New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

Sindhya, Karthik; Miettinen, Kaisa

doi:10.1080/10426914.2010.523913

Cited by 16 publications

(5 citation statements)

References 34 publications

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“…A binary coded genetic algorithm (GA) has been used to optimize the knowledge base for the FL‐based approaches. Sindhya and Miettinen (2011) presented a new perspective in solving computationally demanding problems such as the optimal control of the continuous casting of steel. Authors considered a multi‐objective formulation of the optimal control of the surface temperature of the steel strand with five objectives, where constraint violations are minimized as objectives because no feasible solutions exist otherwise.…”

Section: Literature Reviewmentioning

confidence: 99%

Improvement of Sigma level of a foundry: a case study

Kumar¹,

Satsangi²,

Prajapati³

2013

The TQM Journal

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PurposeThe purpose of this paper is to explore Six Sigma practices in a casting industry, that could improve the green sand casting process in a foundry by reducing the casting defects. The goal was to determine which variables influenced this evolution and the relative weight of critical success factors as the methodology developed.Design/methodology/approachThe DMAIC (Define, Measurement, Analyze, Improve, and Control)‐based Six Sigma approach is implemented to improve the green sand casting process and has made the process more robust to quality variations. Analysis of various critical process parameters of the melt shop is also carried out with the help of Taguchi's method of experimental design.FindingsThe proposed techniques optimized control factors, resulting in superior quality and stability of the green sand castings process, which contributes to minimizing the casting defects and improving the Sigma level of the industry.Research limitations/implicationsThis study was carried out with some boundaries such as the number of castings of differential housings, available resources, time constraints, etc.Practical implicationsThis paper is most valuable for the foundry industry, which can avail the direct benefit of Six Sigma results from the reduction in the number of defects due to improved casting processes and dispels the myths concerning the hardly ever use of Six Sigma in the casting industry.Originality/valueThe novelty of the paper lies in conducting a comparative study on the performance of a Six Sigma project. The paper will be valuable for quality professionals and management personnel in the casting industry.

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Section: Literature Reviewmentioning

confidence: 99%

Improvement of Sigma level of a foundry: a case study

Kumar¹,

Satsangi²,

Prajapati³

2013

The TQM Journal

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“…Over the past five years, much research in the same spirit has been conducted. See, for example, Deb, Sinha, Korhonen, and Wallenius (2010), Fernandez, Lopez, Bernal, Coello Coello, and Navarro (2010), Fowler et al (2010), Jaszkiewicz (2007), Kaliszewski, Miroforidis, and Podkopaev (2012), Köksalan and Karahan (2010), Sindhya and Miettinen (2011), Thiele, Miettinen, Korhonen, and Molina (2009), and Sinha, Korhonen, Wallenius, and Deb (2014) for similar approaches. We briefly comment on each.…”

Section: Introductionmentioning

confidence: 99%

“…Implied convex (preference) cones (consisting of inferior solutions) are used to rank solutions; the rank order replaces the fitness function. ii Sindhya and Miettinen (2011) apply a hybrid EMO algorithm to casting of steel. Kaliszewski et al (2012) explore interactive EMO with controllable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

et al. 2015

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We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.

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“…The efficacy of data‐driven modeling1–4 and the multi‐objective optimization using Genetic Algorithms5 is now firmly established for the systems of materials interest 6–8. Data‐driven model often implies constructing an empirical description of the system utilizing the available experimental information.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

Kumar

Chakrabarti

Chakraborti

2012

steel research int.

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A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predator–prey genetic algorithms of bi‐objective type, this information was used to construct data‐driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi‐objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.

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New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

Cited by 16 publications

References 34 publications

Improvement of Sigma level of a foundry: a case study

Improvement of Sigma level of a foundry: a case study

Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

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