Optimisation of tensile stress of poly(lactic acid) 3D printed materials using response surface methodology

Abstract

Three-dimensional printed plastic products developed through fused deposition modelling (FDM) take long-term mechanical loading in most industrial prototypes. This article focuses on the impact of the 3D printing parameters, type of thermal treatment and variation of characteristic dimensions of standard specimens on the tensile properties of poly(lactic acid) (PLA) material. Two mediums were used for thermal treatment: NaCl powder and plaster. The specimens immersed in NaCl powder were heated to the melting temperature of the filament (200 °C), while the processing of the plastered specimens was performed at a temperature of 100 °C. After treatment, the specimens were cooled at room temperature (25 °C), and the dimensions of the annealed and untreated specimens were controlled. The tensile test of the specimens was performed on the universal test machine. The response surface methodology (RSM) is employed to predict the tensile stress by undertaking input parameters. The analysis of variance (ANOVA) results revealed that the untreated specimens, orientation –45/45 and layer thickness of 0.1 mm had the highest tensile stress value. Thermal treatment in plaster showed a significant increase in tensile strength, while the best specimens were obtained after treatment in NaCl, and all refer to the –45/45 (0.1 mm) orientation.

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“…Its application provides a practical and efficient way of varying the influential or input factors at different levels, contributing to an objective understanding of the researched system/ process with help of statistics. The experimental design methodology is a systematic way to plan, perform and interpret the results of experiments, to obtain as much information as possible, while performing as few experiments as possible [22].…”

Analysis of the influence of FDM parameters in the tensile strength response using machine learning

“…Tensile stress has been extensively studied by various authors, and is one of the most important properties of parts manufactured by 3D printing, whose application must withstand this type of stress to a great extent. These studies have been carried out using different statistical techniques such as factorial design [4,5,6,7,8,9], response surface methodology [10,11,12,13,14], Taguchi [15,16,17,18,19] and machine learning algorithms [20] [21].…”

Exploring the impact of 3D printing parameters on mechanical properties of ABS and PLA materials through machine learning analysis

“…These studies have embraced diverse statistical methodologies, including factorial design, as evidenced in works such as [4,5,6,7,8,9]. Additionally, the utilization of response surface methodology is apparent in studies such as [10,11,12,13,14], while the Taguchi approach has been implemented in investigations like [15,16,17,18,19]. Furthermore, the application of machine learning algorithms has also come to the fore, as evident from references like [20] and [21].…”