Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters

2015 ◽  
pp. 536-563 ◽  
Author(s):  
Arindam Majumder ◽  
Abhishek Majumder
Keyword(s):  
Standard Deviation ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Integrated Approach ◽  
Multi Objective ◽  
Research Areas ◽  
Manufacturing Optimization ◽  
Logical Method ◽  
Deviation Method

Multi-objective optimization is one of the most popular research areas in the world of manufacturing. It concerns the manufacturing optimization problems involving more than one optimization simultaneously, but in this present scenario, it is becoming very tough to solve a manufacturing-related multi-objective problem as no logical method has been developed in assignment of response individual weight. Therefore, to tackle this problem, this chapter proposes a new integrated approach by combining Standard Deviation Method with Particle Swarm Optimization. Two examples of optimizing the advanced manufacturing process parameters are performed to test the proposed approach. The examples considered for this approach are also attempted using other established optimization techniques such as Desirability-based RSM and SDM-GA. The results verify the effectiveness of the proposed approach during multi-objective manufacturing process parameter optimization.

Standard Deviation Method Based PSO

2016 ◽  
Vol 7 (2) ◽  
pp. 15-35
Author(s):  
Arindam Majumder ◽  
Abhishek Majumder
Keyword(s):  
Process Parameters ◽  
Performance Index ◽  
Manufacturing Process ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Advanced Manufacturing ◽  
Optimization Of Process Parameters ◽  
Optimal Value ◽  
Single Attribute ◽  
Deviation Method

Nowadays, optimization of process parameters in manufacturing process deals with a number of objectives. However, the optimization of such process becomes more complex if selected attributes are conflicting in nature. Therefore, to overcome this problem in this study a SDM based PSO algorithm is proposed for optimizing the manufacturing process having multi attribute. In this proposed approach the SDM is used to convert multi attributes into single attribute, named as multi performance index, while the optimal value of this multi performance index is predicted by PSO. Finally, three instances related to optimization of advanced manufacturing process parameters are solved by the proposed approach and are compared with the results of the other established optimization techniques such as Desirability based RSM, SDM-GA and SDM-CACO. From the comparison it has been revealed that the proposed approach performs better as compare to the existing approaches.

Multi-Objective Optimization Problems in Taguchi Parameter Design – A Literature Review

2015 ◽  
Vol 813-814 ◽  
pp. 1188-1192
Author(s):  
S. Rajarasalnath ◽  
K. Balasubramanian ◽  
N. Rajeswari
Keyword(s):  
Real Time ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Taguchi Methods ◽  
Quality Characteristic ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Minimum Number ◽  
Set Up ◽  
Single Response

With the advent of latest technology, manufacturing process becomes so sophisticated and complicated that a single response variable (quality characteristic) can not reflect the true product quality and there is intense competition between market participants for cost and delivery (productivity) as well. Any manufacturing process in the present state requires multi objective optimization model to optimize quality, cost and productivity simultaneously. Optimum prediction is critical as it requires lot of experiments for data capturing which involves time and cost. In the present manufacturing set up, it is essential to identify optimum parametric combination for multi objective function real time problems with lesser experiments and lesser effort with better accuracy. Taguchi method is a well known fraction factorial design, which requires minimum number of trials for Identifying optimum parametric combination in real time problems.In this paper an attempt is made to review the literatures of various methods used by researchers for multi - objective optimization problems using Taguchi methods.

A Comparative Study of Firefly Algorithm and Shuffled Frog-Leaping Algorithm for Constrained Free and Manufacturing Optimization Problems

2013 ◽  
Vol 457-458 ◽  
pp. 618-623
Author(s):  
Pasura Aungkulanon ◽  
Isaree Srikun ◽  
Lakkana Ruekkasem
Keyword(s):  
Manufacturing Process ◽  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Finite Sequence ◽  
Shuffled Frog Leaping Algorithm ◽  
Industrial Systems ◽  
Manufacturing Optimization ◽  
Mean And Variance ◽  
Shuffled Frog Leaping

Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups; optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

2019 ◽  
Vol 16 (2) ◽  
pp. 306-321
Author(s):  
Dharmendra B.V. ◽  
Shyam Prasad Kodali ◽  
Nageswara Rao Boggarapu
Keyword(s):  
Process Parameters ◽  
Optimization Problems ◽  
Abrasive Water Jet ◽  
Taguchi Approach ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Abrasive Water Jet Machining ◽  
Practical Implications ◽  
New Algorithms

Purpose The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness. Design/methodology/approach Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters. Findings Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results. Research limitations/implications The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms. Practical implications Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process. Originality/value This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

A Scalable D Dominant Strategy to Solve Large-Dimensional Multi-Objective Optimization Problems

2014 ◽  
Vol 1037 ◽  
pp. 383-388
Author(s):  
Qiong Yuan ◽  
Guang Ming Dai
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Dominant Strategy ◽  
Numerical Comparison ◽  
Multi Objective Optimization ◽  
Dominant Mechanism ◽  
Multi Objective ◽  
Comparison Test ◽  
Research Areas

Solving large-dimensional multi-objective optimization problems is one of the focus research areas of multi-objective optimization evolutionary . When using traditional multi-objective optimization algorithms to solve large-dimensional multi-objective optimization problems,we found that the unsatisfactory optimizing results often exist. To overcome this flaw, in this paper we studied scalable dominant mechanism and proposed a D dominant strategy. According to the superior theory of D strategy ,we improved the current four kinds of typical multi-objective optimization evolutionary algorithms. The numerical comparison test on DTLZ1-6 (20) questions which were solved by the improved algorithms indicated that D strategy had in varying degrees improved the algorithms for solving large-dimensional multi-objective optimization problems .Thus ,we confirmed that the D strategy for solving large-dimensional multi-objective optimization problems is effective.

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