Complex forms may be easily created with additive manufacturing methods, but managing surface roughness remains a difficulty, even for flat surfaces, because surface quality is dependent on numerous parameters. This research investigates the effect of some printing factors on surface roughness in 3D printing methods. The purpose of this study is to quantify the most influential input printing factors on surface roughness in 3D printing processes. Polyacrylic acid thermoplastic was used to print workpieces, and mathematical models were generated using the regression method to analyze the relationship between process parameters and surface roughness. The exponential model fits the experimental data slightly better than the linear model. Only Ra-90 met all surface roughness classification requirements, while surface roughness measurements in the 0 and 45-degree directions did not meet the requirements and cannot be used to describe the surface roughness. The study highlights the importance of considering input printing parameters when optimizing surface roughness in 3D printing processes, providing valuable insights into the impact of process parameters on surface roughness.

This study employs Direct Metal Laser Sintering (DMLS) technology to investigate surface roughness in stainless steel 316L 3D printing processes. Utilizing the Taguchi method in experimental design, we examine the influence of independent variables—laser power, scan speed, and hatch spacing - on surface roughness quality. Results indicate that laser power has the greatest impact, followed by scan speed and hatch spacing. Notably, both laser power and hatch spacing positively affect surface roughness, while scan speed adversely affects the top surface quality of printed components. This research enhances comprehension of the intricate relationship between process parameters and surface quality in DMLS-based 3D printing, offering insights for optimizing surface roughness in stainless steel 316L applications. The study holds practical significance for enhancing the quality and performance of 3D-printed components across diverse engineering and manufacturing sectors.

This study aims to analyze the influence of specific printing parameters, including infilling, print speed, and layer height, on the CT numbers and printing time of 3D-printed workpieces fabricated from Polylactic Acid (PLA). The primary objective is to optimize these parameters to attain desired CT numbers and print time for radiotherapy applications. To achieve this objective, we employed the Taguchi experimental design and regression analysis methodologies. A series of experiments were conducted to systematically assess the effects of varying infilling, print speed, and layer height values on the CT numbers and printing time of the PLA workpieces. The resulting data were then used to create mathematical models for predicting optimal parameter settings. Our investigations revealed that specific adjustments to infilling and layer height significantly influence the CT numbers and printing time of 3D-printed workpieces. By leveraging the developed mathematical models, precise predictions can be made to optimize independent parameters for the desired CT numbers and printing times, enhancing the efficacy of 3D-printed workpieces for radiotherapy applications. This research contributes to the advancement of 3D-printed workpieces utilized in radiotherapy, offering a pathway to enhance the accuracy and efficiency of treatment delivery. The optimization of printing parameters outlined in this study provides a valuable tool for clinicians and researchers in the field, ultimately benefiting patients undergoing radiotherapy treatments.

This study aimed to investigate the effectiveness of additive manufacturing (AM)-based teaching methods compared to traditional methods for teaching the Archimedes experiment in science education. Results showed that the AM method was significantly more effective in enhancing students’ understanding of the Archimedes principle and their ability to apply it to real world scenarios. The customisable and hands on nature of 3D printing also led to higher levels of student engagement and enjoyment. However, limitations were identified in terms of the time, resources, and cost required to set up the 3D printing experiment. Despite these limitations, our study suggests that AM has the potential to revolutionise science education by providing students with a more engaging, hands on, and customisable learning experience.

This study aimed to investigate the effectiveness of additive manufacturing-based teaching methods compared to traditional methods for teaching the Archimedes experiment in science education. Results showed that the additive manufacturing method was significantly more effective in enhancing students\' understanding of the Archimedes principle and their ability to apply it to real-world scenarios. The customizable and hands-on nature of 3D printing also led to higher levels of student engagement and enjoyment. \nHowever, limitations were identified in terms of the time, resources, and cost re-quired to set up the 3D printing experiment. Despite these limitations, our study suggests that additive manufacturing has the potential to revolutionize science education by providing students with a more engaging, hands-on, and customizable learning experience.