Abstract
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.
Highlights
Additive manufacturing (AM), known as 3D printing (3DP) [1], is a set of technologies that are used to produce objects layer-by-layer from computer-aided design (CAD) models [2]
For the length and the width, non-sintered dimensions are larger than the CAD dimensions, which means that the real parts will expand in length and width than the 3D models after the printing process
The medians of all three errors are close to zero and most absolute values of maximum and minimum errors are less than 0.8 mm, which means that most predictions done by linear regression with interactions (LRI) are accurate and the variations of LRI predictions are smaller than linear regression (LR) predictions
Summary
Additive manufacturing (AM), known as 3D printing (3DP) [1], is a set of technologies that are used to produce objects layer-by-layer from computer-aided design (CAD) models [2]. There are various types of AM processes including material extrusion (ME), selective laser sintering (SLS), selective laser melting (SLM), powder bed fusion (PBF), and stereolithography (STL) [3]. Among these techniques, ME is well-known and the most widely used process [4]. ME has been used in the manufacturing of metal components [10]. New metal-infused polymer filaments have been developed as a feedstock material for ME process and can be used to fabricate metal components using this new, low-cost manufacturing processes [11]
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