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Research On Production Optimization For The Precision Electronic Surface Mount Process

Posted on:2013-02-05Degree:DoctorType:Dissertation
Country:ChinaCandidate:T M ChenFull Text:PDF
GTID:1118330374976410Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Surface mounting technology (SMT) is one of the precise assembly manufacturingtechnologies, in which components are placed one the surface of Printed Circuit Boards(PCBs). Surface mounting is a time-comsuming process and has a large workload.Therefore, the surface mounting machine (SMM) is a key equipment to complete the process.It includes high technical difficulties, has high precision and is very expensive. So, themachine is the bottleneck machine in the whole assembly line. In this paper, theoptimization theory and method to the mounting process of surface mounting machines withover-head gantry are considered. It is helpful to minimize the assembley time, reduce theproduction cost and improve the production efficiency of the electronic manufacturing.The mounting process optimization problem for the SMM involves two highlyinterrelated sub-problms, one is feeder assignment optimization problem and the other iscomponent mounting sequence optimaization. The two sub-problems are well-knownNP-hard and are difficult to work out. Therefore, according to the general research method,from simple to complex, subproblems are firstly focosed on and then the whole optimizationproblem is considered. For these problems, around building optimization models anddeveloping their efficient sovling algorithms, the following research work has been done:1. The feeder allocation opitimization is considered given that the components mountingsequence is known. An optimization model is presented with the objective is to minimize thetravel distance of the header equipped on the SMM. A new hybrid algorithm of ant-colonyoptimization algorithm (ACO) and genetic algorithm (GA) is proposed to solve the problem.In the algorithm, the ACO is firstly used to search a good solution in each iteration, then thecrossover operator and mutation operator of GA are used to obtain some new solutions whichare then used to update the solutions for the ACO. The algorithm takes avdvange of the GAwhich has wide global search capacity and makes up of ACO which is easily trapped in thelocal optium. The results show the algorithm could obtain satisfied near-optimal solutions tothe feed allocation optimization and the solutions make an average improvement4.48%onthose obtained by the single genetic algorithm.2. The component mounting sequence opitimization is considerd given that the feeders have been assigned to slots. A new optimization model is established for the problem.According to the path optimization characteristic of the problem, a new hybrid algorithm ofACO and shuffled frog-leaping algorithm (SFLA) is proposed to solve it. After the ACOobtain it results at each iteration, a local area deep-search and a global information exchangeprocedure of the SFLA are adapted to futher improve the obtained solution. It is helpful toovercome the weakness of being easily trapped into local optimum of the ACO. Furthermore,a few search strategies such as segmented heuristic function, segmented pheromone andgrouping pheromone update stragegy, suitable to the actual mounting situation, are proposedin the algorithm. The results show the algorithm has a global search capability,and thesolutions obtained make an improvement7.89%averagely on those by the single shuffledfrog-leaping algorithm and3.79%by the single ant colony algorithmon.3. The whole mounting process optimization including the two subproblems decribed inprevious is investigated. Firstly, the difficulties and necessity of the whole optimizationproblem are analysed, and then an optimization model is established according to thecharacteristics of the mounting process of the machine. In the problem solving approach,based on the idea of decomposition and cooperation, tabu search and improved shaffledfrog-leaping algorithm are adopted to solve the feeder assignment subproblem and thecomponent mounting sequence subproblem respectively at each iteration of the algorithm.When solving each subproblem, the best solution found so far for the other subproblem isused. By this means, the two subproblems could be cooperatively solved. Experimentalresults show that it could obtain satisfied near-optimal solutions to the whole optimizationproblem, and the solutions make an improvement11.99%on those obtained by the hybridgenetic algorithm reported in literature.4. The whole mounting process optimization and a tabu search algorithm is futherinvestegated in order to obtain better solutions within shorter time compared to the previousalgorithm proposed. A modified tabu search algorithm with diversification perturbationoperator and mutation operator is developed to solve the whole mounting processoptimizaiton problem. In order to achieve the whole optimization of problem, the algorithm isbased on the traditional tabu search algorithm, adopts a diversification perturbation procedurebased on long-term frequency information and a mutation operator to expand the search place, and a local descent search strategy is embedded into the algorithm to optimize the feederassignment subproblem. Experimental results show that the proposed algorithm could obtainsatisfied near-optimal solutions to problem in a short length of time. Compared to thealgorithm presented previously, it makes an improvement on both solution quality andcomputation speed.In the end, the whole research in the dissertation is summarized and the futureinvetigation on optimization problem in surface mount manufacturing is presented.The dissertation is supported by the State Key Program of National Natural ScienceFoundation of China "Research on the Key Technology of Vision Detection and OptimalControl Oriented to the Precise Electronic Assem bly Lines"(Grant No.60835001), theNational Natural Science Foundation of China "Research on Production Optimization andScheduling Method in Surface Mounting Lines"(Grant No.60804053), the Key ResearchCooperation Project of Guangdong Province and the Ministry of Education "Research anddevelopment and industrialization of high-end automatic SMT equipments"(Grant No.2009A090100027) and the National Research Foundation for the Doctoral Program of HigherEducation of China "Research on Optimization in Electronic assembly"(Grant No.200805611065).
Keywords/Search Tags:surface mounting technology, feeder assignment optimizaton, componentmounting sequence optimization, mounting process optimization, optimization algorithm
PDF Full Text Request
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