Optimization of hybrid friction stir welding of PMMA: 3D-printed parts and conventional sheets welding efficiency in single- and two-axis welding traces

Abstract

Herein, the feasibility of joining with the friction stir welding (FSW) process 3D-printed parts made of poly(methyl methacrylate) (PMMA) with extruded PMMA sheets is investigated. A full factorial design method is followed, with two control parameters, i.e., tool rotational and travel speed, and three levels each. The hybrid joints produced were subjected to tensile and flexural loading and the corresponding properties were optimized with statistical modeling tools. Regression analysis provided prediction models for the five output metrics. The temperature was monitored throughout the experimental process. Samples were inspected with optical and scanning electron microscopy and their morphological characteristics were correlated with the joining conditions. The optimized FSW parameters were used for joining PMMA 3D-printed parts with sheets with two-axis joining seams. The produced hybrid joints were more than sufficient in their mechanical properties. The highest welding efficiency achieved in the tensile tests was 1.36, by the sample welded with 900 rpm and 6 mm/min. The sample welded with the same conditions achieved also the highest welding efficiency in the flexural tests (0.98). The findings presented proven the efficiency of the hybrid PMMA joints studied and have direct industrial applications for efficient component production. Graphical Abstract

Operational Performance and Energy Efficiency of MEX 3D Printing with Polyamide 6 (PA6): Multi-Objective Optimization of Seven Control Settings Supported by L27 Robust Design

“…The difference between predicted and actual values for sB in confirmation run 29 is negligible. It should be noted that in the specific run (29), the model failed to predict the EPC value, and as a result, the error with the experimental value was not possible to be calculated (hence the "vague" values in Table 7). In this specific case, the model produced a negative EPC value, which is not physically possible.…”

“…Adaptive multi-layer customization [23], machine learning methods [24], and statistical modeling tools [25], made for examining and maximizing the influence of the 3D printing settings on the consumption of energy in methods of additive manufacturing [26], have all been used in this context. Modeling tools, such as Neural Networks [27], Analysis of Variances (ANOVA) [28], Taguchi design of experiments [29,30], Box Behnken design [31], have been applied for the analysis of experimental data in 3D printing, related to the effect of the 3D printing settings on the performance of the parts. Additionally, the economic viability and ecological impact of FFF, both have an extensive amount of room for improvement [32].…”