Solving an assembly sequence optimisation problem using the genetic algorithm

As digital transformation (DX) develops, various security threats occur, mainly in the manufacturing industry. This study considers the characteristics of small and medium-sized manufacturing companies in the DX environment. Moreover, the security threats to small and medium-sized manufacturing companies are derived, security areas are established from technical and management perspectives, and minimal guidelines for each area are proposed. Based on these results, small and medium-sized manufacturing companies can establish security policies that are tailored to the size of the company, assets, importance of work, and business areas that require digital transformation.

Assembly and disassembly sequences were planned and assessed independently. However, as eco-friendly product design has been enforced by government regulation, it is needed to plan assembly and disassembly sequence at the same time in early product design stage. This paper aims to propose a sequence structure design method by integrating both assembly and disassembly sequence planning. First, graphs represent feasible assembly sequence with components. Second, adjacency matrices are developed to accumulate the sequence information for computational analysis. Third, a black box diagram is applied to understand and consider the functions of a product during assembly and disassembly operations. Finally, factors are determined to overcome gaps between each assembly and disassembly sequence structure. By considering product architecture with Design Structure Matrix (DSM), we can determine modular design for selective disassembly and interface type for assembly. A case study is performed with two coffee makers to demonstrate the effectiveness of the proposed method.

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained.

Virtual assembly, as a typical application of Virtual Reality (VR), provides an efficient tool to optimise assembly planning and evaluation. In this paper, a VR-based Assembly Planning and Training System (VR-APTS) is introduced. Using a Computer-Aided Design (CAD) assembly model as input, the system can output one or several good assembly sequences with the application of an intelligent bionic algorithm. Key techniques in the development of VR-APTS are discussed. A novel information decomposition and transformation method is put forward to transform data from CAD to VR, and a geometric constraint-based virtual environment is set up to support interactive assembly planning and evaluation. First, an initial optimal assembly sequence is generated by virtual disassembly and ant colony optimisation algorithm. Then, this initial sequence is evaluated by virtual assembly simulation, a more practical and optimal assembly sequence is replaned. This is an iterative process until the best assembly sequence is obtained. An application case is studied to demonstrate the basic advantages of the system for assembly planning and training.

A tree-model for assembly sequences planning is proposed after analyzing the characteristic of assembly sequence evaluation, and the generation algorithm of the tree-model is proposed. Based on the tree-model of assembly sequence, the two-phase method for synthetically evaluating and optimizing assembly sequence layer by layer is expounded. Weights of all evaluation indexes are calculated by using the entropy method. Then, by using the fuzzy comprehensive evaluation, the assembly sequence of all data sub-nodes under A or AP in every layer of the assembly sequence tree-model is optimized. Finally, the optimal assembly sequence is generated. Using this method, the calculation extension of the assembly sequence is reduced, and the evaluation efficiency is heightened. The theory is validated by an example of gear reducer.

Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using Genetic Algorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, Guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of Guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of Guided-GAs. It is found that Guided-GAs can effectively solve the assembly planning problem of larger constraints.

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.

The invention relates to a fixture assembly sequence planning method based on an ant colony algorithm. The fixture assembly sequence planning method comprises the following steps: firstly carrying out product assembly modeling based on a directed graph, thus establishing a foundation for solving assembly sequence planning by using the ant colony algorithm later; constructing the ant colony algorithm oriented to the fixture assembly sequence planning according to the fundamental principles of the ant colony algorithm, utilizing the advantages of the ant colony algorithm to optimize the assembly sequence of fixtures, using redirection times and tool change times as evaluation indexes, and searching out the optimum assembly sequence in numerous feasible assembly sequences; using Matlab programming to realize the optimization procedure of the ant colony algorithm on the fixture assembly sequences to obtain the optimum assembly sequence. The fixture assembly sequence planning method can improve the design efficiency of enterprises, reduces the production cost, shortens the production period, and enhances the market competitiveness.

Image-Bot: Generating Synthetic Object Detection Datasets for Small and Medium-Sized Manufacturing Companies

In recent years, many modern methods/techniques have been developed in manufacturing to meet customer needs. Even though many advanced methods have developed, still the cost of manufacturing a product has not come down; this is due to the assembly operation involved to make the final product. Researchers developed many methods to solve the ASP problem, out of which many of them results in local optima and consume more time during execution. In this paper, a new algorithm, namely the crab shell search algorithm (CSSA), is implemented to solve the ASP problem. The CSSA algorithm is developed based on the concept of crab occupying the shells to protect from the foreign bodies and the sun's heat. The proposed algorithm applied to the eleven-part transmission assembly and the 22 part pulley assembly to obtain the optimal assembly sequences, and the obtained results compared with the different well-known algorithms.

Assembling a product is a delicate process at the borderline between design and manufacturing. Shortening the time between them plays a central role both for quality assurance and fast time to market. In this paper, we describe an automatic tool based on a new method, integrating assembly design, sequence optimization, and advanced rigid body path planning. First, we introduce a greedy algorithm for assembling a product, part by part, based on state-of-the-art path planning. We exploit all the six degrees of freedom of a rigid body to search for collision-free paths, instead of limited motions. Then, we use assembly design in order to limit the search for an optimal assembling sequence and to guarantee geometrical quality among the sequences examined. Disassembly path planning is used here to further cut the state space and to give a quality measure to the sequences. Eventually, we present results for an industrial test case, which has been successfully solved by applying our method.

Nowadays in order to perform assembly process planning by using CAPP (Computer Aided Process Planning), researches to generate the assembly sequence have come under constant interest by many researchers and they are devoted to optimize the assembly sequence according to the novel approach such as Genetic Algorithm and the newly biology inspired principle such as ant colony optimizing method. In this paper, the developed assembly sequence generating system analyzes the relationship between assembled parts based on surface contacting information. Moreover, this system generates the new type of parts liaison graph for the assembly sequences via analyzed information such as common area between parts, related ratio and number of the connected parts. Finally, the optimized assembly sequence is generated logically through the parts liaison graph.KeywordsAssembly SequenceLiaison GraphRelated RatioCommon Area

This research paper analyzes low productivity of company workforce as the key risk factor to business sustainability of a company, with special attention to small and medium-sized companies. The scope of research involves for-profit, and small and medium-sized companies doing business in Serbia. This paper explores whether poor human resource management, and, more particularly, low productivity of employees represent one of the key reasons for poor business performance and eventually failure of the company. The quantitative part of this research was conducted during 2022, by analyzing 468 small and medium-sized companies with less than 250 employees, who ended their business during 2021. Current theory determines human resource management in companies, the influence of different sorts on overall business performance, and employee productivity and its causal effects on business survival. This study attempts to cover identified literature gaps by analyzing the risk influences of low productivity on the failure of small and medium-sized companies, with special diversification between manufacturing and service-oriented companies. Key findings from this research were defined with the help of statistical learning methods, suggesting direct correlation of low productivity in the case of small services-oriented businesses and medium-sized manufacturing companies. Future work on this research includes extension of the analysis to other countries in the West Balkans region and the comparation of all determined causalities.

Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.

Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.