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 letter 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.
Highlights
C OMPARED with traditional manufacturing, additive manufacturing (AM) offers the potential for developing complex and customized products that are prohibitively expensive to produce with current manufacturing methods
Based on the melt-pool images (MPIs) data, this research focuses on a laser powder bed fusion (LPBF) process to tackle the following querrstHCioaonnws:thtoe automatically classify the MPI types? MPI classification be used to determine the proper thresholds for compensating the smoke-discharging and r powder-coating control loops? Can the MPI classification be applied to evaluate the performance of polishing parameters for compensating printing quality?
The MPI classifier is considered as System 2 of the LPBF machine, whose outcomes will be used to determine the values of the thresholds or parameters of System 1
Summary
C OMPARED with traditional manufacturing, additive manufacturing (AM) offers the potential for developing complex and customized products that are prohibitively expensive to produce with current manufacturing methods. AM is more favorable towards low-volume production, repair and direct manufacturing of high value-added products [1] It takes more time for the metal AM to produce a component. Using multiple sensor-based systems to monitor the manufacturing process is essential for better part quality, in addition, the high-speed sensors can assist the feedback controller in the desired system. The laser power, scan speed, and shape and temperature of melt pools the common measurement items for monitoring and controlling in the AM process. Because the MPI shapes are parts of the significant features used to judge the stability and quality of the AM process, the camera-based in-situ systems are developed to monitor the melt pool and regulate the laser power online and in real time for the stability of the manufacturing process once the system detects some abnormal behaviors [6]. Inspired by the ways of human thinking [9], this work considers the primary control function as System 1, and the secondary tuning function as System 2; so that System 1 can be fast and intuitive for online control, while System 2 is slow but analytic for the offline tuning of System 1
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