Title: Parametric analysis of wear behaviour on fused deposition modelling build parts

Authors: Swayam Bikash Mishra; Rashmi Pattnaik; Siba Sankar Mahapatra

Addresses: Department of Mechanical Engineering, National Institute of Technology, Rourkela 769008, India ' Department of Mechanical Engineering, Shivajirao S. Jondhale College of Engineering, Mumbai 421204, India ' Department of Mechanical Engineering, National Institute of Technology, Rourkela 769008, India

Abstract: Fused deposition modelling (FDM) is one of the proficient technologies among all rapid prototyping (RP) processes due to its capability to build durable end-use parts with reasonable mechanical strength. FDM process has the ability to develop 3D complex geometry accurately with less time and material waste as compared to other RP processes. However, mechanical wear unfavourably affects the durability and lifespan of the FDM build part when used as an end-use part. It has been observed that few important FDM process parameters significantly determine the mechanical strength, wear resistance and surface roughness of build parts. Since wear is an important phenomenon influencing functionality of a part, effect of six FDM build parameters viz. contour number, layer thickness, raster width, part orientation, raster angle and air gap on sliding wear of the specimen is experimentally investigated in this research work. Using analysis of variance (ANOVA), effect of each process parameter on wear of the build specimen is analysed. From the scanning electron microscope (SEM) images, wear surfaces and internal structures of the specimens are evaluated. Finally, a model based on least square support vector machine (LSSVM) technique is proposed to predict the wear performance of the FDM build parts.

Keywords: additive manufacturing; fused depositing modelling; rapid prototyping; analysis of variance; ANOVA; response surface methodology; RSM; least square support vector machine.

DOI: 10.1504/IJPQM.2017.084461

International Journal of Productivity and Quality Management, 2017 Vol.21 No.3, pp.375 - 391

Available online: 22 May 2017 *

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