Two-Stage Surrogate Assisted Differential Evolution for Optimization of a Non-Circular Drawing Sequence

Nantiwat Pholdee,Sujin Bureerat,백현무,임용택 한국정밀공학회 2017 International Journal of Precision Engineering and Vol.18 No.4

In this work, a two-stage surrogate assisted optimization technique is proposed for optimizing a non-circular drawing (NCD) sequence. The objective function was introduced to minimize inhomogeneity of the effective strain distribution at the cross-sections of the drawn wire which could deteriorate its delamination characteristics. The design variables introduced were die geometry and reduction of areas of the NCD sequence. Combination of Kriging (KG) models and support vector regression (SVR) which found to be an efficient technique in the literature is used as a surrogate model while a new circular sampling generation technique is proposed and applied leading to a two-stage surrogate assisted optimization strategy. The results revealed that using the proposed two-stage surrogate assisted optimization strategy can reduce surrogate model uncertainty within the same total number of training points compared to a traditional approach. The optimum results showed better effective strain homogeneity at the cross-section of the drawn wire than the original wire drawing (WD) process and NCD sequence with the same total reduction of area and less number of passes.

Multi-objective reliability-based topology optimization of structures using a fuzzy set model

Suwin Sleesongsom,Sujin Bureerat 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.10

This research proposes a multi-objective reliability-based topology optimization (MORBTO) for structural design, which considers uncertain structural parameters based on a fuzzy set model. The new technique is established in the form of multi-objective optimization where the equivalent possibilistic safety index (EPSI) is included as one of the objective functions along with mass, and compliance. This technique can reduce complexity due to a doubleloop nest problem used previously due to performing single objective optimization. The present technique can accomplish within one optimization run using a multi-objective approach. Two design examples are used to demonstrate the present technique, which have the objectives as structural mass and compliance with the constraint of structural strength. The results show the proposed technique is effective and simple compared to previous techniques.

Structural health monitoring through meta-heuristics - comparative performance study

Pholdee, Nantiwat,Bureerat, Sujin Techno-Press 2016 Advances in computational design Vol.1 No.4

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

Optimization of Flatness of Strip during Coiling Process based on Evolutionary Algorithms

Nantiwat Pholdee,Sujin Bureerat,박원웅,김동규,임용택,권혁철,천명식 한국정밀공학회 2015 International Journal of Precision Engineering and Vol. No.

In this paper, an optimization study was conducted for improving flatness of the strips during coiling process. The evolutionary optimizer coded by MATLAB is used for reducing axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love’s elastic solution within a thin strip during the coiling process which might cause irregular surface profile of the strip. An improved differential evolution (DE) method employing opposition-based concept is newly proposed in the present work and is used along with several well-established evolutionary algorithms (EAs) to obtain the optimal processing parameters such as spool geometry and coiling tension of the strip coiling process. It was found that the newly proposed differential evolutionary algorithm outperformed other EAs according to the present study. This kind of optimization studies will be helpful in reducing the edge wave defects during the strip coiling process to improve product quality.

Optimal fin planting of splayed multiple cross-sectional pin fin heat sinks using a strength pareto evolutionary algorithm 2

Ramphueiphad, Sanchai,Bureerat, Sujin Techno-Press 2021 Advances in computational design Vol.6 No.1

This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

Optimization of Flatness of Strip during Coiling Process based on Evolutionary Algorithms

Pholdee, Nantiwat,Bureerat, Sujin,Park, Won-Woong,Kim, Dong-Kyu,Im, Yong-Taek,Kwon, Hyuck-Cheol,Chun, Myung-Sik Korean Society for Precision Engineering 2015 International Journal of Precision Engineering and Vol.16 No.7

In this paper, an optimization study was conducted for improving flatness of the strips during coiling process. The evolutionary optimizer coded by MATLAB is used for reducing axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within a thin strip during the coiling process which might cause irregular surface profile of the strip. An improved differential evolution (DE) method employing opposition-based concept is newly proposed in the present work and is used along with several well-established evolutionary algorithms (EAs) to obtain the optimal processing parameters such as spool geometry and coiling tension of the strip coiling process. It was found that the newly proposed differential evolutionary algorithm outperformed other EAs according to the present study. This kind of optimization studies will be helpful in reducing the edge wave defects during the strip coiling process to improve product quality.

A Comparative Study of Eighteen Self-adaptive Metaheuristic Algorithms for Truss Sizing Optimisation

Nantiwat Pholdee,Sujin Bureerat 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.8

Performance comparison of meta-heuristics (MHs) is conducted for truss sizing design. Six traditional truss sizing design problemswith mass objective function subject to displacement and stress constraints were employed for performance test. The test problems havetwo types with and without including buckling constraints. Eighteen self-adaptive MHs from literature are employed to tackle the trusssizing problems. The results from implementing the self-adaptive MHs are compared in terms of convergence rate and consistency. It isfound that for the test problem without buckling constraints, the top two optimisers according to the statistical Wilcoxon rank sum testsare Success-History Based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) and Success-HistoryBased Adaptive Differential Evolution (SHADE) while the top two optimiser for the test problems with buckling constraints is LSHADEand L-SHADE with Eigenvector-Based Crossover and Successful-Parent-Selecting Framework (SPS-L-SHADE-EIG). Thebuckling constraints are significantly important and should be included to truss design subjected to static loads.

Optimum design of a walking tractor handlebar through many-objective optimisation

Mahachai, Apichit,Bureerat, Sujin,Pholdee, Nantiwat Techno-Press 2017 Advances in computational design Vol.2 No.4

In this work, a comparative study of multi-objective meta-heuristics (MOMHs) for optimum design of a walking tractor handlebar is conducted in order to reduce the structural mass and increase structural static and dynamic stiffness. The design problem has objective functions as maximising structural natural frequencies, minimising structural mass, bending deflection and torsional deflection with stress constraints. The problem is classified as a many-objective optimisation since there are more than three objectives. Design variables are structural shape and size. Several well established multi-objective optimisers are employed to solve the proposed many-objective optimisation problems of the walking tractor handlebar. The results are compared whereas optimum design solutions of the walking tractor handlebar are illustrated.

Process optimization of a non-circular drawing sequence based on multi-surrogate assisted meta-heuristic algorithms

Nantiwat Pholdee,백현무,Sujin Bureerat,임용택 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.8

Process optimization of a Non-circular drawing (NCD) sequence of a pearlitic steel wire was performed to improve the mechanicalproperties of a drawn wire based on surrogate assisted meta-heuristic algorithms. The objective function was introduced to minimizeinhomogeneity of effective strain distribution at the cross-section of the drawn wire, which could deteriorate delamination characteristicsof the drawn wires. The design variables introduced were die geometry and reduction of area of the NCD sequence. Several surrogatemodels and their combinations with the weighted sum technique were utilized. In the process optimization of the NCD sequence, thesurrogate models were used to predict effective strain distributions at the cross-section of the drawn wire. Optimization using Differentialevolution (DE) algorithm was performed, while the objective function was calculated from the predicted effective strains. The accuracyof all surrogate models was investigated, while optimum results were compared with the previous study available in the literature. It wasfound that hybrid surrogate models can improve prediction accuracy compared to a single surrogate model. The best result was obtainedfrom the combination of Kriging (KG) and Support vector regression (SVR) models, while the second best was obtained from the combinationof four surrogate models: Polynomial response surface (PRS), Radial basic function (RBF), KG, and SVR. The optimum resultsfound in this study showed better effective strain homogeneity at the cross-section of the drawn wire with the same total reduction of areaof the previous work available in the literature for fewer number of passes. The multi-surrogate models with the weighted sum techniquewere found to be powerful in improving the delamination characteristics of the drawn wire and reducing the production cost.

Synthesis of four-bar linkage motion generation using optimization algorithms

Phukaokaew, Wisanu,Sleesongsom, Suwin,Panagant, Natee,Bureerat, Sujin Techno-Press 2019 Advances in computational design Vol.4 No.3

Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.