Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

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.

A Review on Material Extrusion Additive Manufacturing of Metal and How It Compares with Metal Injection Moulding

“…Zhang et al [101], therefore, proposed the predictive model of the meal MEX dimensional change using machine learning techniques. It is found that neural network algorithms provide the highest accuracy, which can successfully predict dimensional variations and optimise the printing and sintering process parameters of the metal MEX parts [101]. [8,36,44,49,64,127,158].…”

Machine Learning-Based Void Percentage Analysis of Components Fabricated with the Low-Cost Metal Material Extrusion Process

“…Additive manufacturing (AM), also known as 3D printing (3DP) [1], is a set of technologies that produce objects layer by layer from the computer-aided design (CAD) model [2]. At the present stage, there are seven different AM categories, which are material extrusion (ME), vat photopolymerization (VAT), powder bed fusion (PBF), direct energy deposition (DED), sheet lamination (SL), material jetting (MJ), and binder jetting (BJ) [3].…”

“…At the present stage, there are seven different AM categories, which are material extrusion (ME), vat photopolymerization (VAT), powder bed fusion (PBF), direct energy deposition (DED), sheet lamination (SL), material jetting (MJ), and binder jetting (BJ) [3]. Among these techniques, ME is well known and the most widely used [1] because of its numerous advantages such as producing complex parts with less materials and time [4], low-cost [4], environmental friendliness [5], etc. The ME technique is popular in several research areas, such as the food industry [6], medicine [7], aerospace [8], and so on.…”

“…In recent years, ME has been used in metal object manufacturing [9]. However, ME is not widely used in metal 3DP because the working temperature of ME 3D printers is much lower than the melting temperature of most metals [1]. Thus, low-cost metal material extrusion (LCMME) is developed to use metal-infused polymer filaments and fabricate metal components with a low-cost manufacturing process [10].…”

“…Machine learning (ML) is a subset of artificial intelligence and it is widely used to predict the mechanical properties of fabricated parts [18]. ML is the process to use computer algorithm, regression, or classification models to make predictions on the data without using any exact methods [1]. It has the ability to self-train from the given data and makes the decision by itself [19].…”