Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm

Selective assembly is a method where components made with wider tolerance are grouped into a number of bins. Based on the best combination of the bin, the corresponding group components are randomly selected and matched together to make an assembly. Existing techniques focused on equal group number partitioning of components, equal probability, equal group width, and equal area methods to minimize either clearance variation or surplus parts using different optimization techniques. Mostly, simple assemblies with two or three components are worked by various authors in the literature without considering their original dimension by considering only their component’s tolerance. In the present work, components are classified into different unequal group numbers based on their tolerance values. The interrelated dimensional assemblies are made in a single stage by matching the parts based on the best bin combination obtained by the artificial bee colony algorithm. A simple linear assembly and a three-armed knuckle joint assembly are considered examples of problems to demonstrate the effectiveness of the proposed method by minimizing the manufacturing cost.

A discrete fireworks optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution

Purpose The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution. Design/methodology/approach In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm. Findings The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods. Practical implications The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts. Originality/value A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

A Multistage Approach to the Selective Assembly of Components Without Dimensional Distribution Assumptions

Selective assembly is a means of obtaining higher quality product assemblies by using relatively low-quality components. Components are selected and classified according to their dimensions and then assembled. Past research has often focused on components that have normal dimensional distributions to try to find assemblies with minimal variation and surplus parts. This paper presents a multistage approach to selective assembly for all distributions of components and with no surplus, thus offering less variation compared to similar approaches. The problem is divided into different stages and a genetic algorithm (GA) is used to find the best combination of groups of parts in each stage. This approach is applied to two available cases from the literature. The results show improvement of up to 20% in variation compared to past approaches.

Building a 3D Computed Tomography Scanner From Surplus Parts

Computed tomography (CT) scanners are expensive imaging devices, often out of reach for small research groups. Designing and building a CT scanner from modular components is possible, and this article demonstrates that realization of a CT scanner from components is surprisingly easy. However, the high costs of a modular X-ray source and detector limit the overall cost savings. In this article, the possibility of building a CT scanner with available surplus X-ray parts is discussed, and a practical device is described that incurred costs of less than $16,000. The image quality of this device is comparable with commercial devices. The disadvantage is that design constraints imposed by the available components lead to slow scan speeds and a resolution of 0.5 mm. Despite these limitations, a device such as this is attractive for imaging studies in the biological and biomedical sciences, as well as for advancing CT technology itself.

Minimizing Surplus Parts in Selective Assembly Using GA

Selective assembly is a method of obtaining high-precision assemblies from relatively low-precision components. In selective assembly, the mating parts are manufactured with wide tolerances. It is impossible that the number of components in the selective group will be the same, and a large number of surplus parts exist according to the difference in the standard deviations of the mating parts. A method with new grouping method and chromosome structure is proposed to minimize surplus parts by using genetic algorithm.