In situ process quality monitoring and defect detection for direct metal laser melting

Autor:	Felix, Sarah, Majumder, Saikat Ray, Mathews, H. Kirk, Lexa, Michael, Lipsa, Gabriel, Ping, Xiaohu, Roychowdhury, Subhrajit, Spears, Thomas
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Physics - Data Analysis Statistics and Probability Physics - Instrumentation and Detectors
Zdroj:	Sci Rep 12, 8503 (2022)
Druh dokumentu:	Working Paper
DOI:	10.1038/s41598-022-12381-4
Popis:	Quality control and quality assurance are challenges in Direct Metal Laser Melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that can be readily deployed on existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects. Comment: 16 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.01921 Zobrazit plný text záznamu View this record from Arxiv