Optimal Assembly Sequence Research Articles

Research on Orbit Assembly Strategy of Large-scale Space Truss Structure

Background: With the rapid development of spatial technology and mankind's continuous exploration of the space domain, expandable space trusses play an important role in the construction of space station piggyback platforms. Therefore, the study of the in-orbit assembly strategy for space trusses has become increasingly important in recent years. The spatial truss assembly strategy proposed in this paper is fast and effective, and it is applied for the construction of future large-scale space facilities effectively. Objective: The four-prismatic truss periodic module is taken as the research object, and the assembly process of the truss and the assembly behaviors of the spatial cellular robot serving for on-orbit assembly are expressed. Methods: The article uses a reinforcement learning algorithm to study the coupling of truss assembly sequence and robot action sequence, then uses a q-learning algorithm to plan the strategy of the truss cycle module. Results: The robot is trained through the greedy strategy and avoids the failure problem caused by assembly uncertainty. The simulation experiment proves that the Q-learning algorithm of reinforcement learning used for planning the on-orbit assembly sequence of the truss periodic module structures is feasible, and the optimal assembly sequence with the least number of assembly steps obtained by this strategy. Conclusion: In order to address the on-orbit assembly issues of large spatial truss structures in the space environment, we trained the robots through greedy strategy to prevent failure due to the uncertainty conditions both in the strategy analysis and in the simulation study. Finally, the Q-learning algorithm in reinforcement learning is used to plan the on-orbit assembly sequence in the truss cycle module, which can obtain the optimal assembly sequence in the minimum number of assembly steps.

Modified Cat Swarm Optimization for Optimal Assembly Sequence Planning Problems

Modified Cat Swarm Optimization for Optimal Assembly Sequence Planning Problems

Research on optimizing-assembly and optimizing-adjustment technologies of aero-engine fan rotor blades

Aiming at the problems of multiple installations and multiple adjustments of aero-engine fan rotors, this paper conducts research from two aspects: (a) The residual unbalance of rotor roulette is used as the initial unbalance, and the gravitational moment difference of the two blades at the diagonal position of 180° is used as the restriction, the final comprehensive residual unbalance of rotor roulette and rotor blades is the optimization goal. The assembly sequence of blades is planned by the assembly sequence matching planning (ASMP) method, which provides an optimized assembly sequence for the assembly of rotor blades, and shortens the assembly process of rotor; (b) When the static balance of rotating blades is out of tolerance during the assembly process, the adjustment and control of rotating blades is dependent on experience and lack of theoretical guidance, thus, this paper carries out the research on the precise adjustment-control technology of rotating blades based on the balance difference of blades, which provides theoretical guidance for the adjustment-control of rotor blades, and realizes precise adjustment-control of rotor blades. The examples verification results show that the optimizing-assembly and optimizing-adjustment technologies studied in this paper provides optimized decision-making for assembly-adjustment of rotor.

Optimization of Assembly Sequence Planning of Turbine Low-pressure Rotor Blades Based on the Improved Simulated Annealing Algorithm

In view of the NP-hard characteristics of assembly sequence planning problem and the low efficiency of solving the relatively optimal assembly sequence, this paper adopts meta-heuristic algorithms to solve the rotor blades assembly sequence optimization problem, Taking solution accuracy, solution efficiency, and solution robustness as assessment criteria, the effectiveness of solving the rotor blades assembly sequence with several meta-heuristic algorithms is compared and analyzed, the comparison results show that the simulated annealing algorithm has the best solution accuracy, efficiency and robustness for the assembly sequence optimization problem of rotor blades. In order to solve some problems during the solving process of simulated annealing, one improvement is added into it, by adding the reheating process to the SA, the probability that the SA algorithm accepts inferior solutions is increased, and the ability of SA jumping out of the local optimal solution is increased, and the global search capability of the SA algorithm is further enhanced. Finally, the effectiveness of the improved simulated annealing algorithm is verified in this paper by the assembly of steam turbine rotor blades.

An Approach to Integrate Product and Process Design Using Augmented Liaison Diagram, Assembly Sequencing, and Assembly Line Balancing

Abstract Concurrent engineering is a product development approach in which engineers simultaneously design products and processes to improve customer satisfaction, enhance product quality, and reduce product costs. While various design methodologies have been proposed to support concurrent engineering, development of a systematic methodology that comprehensively integrates product and process design is still an ongoing research need. Traditional design for assembly (DFA) has been extended to concurrently design subassemblies and assembly sequences; however, the optimum assembly sequence depends on the efficiency of the assembly line and how assembly tasks are assigned to workstations without causing extensive idle time. This study extends past approaches in the concurrent design of products and processes by integrating assembly line balancing, assembly line design, and sensitivity analysis of assembly line design in addition to assembly line sequencing. In particular, this study proposes an approach to simultaneously design products and processes by integrating (1) a liaison diagram augmented with additional information on the type of liaisons (e.g., screw fixing, snap fitting, or contacting) to find stable subassemblies and corresponding assembly tasks, (2) assembly sequencing to generate all feasible assembly sequences that satisfy requirements for stable subassembly, (3) assembly line balancing to identify all possible assignments of tasks to workstations and to find corresponding cycle times and utilization, (4) assembly line designs that include the types and number of assembly lines for each product design, and (5) sensitivity analysis to evaluate the robustness of the assembly line design.

Robotic Assembly Sequence Generation Using Improved Fruit Fly Algorithm By Applying Gross And Fine Motion As Fitness Equation

Robotic assembly plays a major role in manufacturing industries to reduce the cost of overall product. As the assembly is one of the major manufacturing processes, which involves much information like contact information between the parts, tools needed during assembly, part orientation information during assembly and many more. Most of the researchers considered grippers changes, assembly time, directional changes, part orientation and energy of the part as fitness function. In this research work authors made attempt to consider gross and fine motion as fitness equation to generate optimal assembly sequences with Improved Fruit Fly Algorithm (IFFA). The proposed algorithm is implemented on many industrial products to obtain the optimal assembly sequences.

A study on a Q-Learning algorithm application to a manufacturing assembly problem

The development of machine learning algorithms has been gathering relevance to address the increasing modelling complexity of manufacturing decision-making problems. Reinforcement learning is a methodology with great potential due to the reduced need for previous training data, i.e. the system learns along time with actual operation. This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object, aiming to identify the effectiveness of the proposed approach in the optimisation of the assembly process time. A model-free Q-Learning algorithm is applied, considering the learning of a matrix of Q-values (Q-table) from the successive interactions with the environment to suggest an assembly sequence solution. This implementation explores three scenarios with increasing complexity so that the impact of the Q-Learning's parameters and rewards is assessed to improve the reinforcement learning agent performance. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.

Ant colony optimization for assembly sequence planning based on parameters optimization

As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.

Assembly sequence optimization based on hybrid symbiotic organisms search and ant colony optimization

Assembly sequence optimization aims to find the optimal or near-optimal assembly sequences under multiple assembly constraints. Since it is NP-hard for complex assemblies, the heuristic algorithms are widely used to find the optimal or near-optimal assembly sequences in an acceptable computation time. Considering the multiple assembly constraints, an assembly model is presented for assembly sequence optimization. Then, the hybrid symbiotic organisms search and ant colony optimization is used to find the optimal or near-optimal assembly sequences. The symbiotic organisms search has a relatively strong global optimization capability but weak local optimization capability. On the other hand, the ant colony optimization has the relatively strong local optimization capability for assembly sequence optimization even though the parameters are not optimized. The hybrid symbiotic organisms search and ant colony optimization take advantages of their capacities for assembly sequence optimization. The case study demonstrates that the hybrid symbiotic organisms search and ant colony optimization finds the better assembly sequences within less iteration than the individual ant colony optimization and symbiotic organisms search in most experiments under the same preconditions.

Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge

PurposeThe implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.Design/methodology/approachA spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.FindingsThe proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.Practical implicationsThe assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.Originality/valueThe spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

Design and Fabrication Technology of a PAU and PAR Project

This paper describes the design and fabrication process of a solvent deasphalter unit which included 5 PAUs (Pre-Assembled Process Module Units) and 5 PARs (Pre-Assembled Piperacks) at a refinery in Rotterdam. This paper summarizes the method and experience successfully applied to the shop design and construction process of PAUs and PARs, illustrates the prefabrication and the assembly process of PAU and PAR modules. Many challenges were encountered during the one and a half years’ fabrication, such as mass drawing sheets, fabricate in line with DEP specification and Eurocodes, assembly sequence optimization, the integrate construction method, dimension control and quarantine etc. Design and fabrication processes such as relevant code and standards, shop design softwares, work process of shop design, the application of management system, module arrangement, the process of prefabrication and assembly sequence, the integrate construction method and loadout are highlighted. In the shop design process, 3D Model played a key role. The application of management systems such as PCMS and WIMS significantly improved the efficiency of construction management. Reasonable construction sequence and schedule ensure good performance of fabrication activities. The integrate construction significantly reduced the workload of high altitude work and minimized the project period with efficient construction. It is hoped that the construction experience of this project can give some reference and benefit to similar project in the future.

Process Knowledge Integrated Assembly Sequence Planning for Control Panel

Completely automated assembly sequence planning for control panels is proposed. The proposed algorithm generates the manufacturing bill of material for the assembly processes and total assembly sequence. The algorithm integrates the knowledge of assembly process into a near optimum assembly sequence generation.

Optimal robotic assembly sequence planning using crab shell search algorithm

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.

Optimal robotic assembly sequence planning using crab shell search algorithm

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.

Optimal assembly sequence based on design for assembly (DFA) rules

When designing a product, care must be taken to ensure that the product can be assembled later on at the manufacturing floor. This paper presents a methodology that takes in a CAD design to check its ease of assembly. The proposed methodology can calculate feasibility scores for an assembly step, then use these scores to calculate the optimal sequence of such assembly steps to construct the end product from its loose parts.Two assembly step scoring rules are presented: local and global assembly feasibility. Local assembly feasibility determines the directions in which two subassemblies can move apart an infinitesimally small distance based on contact faces between the subassemblies. Global assembly feasibility considers whether this small distance can be extended to infinity, by checking the overlap of projections of the pair of subassemblies in locally feasible directions. For both of these rules, problems are reduced to previously calculated scores whenever possible, in order to reduce calculation time. These rules are integrated in an algorithm which generates a complete assembly sequence. This assembly sequence is guaranteed to be the global optimum for the given rules.The algorithm successfully generates an assembly sequence for a small example case; the simplification rules reduced the number of local and global assembly feasibility checks by a factor 10 and 6, respectively.

Sequential optimisation of reconfigurable machine cell feeders and production sequence during lean assembly

ABSTRACTThis paper presents a manufacturing model for integrating reconfigurable machine cells as feeders of components to a multi-product assembly line exhibiting a prominent lean characteristic; synchronisation with Takt time. Two mathematical formulations in the form of combinatorial optimisation problems are developed to identify the best reconfiguration for the machines by optimum selection of the modules in the cells to minimise the total error with Takt time and then the optimum sequence of assembling the products to minimise the total reconfiguration time and effort. An elitist genetic algorithm (GA) meta-heuristic is employed twice in succession to search the optimal solutions for both the minimisation problems. The best machine configuration found from the first search is utilised further to identify the optimal assembly sequence. A hypothetical data set representing the features and parameters of the model in conformance with any typical mixed-model assembly is presented for illustration of computational procedure. Synchronisation among machine cells and tardiness are considered as performance evaluation measures to validate the effectiveness of the proposed model.

A quality-driven assembly sequence planning and line configuration selection for non-ideal compliant structures assemblies

In automotive body assembly systems, an optimum assembly sequence planning (ASP) not only increases production efficiency and product quality, but also decreases cost and process cycle time. Typically, ASP evaluation approaches are focused on design for assembly criteria, and very few studies have considered the impact of ASP on dimensional accuracy. The major challenges involving quality-driven ASP evaluation can be enumerated into three categories: (1) batch of compliant non-ideal parts to consider real part defects; (2) variation propagation modeling in multi-station assembly (MSA) system in the presence of stochastic manufacturing errors both at product and process levels; and, (3) the development of dimensional quality criteria for quantitative ASP comparisons. This paper proposes a methodology based on the modeling of dimensional errors propagation in MSA with a batch of compliant non-ideal parts to improve product dimensional quality through optimizing ASP and assembly line configuration. It entails three main steps: (i) assembly sequence generation by k-ary assembly operation method for a predetermined assembly line configuration; (ii) variation propagation simulation taking into account a batch of non-ideal parts, station-to-station repositioning errors, and spring-back phenomenon in MSA system; and, (iii) robust optimization of ASP based on developed quality criteria which contains two quantitative indices. The potential benefits of the proposed methodology are successfully demonstrated on automotive front-rail assembly process.

Effect of Considering Secondary Parts as Primary Parts for Robotic Assembly Using Stability Graph

Robotic assembly sequence generation requires many assembly predicates to generate optimal assembly sequences. Achieving such optimal assembly sequences is difficult as it requires huge search space. In the past literature, most of the researchers try to reduce the search space of assembly sequence planning problem by applying different methods such as computer-aided design-based methods, knowledge-based methods and artificial intelligence-based methods in terms of execution time and number of iterations by considering secondary parts (nuts, bolts, washers, fits, etc.). In this paper, stability graph concept has been introduced to represent the secondary part information for generating optimal assembly sequences. A comparison has been made without fruit fly in terms of execution time and number of iterations while generating optimal assembly sequences with and without consideration of secondary parts as primary parts. To check the effectiveness of the proposed method, three different industrial products with eight parts, 19 parts and 22 parts are considered for generating optimal assembly sequences.

Subassembly Detection and Optimal Assembly Sequence Generation through Elephant Search Algorithm

Most of the engineering products are made with multiple components. The products with multiple subassemblies offer greater flexibility for parallel assembly operation and also disassembly operation during its end of life. Assembly cost and time can be minimized by reducing the number of assembly levels. In this paper, Elephant search algorithm is used to perform Optimal Assembly Sequence Planning (OASP) in order to minimize the number of assembly levels. Subassembly identification technique is used as an integral part of algorithm to identify the parallel assembly possibilities. The proposed method is implemented on industrial products and a detailed comparative assessment has been made with suitable product illustrations to corroborate the efficiency.

Optimal Assembly Sequence generation through computational approach

Optimal assembly sequence (OAS) is always an interesting aspect for an industrial engineer to minimize assembly time and cost, which gives number of assembly levels and sequence of assembly operations. An assembly sequence with more number of parallel possible stable sub-assemblies significantly reduces the overall assembly time for large scale products. Finding such optimal sequences from huge set of all assembly sequences (SAAS) is challenging due to involvement of multiple assembly feasibility validation criteria namely; assembly coherence, geometric feasibility, part stability and mechanical feasibility. In this paper, an efficient computational method is proposed to generate optimal assembly sequences. The method considers extended assembly stability relations to identify stable sub-assemblies for parallel execution. The method is proven in generating optimal solutions for any given product effectively. The method is well discussed and compared with prominent computational methods with suitable product illustrations.