Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques
| Autor: | Ismail Fidan, Muhammad Ismail, James Femi-Oyetoro, Zhicheng Zhang, Michael Allen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Fabrication low-cost metal material extrusion Mean squared error industrial_manufacturing_engineering Computer science media_common.quotation_subject 3D printing Sintering 02 engineering and technology Machine learning computer.software_genre 020901 industrial engineering & automation General Materials Science Quality (business) media_common sintering Mining engineering. Metallurgy Artificial neural network business.industry dimensional accuracy TN1-997 Metals and Alloys Process (computing) 021001 nanoscience & nanotechnology machine learning Extrusion Artificial intelligence 0210 nano-technology business computer additive manufacturing |
| Zdroj: | Metals Volume 11 Issue 5 Metals, Vol 11, Iss 690, p 690 (2021) |
| ISSN: | 2075-4701 |
| DOI: | 10.3390/met11050690 |
| Popis: | Additive manufacturing (AM) is a layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME-made components after 3D printing and sintering process. In this research, the ML algorithms present a significantly high coefficient of determination (i.e., 0.999) and a very low mean square error (i.e., 0.0000878). The prediction outcomes using a neural network approach have the smallest mean square error among all ML algorithms and it has quite small p-values. So, in this research, the neural network algorithm has the highest accuracy. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process. |
| Databáze: | OpenAIRE |
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