## 1 Introduction
These factors are Orientation Angle (ORA), Raster Deposition Angle (RDA), Nozzle Temperature (NT), Bed Temperature (BT), Infill Density (ID), Layer Thickness (LT), and Printing Speed (PS). PA6 was purchased in the form of pellets, and it was made into filament using the extrusion process. The samples were produced utilizing various 3D printing setting combinations. The 3D printed specimens were subjected to experimental testing to ascertain their mechanical properties under compression stress, in accordance with the international standard ASTM D695-02a for testing polymers under compressive loading. The stopwatch method was used to keep track of the amount of time. The energy required during the 3D printing process was monitored with the respective equipment. A detailed analysis of the fracture behavior and morphological characteristics was performed. An L27 orthogonal array was constructed using the Taguchi method to process the experimental data, and Quadratic Regression Modeling (QRM) was then used to develop equations for the predictions of the various response factors explored in the research. To our knowledge, no other study examines as many variables simultaneously for MEX 3D printed PA6 parts' energy usage and mechanical performance, especially in compressive loading. The modeling techniques employed demonstrated the requirement for such an analysis, as they revealed that the parameters under study have varying effects on the work's response characteristics. While the orientation angle and the infill density had a substantial impact on the components' compressive strength, on the other hand, printing speed and layer thickness had a considerable impact on energy usage. A cause-and-effect has been prepared showing the parameters affecting the compressive strength and the energy consumption and is presented in the Supplementary material of the study (Figure S1). The given prediction models have had their dependability confirmed and are suitable for immediate application in industrial applications. The authors specifically decided to examine and conduct testson compressive specimens despite their lengthy printing times, large volumes, and weight (as opposed to tensile specimens):
* Existing research lacks extensive data, particularly when it comes to multi-parametric tests. Due to the high cost of 3D printing, and the need for a more robust compression test apparatus, there is a shortage of such scientific findings. Given that compressive mechanical stress occurs frequently over the operational life of 3D-printed working components, the paucity of compression test results documentation is evident [82]. * The compressive samples' increased volume enabled the monitoring and documentation of their weight, printing time, and power consumption and resulted in measures that are accurate and trustworthy. The current study provides a thorough and in-depth assessment of the general process parameter optimization for Polyamide 6 (PA6), one of the most widely used polymers in 3D printing. With the help of the related predictive equations, the environmental, economic, and mechanical behavior outcomes of 3D-printed PA6 samples are thus at hand.