Numerical Prediction and Sequential Process Optimization in Sheet Forming Based on Genetic Algorithm

Schmidt, A. A.

doi:10.1080/10426914.2010.536926

Cited by 12 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8 and Tiwari 10 considered a multi-pass rolling system as a sequential process and proposed a framework of optimization using genetic algorithm (GA). Schmidt 11 analyzed the drawing of sheet metal part in the stamping machine as a sequential process and optimized it using a GA. Anand et al. 12 presented the empirical and RSM models through finite element-based simulation experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Sreedharan

Jeevanantham

Ananthanarayanan

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

The multi-stage sequential process with multi-objective is a complex problem to address as the decision made at a particular stage influences the subsequent stage and vice versa. In this article, the effects of input variables of plastic injection, mold, and different plating stages were investigated on different output responses, namely weldline, warpage, length, and various metal plating thicknesses. This paper investigates a real-time industrial data of manufacturing an automotive exterior part made of ABS material. A D-optimal experimental layout with 55 experiments was generated for eight input factors each at three levels. Nine different output responses in each experiment were normalized into a weighted grey relational grade using grey relational analysis coupled with principal component analysis. The solutions obtained by the analysis of variance on weighted grey relational grade, and by the desirability analysis of D-optimal were compared and validated. The confirmation experiments recorded an average improvement in cumulative process outputs as 40.56% by grey relational analysis and 38.50% by desirability analysis.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Sreedharan

Jeevanantham

Ananthanarayanan

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…However, disparities, mentioned in many papers [2][3][4] appear between literature results. The viscosity just after step changes in shear rate is found to increase (shear-thickening behavior) [5][6] or to remain constant (Newtonian behavior) [7]. In short, there were so many disparities exist in quantitative literature data.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Semi-Solid Die-Casting Automobile Part Based on the Thermo-Visco-Plastic Constitutive Relation

Wang

2015

AMR

View full text Add to dashboard Cite

Thermal simulation compression tests were performed on semi-solid billet in order to observe and investigate the behavior of 6061 aluminum alloys while varying the processing parameters such as apparent viscosity, the shear rate and the temperature. Specimens of 6061 aluminum alloys were characterized with their semi-solid behavior during partial melting and holding in the semi-solid state. Furthermore, the constitutive equation of semi-solid 6061 aluminum alloys was investigated. Moreover, the tests allow the apparent viscosity and shear rate of the alloys to be determined as a function of the solid volume fraction and strain rate together with the geometry behavior of the specimen. Utilizing these parameters, semi-solid die casting process of 6061 aluminum alloys could be simulated by the change of the solid volume fractions.

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

Numerical Prediction and Sequential Process Optimization in Sheet Forming Based on Genetic Algorithm

Cited by 12 publications

References 18 publications

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Numerical Analysis of Semi-Solid Die-Casting Automobile Part Based on the Thermo-Visco-Plastic Constitutive Relation

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

Contact Info

Product

Resources

About