Optimization of robotic assembly sequence

The assembly process is combination of several products into a single product. The assembly process affects manufacturing processes very great extent because it is very time consuming and expensive process. The cost of assembly can reach up to 30% of the manufacturing cost. Instability and direction change in assembly process increases the cost of assembly thus the total cost of product is increased very great extent. The production rate decreases with increase in time in assembly process, so the correct assembly sequence is needed to reduce the time and cost of assembly. For the given product assembly model, the sequences and paths of parts is determined by assembly sequence planning (ASP) to obtain the assembly with minimum costs and shortest time. Industries are taking interest in automated assembly system; robotic assembly system comes under category of this assembly system which uses robots for performing the required assembly tasks. This system is one of the most flexible assembly systems to assemble various parts into desired assembly. Robotic assembly systems can handle a wide range of styles and products, so that same product can be assembled different ways, and to recover from errors. Robotic assembly has the ability to switch to different products and styles because robotic assembly is programmable assembly and it has advantage of greater process capability. Robotic assembly is faster, more efficient and precise than any conventional process. It is very important to determine the feasible,stable and optimal assembly sequence for an assembly system. An assembly sequence plan is a high level plan for constructing a product from its component parts. It specifies which sets of parts form subassemblies, the order in which parts and subassemblies are to be inserted into each subassembly,are to be performed. The aim of the present work is to determine stable, feasible and optimal robotic assembly sequence which follows the assembly constraints and reduces the assembly cost.An important feature of this developing process is epresented by the need to automatically determine the assembly plan by recognizing the optimum sequence iv of operations based upon cost and accuracy. Products with large number of parts have several alternative feasible sequences among which optimal assembly sequence is generated. Traditional methods often generate combinatorial explosions of alternatives, with intolerable computational times. A new methodology has been developed to find out the best robotic assembly sequence among the feasible robotic sequences. The feasible robotic assembly sequences have been generated based on the assembly constraints and later, Artificial Immune System (AIS) and particle swarm optimization with mutation operation has been applied to generate feasible and optimal assembly sequences and result is compared with the previous technique. In AIS Clonal selection and Affinity maturation have been implemented to determine the optimal assembly sequence. During the implementation, each assembly sequence and its energy value have been considered as antibody and the antibody affinity respectively. In PSO, each part of the assembled product is considered as the particle (bird) and mutation operation is performed for selected assembly sequence in each iteration to update the position and velocity of each particle. To generate optimal assembly sequence, a fitness function is generated, which is based on the energy function associated with assembly sequence. The sequence which is having the best fitness value followed by all assembly constraints is treated as the optimal robotic assembly sequence. Present research work has been divided into six chapters. The introduction of the topic and the related matters including the objectives of the work are presented in Chapter 1.The literature reviews on different issues of the topic in Chapter 2. In Chapter 3 Steps of assembly sequence generation,assembly constraints, instability is presented Chapter 4 presents generation of stable assembly sequences using Novel immune approach method and Particle swarm optimization with mutation operation for the generation of robotic assembly sequence. In Chapter 5, Result and discussion obtained from different methods are presented. Finally, Chapter 6 presents the conclusion and future work

Automatic Disassembly Task Sequence Planning of Aircrafts at their End-of-Life

RÉSUMÉ Une prise de conscience des problèmes environnementaux à l'échelle mondiale ainsi que des avantages économiques a stimulé les chercheurs à trouver les possibilités de réutiliser et de recycler les produits en fin de vie. Chaque année plusieurs centaines d'avions atteignent globalement fin de leur navigabilité et doivent être retirés du service actif. De ce fait, une attention accrue est maintenant accordée à la fin de vie des avions. Désassemblage joue un rôle important dans la prise de décision de fin de vie. La faisabilité économique du processus de démontage avec beaucoup d'incertitudes est une préoccupation majeure limitant sa mise en oeuvre dans la pratique de l'industrie. De nombreuses recherches dans le domaine de la planification et des opérations de processus de démontage a été fait, qui visent de plus en plus la faisabilité économique du démontage avec la réduction des temps de démontage de proposer des séquences de démontage optimisées. Par conséquent, ces dernières années, de nombreux chercheurs ont publié des articles sur la planification de la séquence de démontage des produits en fin de vie qui est un problème NP-complet optimisation combinatoire. Néanmoins, il y a eu un peu d'attention à la planification de la séquence de démontage d'avions en fin de vie. Cette thèse aborde la planification de séquence de démontage des pièces réutilisables d'avions en fin de vie avant le démantèlement pour le recyclage. Puisque les composants récupérés vont être utilisés à nouveau, une approche non-destructive tout en respectant les instructions fournies dans le manuel d'entretien d'avion intitulé « Aircraft Maintenance Manuel » (AMM) pour le retrait des pièces est prise en considération. Ordonnancement de désassemblage dans cette recherche ne traite pas le séquençage le démontage des pièces comme dans d'autres études, mais il planifie séquence de tâches de démontage dans l'AMM. Une tâche de démontage consiste combinaison d'opérations pour la préparation du démontage ou le procède de démontage pour un ou plusieurs pièces. Tout d'abord, un modèle de séquençage de démontage est proposé par l'examen structure des tâches de démontage dans l'AMM. Ensuite, un code Matlab est développé qui lit la base de données énuméré des tâches et sous-tâches qui sont acquises à partir de l'AMM et génère la séquence de démontage des tâches et sous-tâches automatiquement en utilisant le modèle proposé. Le code est capable de générer des séquences de désassemblage de tâches pour n’importe quelle pièce sollicitée.----------ABSTRACT An awareness of the world’s environmental problems plus economic benefits has stimulated researchers to seek the opportunities to reuse and recycle end-of-life (EOL) products. Each year hundreds of aircraft globally reach end of their airworthiness and should be withdrawn from active service. Due to this fact, increased attention is now being paid to EOL of aircrafts. Disassembly plays an important role in EOL decision making. The economic feasibility of the disassembly process with lots of uncertainties is a main concern limiting its implementation in industry practice. Many researches in the field of disassembly process planning and operations has been done that aim increasing economic feasibility of disassembly with reducing disassembly times with proposing optimized disassembly sequences. Consequently, in recent years, many scholars have published articles on disassembly sequence planning of EOL products that is a NP-complete combinatorial optimization problem. Nevertheless, there has been a scant attention towards disassembly sequence planning of EOL aircrafts. This thesis addresses disassembly sequence planning of reusable components of EOL aircrafts before dismantling it for recycling. Since retrieved components are going to be used again, a nondestructive approach with respecting all instructions provided in aircraft maintenance manual (AMM) for removal of parts is taken into consideration. Disassembly scheduling in this work does not deal with scheduling disassembly of components as in other works but it schedules sequence of removal Tasks in AMM. A removal task consists combination of operations for preparation of disassembly or process of disassembly for a part or multiple parts. At first, a disassembly sequencing model with considering structure of disassembly tasks in AMM is proposed. Afterwards a Matlab code is developed which reads from enumerated database of tasks and subtasks that are acquired from AMM and generates disassembly sequence of tasks and subtasks automatically using the proposed model. The code is capable of generating disassembly sequences of tasks for any given removal task of solicited part. Finally, a greedy and an adaptive greedy algorithm are proposed to optimize disassembly sequence of tasks with minimizing changes in visited zones of disassembly operations. Results generated in Matlab code, suggests effectiveness of proposed adaptive greedy algorithm

Assembly sequence planning using hybrid binary particle swarm optimization

Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objective of this research is to formulate and analyse a mathematical model of the ASP problem. The second objective is to minimize the time of the ASP problem and hence reduce the product cost. A case study of a product consists of 19 components have been used in this research, and the fitness function of the problem had been calculated using Binary Particle Swarm Optimization (BPSO), and hybrid algorithm of BPSO and Differential Evolution (DE). The novel algorithm of BPSODE has been assessed with performance-evaluated criteria (performance measure). The algorithm has been validated using 8 comprehensive benchmark problems from the literature. The results show that the BPSO algorithm has an improved performance and can reduce further the time of assembly of the 19 parts of the ASP compared to the Simulated Annealing and Genetic Algorithm. The novel hybrid BPSODE algorithm shows a superior performance when assessed via performance-evaluated criteria compared to BPSO. The BPSODE algorithm also demonstrated a good generation of the recorded optimal value for the 8 standard benchmark problems

Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Computer Aided Optimal Robotic Assembly Sequence Generation

Robots are widely used for assembly operations across manufacturing industries to attain high productivity through automation. An appropriate robotic assembly sequence further minimizes the total production lead time and overall cost by minimizing the number of assembly direction changes, assembly gripper changes and assembly energy thus selection of a valid optimal robotic assembly sequence is significantly essential to achieve economized manufacturing process. An optimal assembly sequence must comply with various assembly requirements in order to make sure that the sequence of assembly operations is functionally feasible in physical environment. In order to test an assembly sequence for its practical possibility, necessary assembly information must be collected accurately from the product. Obtaining such assembly information from product drawings or Computer Aided Design (CAD) models in manual mode were involved in lots of complexity and needs high level skills to ensure correctness. Though retrieving such information from products with less number of parts is simple and less time consuming, for products composed of huge number parts it is very complicated and time consuming. Besides retrieving the assembly information, using it for validating an assembly sequence further raises the complexity of the Assembly Sequence Generation (ASG) problem. To perform optimal feasible assembly sequence generation efficiently, an effective computer aided automated method is developed and executed at two phases. The first phase of research is mainly focused on representing the assembly information in a streamlined manner by considering all possible states of assembly configurations for ease of computerization and developing efficient methods to extract the assembly information automatically from CAD environment though Computer Aided Automation (CAA). These methods basically use assembly contact analysis, part transformations and laws of equilibrium & balancing of rigid bodies. From the existing ASG methods, it is observed most of the researchers ignored/not-considered few of the assembly information such as assembly stability data and mechanical feasibility data due to higher complexity in retrieving it from CAD environment....

Mathematical Modelling for Load Balancing and Minimization of Coordination Losses in Multirobot Stations

The automotive industry is moving from mass production towards an individualized production, in order to improve product quality and reduce costs and material waste. This thesis concerns aspects of load balancing of industrial robots in the automotive manufacturing industry, considering efficient algorithms required by an individualized production. The goal of the load balancing problem is to improve the equipment utilization. Several approaches for solving the load balancing problem are presented along with details on mathematical tools and subroutines employed.Our contributions to the solution of the load balancing problem are manifold. First, to circumvent robot coordination we have constructed disjoint robot programs, which require no coordination schemes, are more flexible, admit competitive cycle times for some industrial instances, and may be preferred in an individualized production. Second, since solving the task assignment problem for generating the disjoint robot programs was found to be unreasonably time-consuming, we modelled it as a generalized unrelated parallel machine problem with set packing constraints and suggested a tighter model formulation, which was proven to be much more tractable for a branch--and--cut solver. Third, within continuous collision detection it needs to be determined whether the sweeps of multiple moving robots are disjoint. Our solution uses the maximum velocity of each robot along with distance computations at certain robot configurations to derive a function that provides lower bounds on the minimum distance between the sweeps. The lower bounding function is iteratively minimized and updated with new distance information; our method is substantially faster than previously developed methods

A priori checking inconsistencies among strategic constraints for assembly plan generation.

International audienceThis paper is related to the field of assembly plan generation. It describes a new approach to a priori check the consistency of an assembly strategy that is given by the assembly system designers before running an assembly plan generation algorithm. The aim of this work is to improve the assembly plan designer's efficiency by reducing the research space while proving the existence of acceptable solutions. The assembly strategy combined with the product's model implies a set of constraints on the assembly processes. The proposed method determines whether the given assembly strategy produces possible assembly processes. In case of inconsistencies among the strategic constraints, the method will help the designer to identify the contradictory constraints. The set of constraints can be expressed by a Boolean equation. First we present the key concepts and models related to the product, processes and added values in the field of assembly plan generation. Second we define existing strategic constraints, and propose three new ones and a classification of strategic assembly constraints. The originality of the proposed method consists in defining an elementary strategic constraint that is used to describe every other constraint. The proposed method leads to model an assembly strategy by a single Boolean equation that is used to check the inconsistencies. An industrial case study is provided to highlight and to demonstrate the interests of this approach