MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

Abstract

Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this paper presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.

Predicting of Process Parameters for Theoretical Concentrated Stress of Fatigue Notch Coefficient of Auto Parts Using Virtual Recognizable Performance Evaluation Research

“…In 2020, Miao, Y. et al [ 14 ] developed an automated technology that uses virtual reality (VR) equipment to measure eye deviation, where a VR display system in which a screen where the gaze target alternately changes between the on and off phases was used to simulate the normal strabismus diagnosis procedure. In 2020, Adnan, M. et al [ 15 ] implemented systems with fast and intuitive capabilities that adapted to different metal properties, machine configurations, and process parameters. Therefore, it is challenging to determine the threshold of the main control loop of the additive manufacturing (AM) process.…”