Optimization of the Printing Parameters to Improve the Surface Roughness in Fused Deposition Modeling

A better surface finish is an essential requirement of any component in particular medical components. The recent development in additive manufacturing technology produces components with a good surface finish. However, the optimization of process parameters helps to achieve a better surface finish. This paper focuses on the optimization of printing parameters of the surface roughness of a flat object developed from an FDM printer. FDM (Fused Deposition Modeling) is a layer-by-layer deposition process to develop 3D objects. It uses solid-state material (Filament) to print the product by melting and depositing the material on the printing bed. Several factors in the FDM process can affect the product’s quality. The parameters such as printing temperature, bed temperature, printing speed, fill density, layer thickness, and air gap influence the quality of the printed products. This investigation has considered printing temperature, layer height, and printing as process parameters. In addition, the parameter affecting the printed object’s surface finish is determined using ANOVA optimization and S/N ratios. PLA (Polylactic Acid) is taken as study material which is one of the feedstocks used in polymer filament and finds its applications in implant printing and medical tools

Optimization of the Printing Parameters to Improve the Surface Roughness in Fused Deposition Modeling

A better surface finish is an essential requirement of any component in particular medical components. The recent development in additive manufacturing technology produces components with a good surface finish. However, the optimization of process parameters helps to achieve a better surface finish. This paper focuses on the optimization of printing parameters of the surface roughness of a flat object developed from an FDM printer. FDM (Fused Deposition Modeling) is a layer-by-layer deposition process to develop 3D objects. It uses solid-state material (Filament) to print the product by melting and depositing the material on the printing bed. Several factors in the FDM process can affect the product’s quality. The parameters such as printing temperature, bed temperature, printing speed, fill density, layer thickness, and air gap influence the quality of the printed products. This investigation has considered printing temperature, layer height, and printing as process parameters. In addition, the parameter affecting the printed object’s surface finish is determined using ANOVA optimization and S/N ratios. PLA (Polylactic Acid) is taken as study material which is one of the feedstocks used in polymer filament and finds its applications in implant printing and medical tools

Optimization of process parameters to minimize circularity error and surface roughness in fused deposition modelling (FDM) using Taguchi method for biomedical implant fabrication

Fused Deposition Modeling (FDM) has emerged as a pivotal additive manufacturing technology that enables the creation of complex geometries with high precision and repeatability, particularly in the fabrication of biomedical implants. The functional additive manufacturing components, such as custom prosthetics, dental implants, and surgical guides, are expected to have dimensional accuracy and superior surface finish to ensure biocompatibility, osseointegration, and optimal tissue-implant interaction. This study aimed to optimize the FDM process parameters to minimize circularity error and surface roughness (Ra) using the Taguchi method, with a focus on biomedical implant fabrication. The printing temperature, printing speed, and layer thickness were identified as significant factors affecting circularity and Ra. Orthogonal arrays of Taguchi L9 were devised with three levels of each factor. Circularity and Ra were measured on the printed specimens, which included biomedical implant prototypes. The signal-to-noise (S/N) ratio response analysis determined the optimal parameter combinations. Results showed that the most dominant factor for circularity error reduction was build orientation, while layer thickness had the highest influence on Ra. The confirmation runs validated that the Taguchi-optimized parameters reduced the circularity error by 42% and Ra by 67% compared to default settings. Therefore, the Taguchi method proved effective in identifying the ideal process combinations for biomedical implant fabrication. The findings can help FDM users in the biomedical sector minimize dimensional errors and improve surface finish quality, ultimately enhancing the performance and reliability of medical implants