On multi response optimization and process capability analysis for surface properties of 3D printed functional prototypes of PVC reinforced with PP and HAp. (2020)

• Record Type: Journal Article
• Authors: Sharma, Ravinder; Singh, Rupinder; Batish, Ajay
• Is Part Of: Materials today. Volume 28:Part 2(2020)
• Page Start: 1115
• Page End: 1122
• Publication Date: 2020
• Subjects: Multi response optimization -- PVC -- PP -- HAp -- Functional prototypes; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.01.092
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This paper outlines the multi response optimization and process capability analysis for surface properties of 3D printed functional prototypes of poly vinyl chloride (PVC) reinforced with poly propylene (PP) and hydroxyapatite (HAp) for possible bio-sensing applications. The process started with mechanical extrusion of filament wire with fix proportion of HAp in biocompatible polymers (PP and PVC) by using twin screw extrusion (TSE) process followed by 3D printing of functional prototypes (of standard tensile specimen as per ASTM D 638) on commercial fused deposition modelling (FDM) setup. The analysis of variance (ANOVA) has been used to study the effect of three controllable parameters of the FDM (i.e. infill percentage, layer thickness, and speed of extrusion head) on the surface roughness (Ra), hardness (H), and dimensional accuracy (ΔD) of 3D printed functional prototypes. The results of multi-response optimization suggests that parts printed at 100% infill density, 0.25 mm layer thickness and 33 mm/sec have better H, controlled Ra and ΔD. Further process capability indices (Cp and Cpk ) were calculated to ensure the statistical nature of the process. The results of study suggest that for selected responses, Cp and Cpk values are ≥1, hence the 3D printed functional prototypes with in-house prepared filaments of PVC-PP-HAp meets the requirements of batch/mass production. Finally X bar and R bar charts were prepared to understand the statistical nature of the process.