Coupler-Curve synthesis of a planar four-bar mechanism using NSGA-II
16 Dec 2012-pp 460-469
TL;DR: The proposed enhancements of the basic scheme of NSGA-II deliver promising improvements in terms of accuracy, and rate of convergence of the solutions, and are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis.
Abstract: This paper applies a genetic algorithm-based optimisation procedure, namely, NSGA-II, to the problem of synthesis of a four-bar mechanism. The internal parameters of ${\texttt{\rm NSGA-II}}$ are tuned using a Design of Experiments (DoE) procedure to enhance the quality of the final results. Constraints are handled through a penalty formulation. Further, a scaling function is introduced, which transforms the penalty terms in a manner that leads to faster convergence of the solutions. The theoretical developments are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis. A comparison of the results vis-a-vis existing ones shows that the proposed enhancements of the basic scheme of ${\texttt{\rm NSGA-II}}$ deliver promising improvements in terms of accuracy, and rate of convergence of the solutions.
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TL;DR: Three improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods and were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.
Abstract: Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof’s criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.
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TL;DR: In this article , a new mathematical approach to solve the mechanism synthesis using motion generation function is presented and the proposed method will study how far the proposed mathematical model (i.e. matrix inversion) will ensure the mechanism to go through prescribed poses or try to approximate them with tolerable error.
Abstract: Looking into today’s mechanism synthesis we see that this well-developed field has been studied from many perspectives such as; transmission angle, working envelop, Grashof criteria and many others. Also, many mathematical representations were used to solve the problems in this field such as vectorial methods, exponent methods dimensionless analysis, genetic algorithm, artificial intelligence. This paper will be presenting new mathematical approach to solve the mechanism synthesis using motion generation function. This proposed method will study how far the proposed mathematical model (i.e. matrix inversion) will ensure the mechanism to go through prescribed poses or try to approximate them with tolerable error.
TL;DR: In this paper , an open-source simulation tool (JaamSim) was used to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility.
Abstract: Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.