تقرير
In situ process quality monitoring and defect detection for direct metal laser melting
| العنوان: | In situ process quality monitoring and defect detection for direct metal laser melting |
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| المؤلفون: | Felix, Sarah, Majumder, Saikat Ray, Mathews, H. Kirk, Lexa, Michael, Lipsa, Gabriel, Ping, Xiaohu, Roychowdhury, Subhrajit, Spears, Thomas |
| المصدر: | Sci Rep 12, 8503 (2022) |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Physics - Data Analysis, Statistics and Probability, Physics - Instrumentation and Detectors |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1038/s41598-022-12381-4 |
| URL الوصول: | http://arxiv.org/abs/2112.01921 |
| رقم الأكسشن: | edsarx.2112.01921 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1038/s41598-022-12381-4 |
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