A Genetic Algorithm for Assembly Sequence Planning

This work presents a genetic algorithm for assembly sequence planning. This problem is more difficult than other sequencing problems that have already been tackled with success using these techniques, such as the classic Traveling Salesperson Problem (TSP) or the Job Shop Scheduling Problem (JSSP). It not only involves the arranging of tasks, as in those problems, but also the selection of them from a set of alternative operations. Two families of genetic operators have been used for searching the whole solution space. The first includes operators that search for new sequences locally in a predetermined assembly plan, that of parent chromosomes. The other family of operators introduces new tasks in the solution, replacing others to maintain the validity of chromosomes, and it is intended to search for sequences in other assembly plans. Furthermore, some problem-based heuristics have been used for generating the individuals in the population

Product assembly sequence optimization based on genetic algorithm

Genetic algorithm (GA) is a search technique used in computing to find approximate solution to optimization and search problem based on the theory of natural selection. This study investigates the application of GA in optimizing product assembly sequences. The objective is to minimize the time taken for the parts to be assembled into a unit product. A single objective GA is used to obtain the optimal assembly sequence, exhibiting the minimum time taken. The assembly experiment is done using a case study product and results were compared with manual assembly sequences using the ‘Design for Assembly’(DFA) method. The results indicate that GA can be used to obtain a near optimal solution for minimizing the process time in sequence assembly. This shows that GA can be applied as a tool for assembly sequence planning that can be implemented at the design process to obtain faster result than the traditional methods

Comparison of a bat and genetic algorithm generated sequence against lead through programming when assembling a PCB using a 6 axis robot with multiple motions and speeds

An optimal component feeder arrangement and robotic placement sequence are both important for improving assembly efficiency. Both problems are combinatorial in nature and known to be NP-hard. This paper presents a novel discrete hybrid bat-inspired algorithm for solving the feeder slot assignment and placement sequence problem encountered when planning robotic assembly of electronic components. In our method, we use the concepts of swap operators and swap sequence to redefine position, and velocity operators from the basic bat algorithm. Furthermore, we propose an improved local search method based on genetic operators of crossover and mutation enhanced by the 2-opt search procedure. The algorithm is formulated with the objective of minimizing the total traveling distance of the pick and place device. Through numerical experiments, using a real PCB assembly scenario, we demonstrate the considerable effectiveness of the proposed discrete Bat Algorithm (BA) to improve selection of feeder arrangement and placement sequence in PCB assembly operations and achieve high throughput production. The results also highlighted that the even though the algorithms out performed traditional lead through programming techniques, the programmer must consider the influence of different robot motions

Integrating CAD files and automatic assembly sequence planning

In this research study, a fully automated assembly sequence planner was developed, which automatically extracts geometrical information directly from STEP CAD files and then generates feasible assembly sequences with minimum assembly direction reorientations. The effectiveness of using the planner to reduce assembly time was also verified. The research study included three parts;In the first part of the research study, algorithms and software were developed for extracting geometrical information contained in STEP CAD files and for detecting potential collisions between parts during assembly along principal-axis assembly directions, based upon the extracted geometrical information. The developed software directly takes a STEP CAD file of a designed product assembly as input, and outputs six interference-free matrices representing collision information between parts in six principal assembly directions;In the second part of the research study, the algorithm developed in the first part was integrated into a genetic algorithm-based assembly sequence planner. The enhanced planner then was used to find assembly sequences with minimum reorientations automatically. The integrated assembly sequence planner directly takes a STEP CAD file of a designed product assembly as input, and outputs geometrically feasible assembly sequences requiring minimum reorientations;In the third part of the research study, a case study was conducted to verify the impact of assembly direction reorientations on assembly time, for both robot assembly and human operator assembly. Results of the case study show that, for both robot and human operator assembly processes, the number of reorientations in an assembly sequence has a significant impact on assembly time. The results support the primary research hypothesis that more assembly direction reorientations in a sequence require a longer assembly time. The case study helped verify and quantify the importance and effectiveness of using a fully automated assembly sequence planner to reduce the number of assembly direction reorientations in assembly sequence planning

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

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 and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies

Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

An assembly oriented design framework for product structure engineering and assembly sequence planning

The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology

Automatic generation of robot and manual assembly plans using octrees

This paper aims to investigate automatic assembly planning for robot and manual assembly. The octree decomposition technique is applied to approximate CAD models with an octree representation which are then used to generate robot and manual assembly plans. An assembly planning system able to generate assembly plans was developed to build these prototype models. Octree decomposition is an effective assembly planning tool. Assembly plans can automatically be generated for robot and manual assembly using octree models. Research limitations/implications - One disadvantage of the octree decomposition technique is that it approximates a part model with cubes instead of using the actual model. This limits its use and applications when complex assemblies must be planned, but in the context of prototyping can allow a rough component to be formed which can later be finished by hand. Assembly plans can be generated using octree decomposition, however, new algorithms must be developed to overcome its limitations