Multi-objective Optimal Design of a 2-DOF Flexure-Based Mechanism Using Hybrid Approach of Grey-Taguchi Coupled Response Surface Methodology and Entropy Measurement

2016 ◽  
Vol 41 (12) ◽  
pp. 5215-5231 ◽  
Author(s):  
Shyh-Chour Huang ◽  
Thanh-Phong Dao
Keyword(s):  
Response Surface Methodology ◽  
Optimal Design ◽  
Response Surface ◽  
Hybrid Approach ◽  
Multi Objective ◽  
Entropy Measurement ◽  
Coupled Response ◽  
Grey Taguchi

Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkateshwar Reddy Pathapalli ◽  
Meenakshi Reddy Reddigari ◽  
Eswara Kumar Anna ◽  
P. Srinivasa Rao ◽  
D V. Ramana Reddy
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Content Type ◽  
Relational Analysis ◽  
Desirability Approach ◽  
Grey Relational ◽  
Grey Taguchi

PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

Optimization of Aqueous Two Phase Extraction of Proteins from Litopenaeus Vannamei Waste by Response Surface Methodology Coupled Multi-Objective Genetic Algorithm

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
P. Saravana Pandian ◽  
S. Sindhanai Selvan ◽  
A. Subathira ◽  
S. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Molecular Weight ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Litopenaeus Vannamei ◽  
Value Added ◽  
Phase System ◽  
Two Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Abstract Waste generated from industrial processing of seafood is an enormous source of commercially valuable proteins. One among the underutilized seafood waste is shrimp waste, which primarily consists of head and carapace. Litopenaeus vannamei (L. vannamei) is the widely cultivated shrimp in Asia and contributes to 90 % of aggregate shrimp production in the world. This work was focused on extraction as well as purification of value-added proteins from L. vannamei waste in a single step aqueous two phase system (ATPS). Polyethylene glycol (PEG) and trisodium citrate system were chosen for the ATPS owing to their adequate partitioning and less toxic nature. Response surface methodology (RSM) was implemented for the optimization of independent process variables such as PEG molecular weight (2000 to 6000), pH (6 to 8) and temperature (25 to 45 °C). The results obtained from RSM were further validated using a Multi-objective genetic algorithm (MGA). At the optimized condition of PEG molecular weight 2000, pH 8 and temperature 35 °C, maximum partition coefficient and protein yield were found to be 2.79 and 92.37 %, respectively. Thus, L. vannamei waste was proved to be rich in proteins, which could be processed industrially through cost-effective non-polluting ATPS extraction, and RSM coupled MGA could be a potential tool for such process optimization.

Multi-objective Optimization of UV Spot Curing Technique of Slider - Suspension Attachment Process Using Response Surface Methodology Approach

2021 ◽  
Author(s):  
Santi Pumkrachang
Keyword(s):  
Response Surface Methodology ◽  
Shear Strength ◽  
Response Surface ◽  
Process Parameters ◽  
Epoxy Adhesive ◽  
Quadratic Model ◽  
Uv Curable ◽  
Multi Objective ◽  
Validation Experiment ◽  
Cure Time

The ultraviolet (UV) curing of slider-suspension attachment is going to change from a manual to an automated process. As a result, the bonding parameters of adhesive between slider and suspension needs to be optimized. This paper aims to study two output responses of the UV curable epoxy adhesive i.e., shear strength force and pitch static attitude (PSA) of the joint between slider and suspension in a head gimbal assembly (HGA). Four process parameters were investigated using response surface methodology (RSM) based on face-centered central composite design (FCCD). The RSM was applied to establish a mathematical model to correlate the significance of process parameters and the responses. Then the based multi-objective was applied to determine a quadratic model and obtained the output maximization at 224 g of shear strength force and PSA value close to the target at 1.8 degrees. The input process parameters were optimized at 0.7 s of UV bottom cure time, 120 °C of UV dual side temperature, 5.0 s of UV dual side cure time, and 230 μm of adhesive dot size. The validation experiment showed a prediction response error of less than 7% of the actual value.

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