• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.272-279
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• 2002

A common task in assembly modeling is the determination of the position and orientation of a set of components by solving the spatial relationships between them. Assembly models could be constructed at various levels of abstraction. They could be classified into component or geometry-level assembly models. The geometry-level assembly design approach using mating constraints such as against and fits is widely used in the commercial modelers, but it may be very tedious in some cases fur designer. In this paper, we propose a new method to construct an assembly model at the component-level by extracting joint mating features from the kinematics constraints specified between components. The assembly model constructed using the proposed method includes hierarchical and relational assembly models, component/sub-assembly positions and degrees of freedom information. The proposed method is more intuitive and natural way of assembly design and it guarantees the topological robustness of assembly modification such as component replacement and modification.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1881-1897
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• 1994

This paper presents a neural network-based computational scheme to generate the optimized robotic assembly sequence for an assembly product consisting of a number of parts. An assembly sequence is considered to be optimal when it meets a number of conditions : it must satisfy assembly constraints, keep the stability of in-process subassemblies, and minimize assembly cost. To derive such an optimal sequence, we propose a scheme using both the Hopfield neural network and the expert system. Based upon the inferred precedence constraints and the assembly costs from the expert system, we derive the evolution equation of the network. To illustrate the suitability of the proposed scheme, a case study is presented for industrial product of an electrical relay. The result is compared with that obtained from the expert system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.40-50
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• 1993

This paper presents a new method for generating assembly sequences. The method acquires constraints on sequencing the assembly tasks without redundancy. Then it generates the assembly sequences without seatching undesirable tasks by recursively searching all assembly tasks which do not violate the assembly constraints. Since the method does not search undesirable tasks, it can generate assembly sequences for complex products. Current available methods may not generate assembly sequences for the complex product since their search-space increases explosively as the number of parts rises.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.1
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• pp.1-17
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• 1993

It is well known that an assembly operation is usually constrained by the geometric interference between parts. These constraints are normally presented as AND/OR precedence relationships. To find a feasible assembly sequence which satisfies the geometric constraints is not an easy task because of the TSP(Traveling Salesman Problem) nature with precedence constraints. In this paper, we developed an automated system based on Neural Network for generating feasible assembly sequences. Modified Hopfield and Tank network is used to solve the problem of AND/OR precedence-constrained assembly sequences. An economic assembly sequence can be also obtained by applying the cost matrix that contains cost-reducing factors. To evaluate the performance and effectiveness of the developed system, a case of automobile generator is tested. The results show that the developed system can provide a "good" planning tool for an assembly planner within a reasonable computation time period.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.1
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• pp.213-221
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• 1996

An assembly sequence is considered to be optimal when is minimizes assembly cost while satisfying assembly constraints. To derive such an optimal sequence for robotic assembly, this paper proposes a method using a simulated annealing algorithm. In this method, an energy funciton is derived inconsideration of both the assembly constraints and the assembly cost. The energy function thus derived is iteratively minimized until no further change in energy occurs. During the minimization, the energy is occationally perturbed probabilistically in order to escape from local minima. The minimized energy yields an optimal assembly sequence. To show the effectiveness of the proposed method, case studies are presented for industrial products such as an electrical relay and an automobil alternator. The performance is analyzed by comparing the results with those of a neural network-based method, based upon the optimal solutions of an expert system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.105-116
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• 2018

In this study, we consider the assembly line balancing (ALB) problem which is known as an very important decision dealing with the optimal design of assembly lines. We consider ALB problems with soft constraints which are expected to be fulfilled, however they are not necessarily to be satisfied always and they are difficult to be presented in exact quantitative forms. In previous studies, most researches have dealt with hard constraints which should be satisfied at all time in ALB problems. In this study, we modify the mixed integer programming model of the problem introduced in the existing study where the problem was first considered. Based on the modified model, we propose a new algorithm using the genetic algorithm (GA). In the algorithm, new features like, a mixed initial population selection method composed of the random selection method and the elite solutions of the simple ALB problem, a fitness evaluation method based on achievement ratio are applied. In addition, we select the genetic operators and parameters which are appropriate for the soft assignment constraints through the preliminary tests. From the results of the computational experiments, it is shown that the proposed algorithm generated the solutions with the high achievement ratio of the soft constraints.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1267-1270
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• 1997

An assembly sequence is considered to be optimal when it minimizes assembly cost while satisfying assembly constraints. to generate such sequences for robotic assembly, this paper proposes a method using a genetic algorithm (GA). This method denotes an assembly sequence as an individual, which is assigned a fitness related to the assembly cost. Then, a population consisting of a number of individuals evolves to the next generation through genetic operations of crossover and mutation based upon the fitness of the individuals. The population continues to repetitively evolve, and finally the fittest individual and its corresponding assembly sequence is found. Through case study for an electrical relay, the effectiveness of the proposed method is demonstrated. Also, the performance is evaluated by-comparing with those of previously presented approaches such as a neural-netowork-based method and a simulated annealing method.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.391-402
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• 2008

Today's product development environment is characterized as abrupt changes in consumer needs and short lifecycle of the product, which requires manufacturing companies to be equipped with time-to-market and time-to-volume strategies as prime factors for the successful product launch on market. They especially need to bring a product to the market faster with smart methodologies that enable assembly manufacturers to efficiently simulate and manage part assembly information throughout product development process. The research in this paper provides n e-manufacturing methodology considering geometric constraints in order to decide assembly process sequences. The methodology organizies the part layout (position & orientation) in order to optimize human postures during the assembly processes. As the case studies, assembly parts for two commercial products have been generated as CAD models and analyzed for the desired solutions for part assembly sequences using the proposed methodology.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.8
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• pp.669-681
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• 2016

Mechanical designers often make mistakes that result in unwanted over-constraints, causing difficulty in assembly operations and residual stress due to interference among parts. This study is concerned with detection and elimination of over-constraints. Screw theory is a general method that is used for constraint analysis of an assembly and motion analysis of a mechanism. Mechanical assemblies with plane-plane, pin-hole, and pin-slot constraint pairs are analyzed using screw theory to illustrate its utility. As a real-world problem, a ball valve design is analyzed using the same method, and several unwanted over-constraints are detected. Elimination measures are proposed. Nominal dimensions of some parts are adjusted, and dimensions and tolerances of the pins and holes are modified using the virtual condition boundary concept. The revised design is free of over-constraints. General procedure for applying screw theory to constraint analysis is established and demonstrated; it will contribute to improving quality of assembly designs.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.452-457
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• 1992

Cur-rent available methods for generating assembly sequences have a large undesirable search-space. This paper presents a method for reducing the search-space. The method acquires explicitly assembly constraints caused by not only the geometry of parts but also the connectivity between the parts, in simplified form. Then the method generates assembly sequences without searching undesirable tasks using the assembly constraints. If these undesirable tasks are excluded, assembly sequences can be generated by searching only a fraction of all assembly tasks for a product and its subassemblies.