Additive manufacturing processes are being increasingly explored by researchers around the world for a variety of medical applications, such as patient-specific models, implants, prosthetics, orthotics, drug delivery devices and tissue engineering scaffolds. The objective of this study is to obtain patient-specific models and implants from computed tomography (CT) scan data and validate the strength of implant using finite element analysis. For this purpose, CT scan data of two patients were obtained in digital imaging and communication in medicine (DICOM) file format. DICOM files were converted into computer-aided design models using open source image processing software DeVIDE and saved in stereolithography (STL) format. The STL files were cleaned and corrected in Materialise's Magics RP software. These models were loaded into 3D systems' Geomagic Freeform software to design the customized implants. Finite element analysis was performed to check the strength of cranium implant. Maximum von Mises stress and deformation were found well below the allowable limit of the material. Finally, physical models of cranium, pelvic bone and implant prototypes, namely cranial, ilium, pubic symphysis and ischium were manufactured in polyamide PA2200 on a selective laser sintering machine. A simulation-based surface roughness evaluation was also performed to assess the range of surface roughness values (R a) of various implant prototypes. The R a values for implants were observed between 14.4 and 34.67 µm.

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The optimization of machining parameters is critical to the quality of machined products and the production rate. This paper aims to optimize the surface roughness of aluminium-2014 alloy by adjusting the machining parameters of computer numerical control (CNC) turning, including, cutting speed, depth of cut and feed rate. According to L9 orthogonal array, a total of nine experiments were conducted according to Taguchi method with different parameter settings. The surface roughness of the machined products was measured by a roughness tester, and evaluated by signal-to-noise ratio (SNR). The analysis of variance (ANOVA) was conducted to find the optimal parameter settings for surface roughness. The results show that the cutting speed is the most influential parameter (67.28 %) on surface roughness, followed by feed rate (32.28 %) and depth of cut (0.33 %) for surface roughness. Hence, the surface roughness can be optimized by minimizing the feed rate and depth of cut.