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The optimal fixture layout is crucial to product quality assurance in the multi-station sheet metal assembly processes. Poor fixture layout may lead to product variation during the assembly processes. In this paper, a genetic algorithm (GA)-based optimisation approach has been presented for the robust fixture layout design in the multi-station assembly processes. The robust fixture layout is developed to minimise the sensitivity of product variation to fixture errors by selecting the appropriate coordinate locations of pins and slot orientations. In this paper, a modified state space model for variation propagation in the multi-station sheet metal assembly is developed for the first time, which is the mathematical foundation of optimal algorithm. An e-optimal is applied as the robust design criteria. Based on the state space model and design criteria, a genetic algorithm is used to find the optimal fixture layout design. The proposed method can greatly reduce the sensitivity level of product variation. A four-station assembly process of an inner-panel complete for a station wagon (estate car) is used to illustrate this method. [ABSTRACT FROM AUTHOR]

Multi-sequence modelling is proposed to optimise assembly sequences for compliant body assemblies such as automotive and aircraft bodies. Multi-sequence modelling is composed of assembly modelling, assembly sequences, tolerance analysis, and assembly operations. Assembly modelling describes the geometric modelling through the liaison graph, which is based on assembly sequence optimisation. Assembly sequences provide information for assembly sequence optimisation. Three-dimensional tolerance analysis is employed to evaluate assembly operations, which is different from previous conceptual tolerance analysis. A genetic algorithm is presented to optimise assembly operations among components. Results show that different sequences lead to a different tolerance of key product characteristics because assembly operations among components are not identical. This paper provides the flowchart of optimising assembly sequences according to the tolerance of key product characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of assembly line worker assignment and balancing problem (ALWABP) which considers ergonomic risks. ALWABP occurs when task times vary according to the assigned worker. Although the operation time of a task is assumed to be fixed in classical assembly lines, it depends on the operator who executes the task in ALWABP. In ALWABP literature, the primary and secondary objectives are minimizing cycle time and balancing workload among workstations smoothly, respectively. When smoothing workload, only task times are taken into consideration in the relevant literature. However, degree of difficulty of tasks is also very important. Two workers executing two different stations with the same station time are assumed to be equally loaded according to the traditional perception. In fact, even they have the same station time; their workloads are different because of the different tasks they execute, in real life assembly line configurations. In order to close this gap between the real life and the literature, this study introduces an ALWABP problem with considering ergonomic risk factors (ErgoALWABP). Due to the complex nature of the problem, optimum seeking methods are not capable of solving it. So, the proposed problem is tackled with the multiple-rule based constructive randomized search approach. Also, OCcupational Repetitive Action method is used for making ergonomic risk assessment. Performance of the proposed solution procedure is compared with the relevant literature on benchmark data. Experimental results show that ergonomic environment could be improved when ergonomic risk factors are taken into account. [ABSTRACT FROM AUTHOR]

The current approach in practice to produce the flowshop schedules of precast production is the dispatching rule technique, which does not guarantee optimal schedules. Some researchers have developed models for a single production line to optimize the scheduling, in which Genetic Algorithm (GA) is used to obtain the solution. However the models cannot be used for multiple production lines. Moreover, some optimization constraints and objectives were missing in the models, such as avoiding frequent type change of precast components during production. Both issues hinder their work to be applied to real precast plants. To overcome the problem, this paper proposes a Flowshop Scheduling Model of Multiple production lines for Precast production (MP-FSM) and develops a corresponding optimization approach to facilitate optimized scheduling by using GA. The approach was validated preliminarily by comparing with traditional scheduling approaches. The results demonstrated that optimized schedules can be obtained by using the proposed approach. [ABSTRACT FROM AUTHOR]

The dimensional quality of auto-body relates to the whole external appearance, wind noise, the effect of closing the door and even driving smoothness of vehicles. The assembly dimensional quality can be improved by optimising assembly operations between parts and reasonable allocating tolerances of components. A multi-attribute directed liaison graph is proposed to describe precedence relationships and key control characteristics (KCCs) to eliminate unfeasible assembly sequences. A hybrid particle swarm optimisation and genetic algorithm is presented to optimise assembly operations for selecting the best assembly sequence. Based on the above, tolerances of the optimal KCCs are designed by using NSGA-II algorithm according to assembly tolerances and manufacturing costs. To improve optimisation effectiveness, the initial population uses the orthogonal experimental design and sensitivity coefficients of KCCs to generate chromosomes. Finally, the work applied the method by illustrating the process of assembly sequence optimisation and tolerance allocation, and the results show that this case verified the proposed method. [ABSTRACT FROM PUBLISHER]

Genetic algorithm has been applied to the problem of determining optimal joint solution of sheet panel assembly in the early design stage. The optimum sought is that for which an assembly of the components shows least variability, as measured by one or more relevant parameters (e.g., a key dimension) for a fixed set of tolerances. A skeleton representation of sheet panel assembly is proposed in order to provide basic support for the algorithm. Genetic coding and operators for the joint selection problem are developed with examination of the algorithm performance. The aim of the method is demonstrated by an example of a multiple parts assembly. [ABSTRACT FROM AUTHOR]