to provide a quantitative evaluation of the models' performance. The visual and numerical results indicate that the MPS-GAN and MPS-GAN-IR models could be a viable alternative to experimental tests and physics-based simulations.