Abstract
Additive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower environmental degradation, and portable apparatus. Despite numerous advantages, the limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. The present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. The output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377.
Highlights
Additive manufacturing technologies manufacture the part through layer-by-layer strategy as opposite to conventional subtractive manufacturing techniques [1]. e major advantage of these advanced manufacturing techniques over traditional manufacturing techniques is digitalization of the process which receives input form computer-generated product designs [2,3,4]. e rapid production and customization of parts with low cost and lower tooling requirements add to the advantages of these manufacturing strategies [5]
FFF process parameters, i.e., layer thickness, orientation angle, raster angle, raster thickness, and air gap has been studied on dimensional accuracy of parts. e genetic algorithm approach has been implemented on to calculate Mod W, which is the output of four different dimensional accuracy parameters with different weightages
In case of air gap, the SN ratio is maximum at 0.004 mm, whereas it is reduced by maximum and minimum values of air gap, i.e., 0 mm and 0.008 mm, respectively. e impact of raster angle and raster width is minimum on SN ration of dimensional accuracy. e SN ratio is maximum at 0° and 60° raster angle settings, whereas 0.4064 raster width yielded better dimensional stability
Summary
Raman Kumar ,1 Jasgurpreet Singh Chohan ,1 Sandeep Singh ,2 Shubham Sharma ,3 Yadvinder Singh, and S. The limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. E present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. E output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377
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