scholarly journals Combining Multi-Objective Optimization, Principal Component Analysis and Multiple Criteria Decision Making for ecodesign of photovoltaic grid-connected systems

2018 ◽  
Vol 27 ◽  
pp. 94-101 ◽  
Author(s):  
J. Raul Perez-Gallardo ◽  
Catherine Azzaro-Pantel ◽  
Stéphan Astier
Keyword(s):  
Decision Making ◽  
Principal Component Analysis ◽  
Multiple Criteria Decision Making ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Criteria ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-response Optimization in Machining of GFRP (Epoxy) Composites: An Integrated Approach

2015 ◽  
Vol 15 (3) ◽  
pp. 267-292 ◽  
Author(s):  
Rajesh Kumar Verma ◽  
Kumar Abhishek ◽  
Saurav Datta ◽  
Pradip Kumar Pal ◽  
S.S. Mahapatra
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Feed Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Temperature ◽  
Multi Objective Optimization ◽  
Gfrp Composites ◽  
Multi Objective ◽  
Multiple Process

AbstractThis paper investigates on optimization of process control parameters during machining (drilling and turning) of Glass Fiber Reinforced Polymer (GFRP) composites by considering multiple process performance yields. The main characteristic indices for evaluating drilling performance are thrust force, torque and delamination factor (at entry as well as exit); the corresponding machining parameters are drill speed, feed rate and diameter of the drill bit. The following process parameters viz. spindle speed, feed rate, and depth of cut have been considered to investigate multiple process responses viz. Material Removal Rate (MRR), surface roughness (Ra), tool-tip temperature (maximum temperature generated during machining at tool-tip) and resultant cutting force whilst turning of GFRP (epoxy) composite specimens. As traditional Taguchi method is unable to solve multi-objective optimization problem; to overcome this limitation; the study proposes Principal Component Analysis (PCA) along with fuzzy logic and finally Taguchi philosophy towards multiple-objective optimization in machining of GFRP composites. Analysis of the solutions for the multi-objective optimization by aforesaid approach has been depicted through two case experimental researches. It has also been observed from drilling experiments that PCA-fuzzy (integrated with Taguchi method) has provided better result as compared to WPCA (Weighted Principal Component Analysis) based Taguchi method. The proposed PCA-Fuzzy based Taguchi method can fruitfully be applied for continuous quality improvement and off-line quality control of the process/product.

scholarly journals An Integrated Method for Multi-Objective Optimization of Multi-Pass Fe50/TiC Laser Cladding on AISI 1045 Steel based on Grey Relational Analysis and Principal Component Analysis

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 151 ◽  
Author(s):  
Qianting Wang ◽  
Xianbin Zeng ◽  
Changrong Chen ◽  
Guofu Lian ◽  
Xu Huang
Keyword(s):  
Principal Component Analysis ◽  
Laser Cladding ◽  
Grey Relational Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Relational Analysis ◽  
Aisi 1045 ◽  
Grey Relational

As an essentially multi-input multi-output process, determination of optimal conditions for laser cladding normally requires multi-objective optimization. To understand multi-response coupling, the effects of processing parameters on the morphology quality of multi-pass laser claddings of Fe50/TiC on medium carbon steel AISI 1045 were investigated based on composite central design using response surface methodology. Multiple responses, including clad width, flatness, and non-fusion area, were transformed into a single objective through grey relational analysis, with weights objectively identified by principal component analysis. The correlation between grey relational grade (GRG) and process parameters was established by regression analysis. The results show that the GRG response model has excellent goodness of fit and predictive performance. A validation experiment was conducted at the process condition optimized for maximum GRG. The relative error of the predicted optimal GRG is 4.87% whereas those of interested individual objectives, i.e. clad width, flatness, and non-fusion area, are 5.73%, 2.97%, and 6.73%, respectively, which verifies the accuracy of the established model. The investigation of mechanical properties suggests the hardness of substrate can be improved from 20 HRC to 60 HRC and wear resistance to over 8.14 times better.

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