Relative density and surface roughness prediction for Inconel 718 by selective laser melting: central composite design and multi-objective optimization

2022 ◽  
Author(s):  
Cuiyuan Lu ◽  
Jing Shi
Keyword(s):  
Surface Roughness ◽  
Central Composite Design ◽  
Relative Density ◽  
Selective Laser Melting ◽  
Inconel 718 ◽  
Multi Objective Optimization ◽  
Laser Melting ◽  
Multi Objective ◽  
Roughness Prediction ◽  
Central Composite

scholarly journals Collaborative Optimization on Density and Surface Roughness of 316L Stainless Steel in Selective Laser Melting

2020 ◽  
Author(s):  
Yong Deng ◽  
Zhongfa Mao ◽  
Nan Yang ◽  
Xiaodong Niu ◽  
Xiangdong Lu
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Relative Density ◽  
Selective Laser Melting ◽  
Laser Power ◽  
316L Stainless Steel ◽  
Scanning Speed ◽  
Processing Parameters ◽  
Collaborative Optimization ◽  
Laser Melting

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

scholarly journals On selective laser melting of Inconel 718: Densification, surface roughness, and residual stresses

2020 ◽  
Vol 193 ◽  
pp. 108818 ◽  
Author(s):  
Mohamed Balbaa ◽  
Sameh Mekhiel ◽  
Mohamed Elbestawi ◽  
Jeff McIsaac
Keyword(s):  
Surface Roughness ◽  
Residual Stresses ◽  
Selective Laser Melting ◽  
Inconel 718 ◽  
Laser Melting

Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718

2019 ◽  
Vol 71 (6) ◽  
pp. 787-794 ◽  
Author(s):  
Xiaohong Lu ◽  
FuRui Wang ◽  
Liang Xue ◽  
Yixuan Feng ◽  
Steven Y. Liang
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Cutting Parameter ◽  
Multi Objective

Purpose The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718. Design/methodology/approach Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. Findings This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718. Originality/value There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

scholarly journals Collaborative Optimization of Density and Surface Roughness of 316L Stainless Steel in Selective Laser Melting

Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1601 ◽  
Author(s):  
Yong Deng ◽  
Zhongfa Mao ◽  
Nan Yang ◽  
Xiaodong Niu ◽  
Xiangdong Lu
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Relative Density ◽  
Selective Laser Melting ◽  
Laser Power ◽  
316L Stainless Steel ◽  
Scanning Speed ◽  
Processing Parameters ◽  
Collaborative Optimization ◽  
Laser Melting

Although the concept of additive manufacturing has been proposed for several decades, momentum in the area of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization during SLM to obtain high relative density and low surface roughness simultaneously in the literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. A statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. We observed that the laser power and scanning speed significantly affected the above objective quality, but the influence of the hatch spacing was comparatively low. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

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