Manufacturing of high precision assemblies pose a great challenge to engineers. High precision assemblies are generally assembled using selective assembly when the assembly tolerance requirement is less than the sum of the part tolerances. Although extensive research has been done in selective assembly modelling for minimising surplus parts and tolerance variation, they do not suit given specifications. In this paper, a new selective assembly model using a genetic algorithm is proposed to provide a detailed method for assembling parts for achieving specified assembly tolerance with minimum surplus parts. This model provides the best combination of selective groups and the number of assemblies in each group and so the assembly process is simplified. Genetic Algorithm is used to find the best combinations and the number of assemblies in each combination to minimize surplus parts. This paper analyses the effectiveness of the model for given target assembly tolerance for a linear assembly and it can be extended to any type of product.

高精度组件的制造对工程师提出了巨大的挑战。当装配公差要求小于零件公差之和时，通常使用选择性装配来装配高精度装配。尽管在选择性装配模型方面已经进行了广泛的研究，以最大程度地减少多余零件和公差变化，但它们不适合给定的规格。在本文中，提出了一种新的使用遗传算法的选择性装配模型，以提供一种详细的装配零件的方法，以实现具有最小剩余零件的指定装配公差。该模型提供了选择性组和每个组中装配数量的最佳组合，因此简化了装配过程。遗传算法用于查找最佳组合以及每个组合中的装配数量，以最大限度地减少多余零件。本文针对给定的线性组装目标组装公差，分析了该模型的有效性，该模型可以扩展到任何类型的产品。