Assembly Sequence Planning Research Articles

Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

Purpose Environmental problems in manufacturing industries are a global issue owing to severe lack fossil resources. In assembly sequence planning (ASP), the research effort mainly aims to improve profit and human-related factors, but it still lacks in the consideration of the environmental issue. This paper aims to present an energy-efficient model for the ASP problem. Design/methodology/approach The proposed model considered energy utilization during the assembly process, particularly idle energy utilization. The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). A computational test was conducted using five assembly problems ranging from 12 to 40 components. Findings The results of the computational experiments indicated that the proposed model was capable of generating an energy-efficient assembly sequence. At the same time, the results also showed that MFO consistently performed better in terms of the best and mean fitness, with acceptable computational time. Originality/value This paper proposed a new energy-efficient ASP model that can be a guideline to design assembly station. Furthermore, this is the first attempt to implement MFO for the ASP problem.

Iteratively Refined Feasibility Checks in Robotic Assembly Sequence Planning

Due to shorter product life cycles and increasing customization, production lines must be able to quickly adapt to novel product variants. This requires the automatic generation of assembly sequence plans from product specifications, as manual engineering of plans is slow and labor intensive. The main challenge in assembly planning is that the search for a valid plan must take the capabilities of the robotic system that will execute the plan into account. But checking the feasibility of executing a plan requires a simulation of the system, which slows down the search for a valid and executable plan. We therefore propose and implement two ideas to reduce search times. First, we iteratively refine the feasibility check of an assembly plan from levels taking only parts into account (which is fast) to high-fidelity levels including motion planning and full robotic simulations (which is high fidelity, but slow). Slower levels are only checked if faster levels succeed. The second is that errors in these levels are propagated upwards as symbolic rules that prune the search tree. We demonstrate how our contributions reduce the need for high-fidelity simulations on a two-armed robotic system that assembles product variants out of aluminum profiles.

Assembly sequence plan generation of heavy machines based on the stability criterion

The Assembly or Disassembly Sequence Planning (ASP/DSP) remains an actual optimization problem in the manufacturing industry. It has represented a great deal of interest over the past three decades. Furthermore, given the complexity of today’s mechanisms, an optimal ASP/DSP can reduce the design costs and minimize the maintenance time of industrial equipment. Among the least used criteria in ASP/DSP generation, the stability criterion seems to be quite interesting especially for heavy mechanisms. This paper proposes an approach to generate feasible ASP based on the components’ stability during the assembly operations. The generation of feasible ASP was guaranteed using the geometrical and topological assembly constraints extracted from the CAD assembly. An example of a heavy machine is adopted to highlight the advantages of the proposed approach.

A case study of energy efficient assembly sequence planning problem

Energy efficiency has become an important issue in manufacturing industry, since it is one of the biggest energy consumers in the world. Despite the importance of energy efficiency, it is much obvious that the research in assembly sequence that focus on environmental aspect is still lacking. In Assembly Sequence Planning (ASP), the research on problem optimization is mainly demanded for the effective computational approach to determine the best assembly sequence. This paper presents a case study from an electronic product assembly that considers the energy utilization during assembly process. In particular, the case study focuses to reduce the idle energy utilization in assembly process. The case study was optimized using newly proposed Moth-Flame Optimization (MFO) and then being compared with well-frequent used algorithms including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The result of the computational experiment test was divided into comparison of assembly layout between MFO proposed layout and existing layout. Besides, the statistical test involving Analysis of Variance (ANOVA) and post-hoc test of Fisher’s Least Significant Difference (LSD) were then conducted. The proposed MFO performed better in terms of the best minimum fitness (0.401681), average fitness (0.415308), standard deviation of fitness (0.022601), with appropriate computational time and power consumed. In meantime, the results also indicated that the case study was suitable in the development of energy efficient model for ASP.

Assembly Sequence Planning by Probabilistic Tree Transformation

Various types of computer systems including CAD/CAM systems have been introduced in machine industry. Some of the systems can handle assembly sequence planning, however it requires long time for planning. This paper proposes a method of generating assembly sequences efficiently. This method extracts some parts and/or subassemblies whose possibilities of being removed from a product are strong, and tests whether they can be removed without any geometric interference. By performing these operations repeatedly, the method generates a disassembly sequence of the product, and obtains an assembly sequence by reversing it. The extraction of some parts and/or subassemblies is performed, based on probabilistic tree transformation. The authors present a calculation example by using a software tool integrated with a CAD system.

A Simplified Model for Assembly Precision Information of Complex Products Based on Tolerance Semantic Relations

Assembly precision analysis (APA) plays an important role in the whole life cycle of complex products design, manufacturing, assembly and even remanufacturing. Assembly precision information model (APIM) is usually complex since it is affected by many factors, such as design tolerance of parts, assembly process scheme, assembly sequence planning and tolerance of positioning tooling, etc. Therefore, it is of practical significance for APA to reasonably reduce the workload of assembly precision information (API) modeling. A semantic simplification approach for APIM is proposed in this paper, which mainly takes semantic relations between APIM and design tolerance of parts into consideration. Initially, ontology of structure knowledge of APIM is constructed according to a tolerance standard. Furthermore, simplification rules are respectively established by considering two semantic relations: one semantic relation between deviation change direction and deviation accumulation direction and the other semantic relation among multiple geometric characteristics on the same geometric feature. Additionally, by utilizing ontology reasoning function, the simplified semantic APIM is generated. Finally, the effectiveness of the proposed method is demonstrated by a practical example of engine front auxiliary drive equipment. It is expected that our work would lay the foundation for APA of complex products based on actual measured data.

Micro-Assembly Sequence and Path Planning Using Subassemblies

This paper presents a novel assembly sequence planning (ASP) procedure utilizing a subassembly based search algorithm (SABLS) for micro-assembly applications involving geometric and other assembly constraints. The breakout local search (BLS) algorithm is adapted to provide sequencing solutions in assemblies with no coherent solutions by converting the final assembly into subassemblies which can be assembled together. This is implemented using custom-made microparts which fit together only in a predefined fashion. Once the ASP is done, the parts are manipulated from a cluttered space to their final positions in the subassemblies using a path-planning algorithm based on rapidly exploring random tree (RRT*), a random-sampling based execution, and micromanipulation motion primitives. The entire system is demonstrated by assembling LEGO® inspired microparts into various configurations which involve subassemblies, showing the versatility of the system.

An Optimal Robotic Assembly Sequence Planning by Assembly Subsets Detection Method Using Teaching Learning-Based Optimization Algorithm

In recent days, many interacted shape products have been developed by manufacturing industries for different applications in various fields such as defense, aerospace, and space centers. In manufacturing, 30% of time consumption is due to assembly operation compared with the remaining processes in manufacturing. It is very difficult to get optimal sequence because assembly sequence planning is a multimodel optimization problem. As the number of parts in the assembly increases, the possible number of sequences increases exponentially therefore obtaining the optimal assembly sequence becomes more difficult and time consuming. There exist many mathematical algorithms to obtain optimal assembly sequences. But, recent studies state that they perform poorly when it comes to multiobjective optimal assembly sequence. In recent years, researchers have developed several soft computing-based algorithms for solving assembly sequence problems. In this paper, assembly subset detection method has been introduced. The proposed method is applied for the first time to solve assembly sequence problems. This method eliminates those assembly sets that have more directional changes and require more energy. The method is compared with other algorithms, namely, genetic algorithm (GA), enhanced GA, ant colony optimization (ACO), memetic algorithm, imperialistic harmonic search algorithm, and flower pollination algorithm (FPA), and is found to be successful in achieving the optimal assembly sequence for an industrial product with smaller number of iterations. Note to Practitioners —This paper is motivated by the redesign of helicopter cowling of a Canadian aircraft company using concepts of design for assembly. Though we could reduce the number of parts using advanced composite materials and manufacturing processes, obtaining a feasible assembly for the new assembly structure required a lot of computation time. Hence, the researchers studied the existing literature on assembly sequence generation methods and their limitations, and came up with efficient automated optimal sequence generation method.

Practically Feasible Optimal Assembly Sequence Planning with Tool Accessibility

Practically Feasible Optimal Assembly Sequence Plan (PFOASP) achievement is most important for an industrial engineer to meet the demand of producing the final product in less time and cost. A PFOASP directs the industrial engineer to generate a complete product with less number of manipulations from individual parts. An assembly sequence is said to be optimal from its nature of performing parallel operations to form stable subassemblies which directly reduces the overall assembly time for handling large scale products. Most the researchers followed to ignore tool accessibility in their approaches to avoid the computational complexities. However, the generated solution lacks completeness in practice because tools play the significant role while assembling operation. PFOASP determination is most challenging due to validation of multiple feasibilities qualifying criterion. In this paper, an attempt is made to explain the complexity and the importance of tool accessibility consideration in PFOASP with suitable illustrations. A tool based bounding box method is proposed to solve geometrical feasibility in PFOASP.

Modified BAT Algorithm for Optimum Assembly Sequence Planning

Assembly Sequence Planning (ASP) is one of the important optimization problems in manufacturing. Optimum assembly sequence is difficult due to various reasons: As ASP is a NP hard combinatorial problem, achieving the optimum assembly sequence is a difficulty process. Moreover, ASP problem is multi-model optimization problem where a product can assemble in many possible ways. As the part count in the assembly increases, the time for assembly is more and obtaining the optimum assembly sequences is difficult. Many mathematical algorithms are proposed to obtain optimum solutions, which performs poorly. Meanwhile researchers are motivated towards developing the Artificial Intelligence (AI) techniques to solve ASP problems due to their less search space for implementing even the complex assemblies. The challenging task in ASP problem is automatic extraction assembly constraints to obtain the optimum assembly sequence. Keeping this thing in mind, in this paper, a Modified BAT Algorithm (MBA) has been implemented to solve ASP problem. In this paper first time BAT algorithm is applied to solve discrete optimization problem.

RESEARCH ON ASSEMBLY SEQUENCE PLANNING AND OPTIMIZATION OF PRECAST CONCRETE BUILDINGS

Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building

Assembly sequence planning using soft computing methods: A review

The implementation of artificial intelligence techniques is increasing rapidly in recent years to solve numerous engineering problems. Assembly sequence planning is one of the prominent complex combinatorial problem draw attention of industrial engineers to economize the overall manufacturing cost by minimizing the assembly time and energy. Due to large search space and multiple assembly predicate criteria, researchers are motivated towards efficient utilization of AI techniques to address the problem. Literature review on various artificial intelligence techniques for obtaining the optimal assembly sequence planning are analyzed and the limitations of the existed methodologies are discussed in detail. This review provides an outlook for the researchers on various artificial intell1igent techniques which will be useful to carry out research for obtaining the optimum assembly sequence planning while qualifying various assembly predicate criteria.

Solving Assembly Sequence Planning using Angle Modulated Simulated Kalman Filter

This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. Next, the Angle Modulated SKF (AMSKF) is proposed for solving ASP problem. The main idea of the angle modulated approach in solving combinatorial optimization problem is to use a function, g(x), to create a continuous signal. The performance of the proposed AMSKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that AMSKF outperformed all the algorithms in obtaining the best solution.

Assembly sequences plan generation using features simplification

The automatic generation of the assembly/disassembly plan is considered a topical problem in the computer-aided tools area. The main problem of the major proposed approaches is the excessive processing time to treat the whole assembly model. This paper proposes a simplification approach of CAD models to make the generation of assembly plan easy. The developed method begins by the elimination of connection elements to obtain a simplified assembly sequence. A case-based reasoning algorithm is, then, developed to insert the entire eliminated elements in the global assembly sequence plan. Two examples of CAD assembly are treated in order to compare and validate the obtained results.

Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review

Assembly sequence planning (ASP) is an NP-hard problem that involves finding the most optimum sequence to assemble a product. The potential assembly sequences are too large to be handled effectively using traditional approaches for the complex mechanical product. Because of the problem complexity, ASP optimization is required for the efficient computational approach to determine the best assembly sequence. This topic has attracted many researchers from the computer science, engineering, and mathematics background. This paper presents a review of the research that used soft computing approaches to solve and optimize ASP problem. The review on this topic is important for the future researchers to contribute in ASP. The literature review was conducted through finding related published research papers specifically on ASP that used soft computing approaches. This review focused on ASP modeling approach, optimization algorithms and optimization objectives. Based on the conducted review, several future research directions were drawn. In terms of the problem modeling, future research should emphasize to model the flexible part in ASP. Besides, the consideration of sustainable manufacturing and ergonomic factors in ASP will also be the new directions in ASP research. In addition, a further study on new optimization algorithms is also suggested to obtain an optimal solution in reasonable computational time.

Intelligent robot of inclined assembly sequence planning in Industrial 4.0

In the industry 4.0, the Cyber-physical system (CPS) is one of the most important core which makes the manufacturing process more intelligent. Intelligent assembly operation is an important key in intelligent manufacturing of CPS. To complete the intelligent assembly operation, the cooperation between assembly robotic arm and assembly sequence planning (ASP) is necessary. However, the ASP and writing robotic codes manually is time consuming and requires professional knowledge and experience. Because the Local Coordinate System (LCS) is often ignored when checking for interference. If product have inclined interference and without considering LCS and causing and infeasible ASP. Therefore, this paper proposes a LCCPIAS (Local Coordinate Cyber-Physical Intelligent Assembly System) system to achieve three objective functions. First, this paper presents a dual-projected-based interference analysis approach (DPIAA) that analyzes the relations between components. Second, this paper generates optimal assembly sequence automatically to let the assembly sequence more suitable for the robotic arm to perform the assembly operation. The last one is LCS can recognize inclined interference between components and generate feasible ASP. Furthermore, this paper uses CAD model to verify that the DPIAA is faster and consider LCS interference can solve inclined interference problem. In the future assembly factory, the proposed method can help to realize intelligent manufacturing.

Assembly plans generation of complex machines based on the stability concept

Assembly Sequence Planning (ASP) is one of the well-known optimization problems in the manufacturing industry. It represents a great deal of interest over the past three decades. This paper aims to present a method for generating ASP, taking into account the stability assignment during assembly’s operations. The developed method uses the topological and geometrical assembly constraints extracted and generated from CAD system (assembly constraints, mass and center of gravity of components, interference and contact matrices), to guarantee the generation of a feasible ASP. The main contribution of this research is the development of a method which respects the stability assignment of heavy machines during the assembly’s operations. Also, it reduces the design costs and minimizes the time of replacement or repair of failing components of industrial equipments, which has become complicated task given the complexity of today’s mechanisms. A validation example is used to highlight the advantages of the proposed approach.

Research Method of Assembly Sequence Planning Based on Assembly Constraint Relationship Analysis

In order to effectively reduce the human-computer interaction (HCI) workload before the assembly process simulation in the digital assembly environment, and to enhance the intelligence of assembly sequence planning, we introduce the assembly constraint relationship into the automatic generation of assembly sequences and propose a practical assembly sequence planning method for intelligent assembly. Firstly, starting from the assembly geometry model, an assembly model liaison graph is constructed by combining the analysis of assembly constraint relationship and the definition of disassembly direction rules. Then, according to the liaison graph, disassembly priority constraint matrix based on constraint relationship is constructed, and the automatic generation and dynamic update of the disassembly priority diagram can be realized by the definition of disassembly doubly linked list. According to the diagram, the rationality of the disassembly sequence is determined through the interference check analysis, and then the assembly sequence is automatically generated from the reversed disassembly sequence. Finally, the method in the text is verified by an example case study.

Master Assembly Network Generation

Assembly sequence planning is an important step. Traditional, assembly sequences are not planned for alternatives which limit the flexibility to handle changes on the shop floor. A novel method for generating a master assembly network with alternative sequences based on legacy assembly data for a product family is proposed. This method is inspired by the phylogenetic networks used in biology, namely the soft-wired galled network. A family of three control valves is used as a case study. This method decreases the needed time and cost of introducing a new product. The assembly network increases the flexibility and adaptability of the system.

Computer aided design-based assembly sequence planning: a next stage in agile manufacturing research

Today, certain manufacturing companies have developed agile capabilities to react quickly in accordance with the dynamic demands of the customers. These manufacturing companies are regarded by the modern researchers as 'agile manufacturing companies.' Other companies are unable to compete with agile manufacturing companies. In order to facilitate companies to acquire agility, modern researchers are required to identify the next stage of research whose outcome will enable today's manufacturing companies to become agile manufacturing companies. In this background, in this paper, a literature review is reported. At the end of conducting this literature review, the benefits of implementing agile manufacturing and assembly sequence planning were gathered and compared. Based on this comparison, this paper is concluded by suggesting that determining optimal assembly sequences of the components to produce products for implementing agile manufacturing paradigm in today's manufacturing companies shall be the next stage of research.