ON THE REQUIREMENTS FOR MODEL BASED THERMAL CONTROL OF MELT POOL GEOMETRY IN LASER POWDER BED FUSION ADDITIVE MANUFACTURING
As additive manufacturing processes mature, process control is increasingly sought after as a means for improved quality. Accurate and responsive control of melt pool geometry in laser powder bed fusion processes is expected to improve the final part microstructure, prevent overand undermelting defects, improve surface finish and mechanical properties, and reduce residual stress. Various approaches for process control of melt pool geometry have been proposed but there are still significant barriers for their adoption. In this paper, we look into the elements of such process controllers and identify their requirements in terms of range of operation and bandwidth; and discuss the feasibility of such control systems. Introduction Laser powder bed fusion (L PBF) additive manufacturing (AM) technologies are used to fabricate solid objects from three dimensional (3D) model data by fusing selected regions of a powder bed with a laser and repeating the process for every layer [1] L PBF is considered one of the most promising AM technologies due to its superior surface and geometric quality, and its capability to work with a wide spectrum of materials. Metallic parts produced with L PBF AM have been found to be almost fully dense and to reach mechanical properties that sometimes surpass those of components produced with conventional methods [2] In spite of all the attention drawn, these processes are still not considered mature. Currently, the inability to guarantee material properties is holding back their adoption as there is no confidence that manufactured parts will meet specific structural needs. Significant effort has been dedicated to the search of predetermined optimal processing conditions to result in desired mechanical properties for a given part. However, this approach is not economical nor robust enough to deal with perturbations. Process control has been identified as an important area of research in AM and had been considered critical "to achieve predictable and repeatable operations" [3] Vlasea et al. identified four approaches for monitoring and control in AM: pre process optimization, in situ defect detection, feedback control, and signature derived control [4] While the first two approaches focus on optimization and monitoring, the last two rely on feedback to identify and mitigate process Official contribution of the National Institute of Standards and Technology (NIST) not subject to copyright in the United States. The full descriptions of the procedures used in this paper may require the identification of certain commercial products. The inclusion of such information should in no way be construed as indicating that such products are endorsed by NIST or are recommended by NIST or that they are necessarily the best materials, instruments, software or suppliers for described purposes. 133 Contributed Papers from Materials Science and Technology 2016 (MS&T16) October 23 27, 2016, Salt Palace Convention Center, Salt Lake City, Utah USA Copyright (c) 2016 MS&T16(r)