Assembly Planning Research Articles

Hybrid genetic algorithm with Wiener process for multi-scale colored balanced traveling salesman problem

Colored traveling salesman problem (CTSP) can be applied to Multi-machine Engineering Systems (MES) in industry, colored balanced traveling salesman problem (CBTSP) is a variant of CTSP, which can be used to model the optimization problems with partially overlapped workspace such as the planning optimization (For example, process planning, assembly planning, productions scheduling). The traditional algorithms have been used to solve CBTSP, however, they are limited both in solution quality and solving speed, and the scale of CBTSP is also restricted. Moreover, the traditional algorithms still have the problems such as lacking theoretical support of mathematical physics. In order to improve these, this paper proposes a novel hybrid genetic algorithm (NHGA) based on Wiener process (ITÖ process) and generating neighborhood solution (GNS) to solve multi-scale CBTSP problem. NHGA firstly uses dual-chromosome coding to construct the solutions of CBTSP, then they are updated by the crossover operator, mutation operator and GNS. The crossover length of the crossover operator and the city number of the mutation operator are controlled by activity intensity based on ITÖ process, while the city keeping probability of GNS can be learned or obtained by Wiener process. The experiments show that NHGA can demonstrate an improvement over the state-of-art algorithms for multi-scale CBTSP in term of solution quality.

Fixturing-driven determination of multi-product assembly plans in the context of product variety and reconfiguration

Fixturing-driven determination of multi-product assembly plans in the context of product variety and reconfiguration

Model-based assembly process planning for flexible aircraft cabin architectures

The configuration and equipment of an aircraft cabin has a significant impact on the passenger’s flight experience. To meet the needs of passengers and improve the flight experience, airlines repeatedly demand customised adaptations to the cabin design. The implementation of these changes requires a high degree of flexibility in production and can lead to difficulties in setting up a fixed final assembly line. In addition, unforeseen events and changes in the OEMs’ supply chain require a rapid response in aircraft production to be able to deliver on time. Digitalisation in product development and production planning makes it possible to overcome these challenges and makes an important contribution to flexibility, time and cost efficiency. This paper presents a digital approach to modelling and flexible planning of assembly processes of the cabin. To this end, aircraft design data is automatically linked to the assembly system to react quickly to design changes in the assembly planning process. The integration is realised in a system architecture model depicted with the systems modelling language (SysML). The architecture model follows the formal process description defined in VDI3682. A planning algorithm then uses the production architecture parameters to optimise the assembly processes, e.g. in terms of time. The approach presented is demonstrated using the example of scheduling the pre-assembly processes of the "crown module". The latter includes all structural and functional components above the window panels, such as overhead bins, electrics and air conditioning. The results show how changes in aircraft and cabin design or in the production plant and resources can be flexibly evaluated. This allows conceptual changes to be evaluated and traded before the cabin design is frozen and transferred to real production. These advantages contribute to the time and cost optimisation of aircraft production. This work thus makes a valuable contribution to the further development of digital solutions in aircraft production.

Development of a framework to facilitate a data assembly plan for multi-regional research

A network of organizations works together to facilitate multi-regional research across Canada. This network is streamlining the traditionally burdensome data access process, a major part of which is a project’s data assembly plan (DAP). A framework is proposed for the development of a DAP usable by researchers and multiple data centres across Canada. The network of 13 provincial/territorial and pan-Canadian data centres collaborated to understand variations in data request processes and local requirements. During this collaboration, partners used an iterative approach to review local forms, processes, and undertake consultations with the research community, and to identify critical components of the DAP. In April 2022, the network launched the centrally provisioned, standardized DAP form, which to date has been deployed by approximately five projects. Users have found the DAP’s unique aggregation and documentation capabilities particularly helpful. Overall, the preliminary feedback from researchers has been positive. The DAP has allowed aggregation of specific details about a project’s data requirements: cohort definition(s), data extraction(s) and analytical plans, in a single unified form. The DAP is an important component in streamlining the process for requesting data from multiple provinces/territories, organizations, and data sources. The DAP has ensured consistency across the network’s data centres that are providing data, supporting data linkage, and helping safeguard the quality of analytical results. Further process improvements are anticipated to address user experience feedback and to promote quality research.

A Methodological Approach to Assembly Time Standard Estimation Based on Incomplete Characteristics of the Production Process and Using Small Databases

The problem solved in this article concerns assembly planning, which is time-consuming, but crucial in the development of mechanical products. At the product design stage there is no complete information about the manufacturing process, so it is necessary to develop an approach to help process the uncertain and incomplete information. In order to compare different product variants, the assembly time standard has to be estimated on the basis of the incomplete product and production process characteristics. This paper presents a method for estimating the assembly time standard using time classes, decision tree and evidence theory.

Integrating Small Team Project-Based Learning in Introductory Manufacturing Engineering Technology: Design, Manufacture, and Race Hot Wheels-Type Cars

We propose a compelling session for the ICONEST Conference 2023 that explores the innovative integration of small team project-based learning into introductory manufacturing engineering technology related courses. Our focus is on a hands-on project: designing, manufacturing, and racing Hot Wheels-type cars. This engaging project encompasses various facets of manufacturing engineering technology that includes simple design, manufacturing and assembly planning travelers, scheduling, budgeting, bill of materials, quality inspection, assembly and testing. Educators will gain insights into effective approaches, bridging the gap between theory and practice while equipping students with practical skills. We will explore the use of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) software, fundamental tools in modern engineering, as well as explore simple machining, water jet cutting, and additive manufacturing. By incorporating project-based learning into our course curriculum, we not only foster a deep understanding of manufacturing principles but also cultivate problem-solving, teamwork, and creativity among students. The Hot Wheels-type car project is an ideal vehicle for teaching the entire manufacturing process, from conceptualization to execution and the students enjoy the competition. We believe that our proposal will make a valuable contribution to the conference's mission of advancing engineering technology education, and we look forward to engaging with colleagues and experts to further enhance the quality of manufacturing engineering technology and similar technology programs.

Automating the assembly planning process to enable design for assembly using reinforcement learning

AbstractThis paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process.

Study on intelligent assembly process planning and execution system based on digital twin

PurposeThe purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.Design/methodology/approachBased on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.FindingsDigital twin technology enhances the quality and efficiency of assembly process planning and execution system.Originality/valueIt provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Decentralized Scheduling and Dispatch Control for the Traditional Labor-Intensive Assembly System

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort

Simulation-based validation of process monitoring tasks in assembly

The importance of process monitoring has increased significantly in today’s manufacturing landscape due to several factors. These factors include stricter quality standards, the presence of complex and diverse processes for customized products, and a focus on sustainability in energy and material consumption. In order to seamlessly integrate process monitoring into the production planning process, it is essential to automate the monitoring process planning while also taking into account production planning. Given the increasing importance of high-quality processes and products, assembly processes have become critical, as errors in assembly can lead to expensive production costs. However, generating and validating different process monitoring alternatives in assembly planning can be challenging because different processes and resources exist for different assembly plans. This research aims to solve this problem by providing a method to validate process monitoring alternatives more efficiently, thereby reducing the amount of manual effort and expert knowledge. This goal is achieved through automated validation using two simulation approaches: Collision Testing and Ray Casting. The results of this study are presented to demonstrate the effectiveness of the proposed solution.

Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

Building an Information Modeling-Based System for Automatically Generating the Assembly Sequence of Precast Concrete Components Using a Genetic Algorithm

Facing a significant decrease in economic working processes, Off-Site Construction (OSC) methods have been frequently adopted in response to challenges such as declining productivity and labor shortages in the construction industry. Currently, in most OSC applications, the assembly phase is traditionally managed based on the personal experience and judgment of the site managers. This approach can lead to inaccuracies or omissions, particularly when dealing with a large amount of information on large, complex construction sites. Additionally, there are limitations in exploring more efficient and productive alternatives for rapidly adapting to changing on-site conditions. Given that the assembly phase significantly affects the OSC productivity, a systematic management approach is crucial for expanding OSC methods. Some initial studies used computer algorithms to determine the optimal assembly sequences. However, these studies often focused on geometrical characteristics, such as component weight or spatial occupancy, neglecting crucial factors in actual site planning, such as the work radius and component installation status. Moreover, these studies tended to prioritize the generation of initial assembly sequences rather than providing alternatives for adapting to evolving on-site conditions. In response to these limitations, this study presents a systematic framework utilizing a Building Information Modeling (BIM)–Genetic Algorithm (GA) approach to generate Precast Concrete (PC) component installation sequences. The developed system employs Genetic Algorithms to objectively explore diverse assembly plans, emphasizing the flexibility of accommodating evolving on-site conditions. Real on-site scenarios were simulated using this framework to explore multiple assembly plan alternatives and validate their applicability. Comprehensive interviews were conducted to validate the research and confirm the system’s potential contributions, especially at just-in-time-focused PC sites. Acknowledging a broader range of variables such as equipment and manpower, this study anticipates fostering more systematic on-site management within the context of a digitized construction environment. The proposed algorithm contributes to improving both productivity and sustainability of the construction industry by optimizing the management process of the off-site construction projects.

Cooperative Game-Based Digital Twin Drives Decision Making: Overall Framework, Basic Formalization and Application Case

The emerging progress brought about by Industry 4.0 generates great opportunities for better decision making to cope with increasingly uncertain and complex industrial production. From the perspective of game theory, methods based on computational simulations and methods based on physical entities have their intrinsic drawbacks, such as partially accessible information, uncontrollable uncertainty and limitations of sample data. However, an insight that inspired us was that the digital twin modeling method induced interactive environments to allow decision makers to cooperatively learn from the immediate feedback from both cyberspace and physical spaces. To this end, a new decision-making method was put forward using game theory to autonomously ally the digital twin models in cyberspace with their physical counterparts in the real world. Firstly, the overall framework and basic formalization of the cooperative game-based decision making are presented, which used the negotiation objectives, alliance rules and negotiation strategy to ally the planning agents from the physical entities with the planning agents from the virtual simulations. Secondly, taking the assembly planning of large-scale composite skins as a proof of concept, a cooperative game prototype system was developed to marry the physical assembly-commissioning system with the virtual assembly-commissioning system. Finally, the experimental work clearly indicated that the coalitional game-based twinning method could make the decision making of composite assembly not only predictable but reliable and help to avoid stress concentration and secondary damage and achieve high-precision assembly. Obviously, this decision-making methodology that integrates the physical players and their digital twins into the game space can help them take full advantage of each other and make up for their intrinsic drawbacks, and it preliminarily demonstrates great potential to revolutionize the traditional decision-making methodology.

APLAN: open assembly planning framework in FreeCAD

In the latest decade, the consumer’s desire for personalisation has clearly taken of, challenging manufacturers to diversify their product portfolio while maintaining the same (or near) mass-produced prices. As a result, production is nowadays geared towards low volumes with high variety for which adaptive assembly systems are required. Hence, being able to automatically map out the assembly of complex products yields significant strategic advantages over competing manufacturers. Although a wide range of assembly sequencing algorithms have been developed in the past, researchers and companies still experience intellectual and financial thresholds to applying these software packages themselves. Either the algorithms have to be largely re-engineered from scientific literature, or are offered as part of costly commercial CAD software in the form of plugins and add-ons. However, in some cases, the developed source code is made publicly available via online collaboration platforms though in a variety of programming languages and often requiring the cumbersome installation of additional software libraries.Out of these needs, the proposed APLAN module arose: a software framework allowing developers to share their (dis)assembly planning algorithms in a centralised, uniform manner through the open-source 3D parametric modelling software FreeCAD. As impetus, several in-house developed algorithms for determining topological and geometrical (dis)assembly constraints are included as well as an academic (dis)assembly planner. Previously, such algorithms had to be executed via the terminal, after which the extensive liaison, blocking, and AND/OR graphs were outputted as text or static images. Through APLAN, users are now able to inspect and edit these graphs interactively and dynamically. To conclude, this manuscript finally reports on the benchmarking of the aforementioned connection and obstruction detectors, and their academic and industrial application for human-robot collaboration.

Assembly workshops in virtual reality as an integral part of the assembly planning process

Within the assembly planning process for automotive assembly lines, virtual reality (VR) systems are used mainly on a project-by-project basis. So far, there is no information about the knowledge gained from such workshops and the transferability of the results. Integration of VR as a standard process within the assembly planning has considerable potential. This paper addresses the question of how shopfloor employees and assembly planners can work efficiently in virtual environments on a solution for a concrete planning problem. Existing concepts for virtual workshops are analyzed and a multi-stage procedure for assembly workshops is derived. The assembly workshops in VR are used as a communication platform for different stakeholders, with a focus on the exchange with shopfloor employees. An implementation of the concept concerning its functionality is carried out based on a use case of the design of assembly tools for an automobile assembly line. For this purpose, an assembly platform for the accessibility of an assembly point inside the rear trunk of an automobile is designed in a joint VR workshop and transferred to an operational assembly line. The results show a broad acceptance of the developed solution among the employees.

Robotic assembly systems planning and scheduling problems: A revie

Evolving market trends, characterized by an increasing demand for personalized products with short life cycles and variable demands, pose a significant challenge to the industry. One of the industry's strategies is to adopt robotic assembly systems to improve productivity and increase system flexibility. The widespread adoption of robots in assembly processes is evident; however, success is not guaranteed with implementation alone. Equally critical is addressing assembly planning and scheduling problems in robotic systems. To facilitate understanding, this review offers, in Section 2, a classification of robotic assembly systems, with an emphasis on a new layout termed the robotic matrix-structure assembly system. Section 3 classifies the planning and scheduling problems applied to the robotic assembly systems. In Section 4, we discuss the approaches and techniques used to formulate and solve the planning and programming challenges. Finally, statistical data are presented to illustrate current research trends and identify gaps for future research.

EXTENDING SIMULATION-BASED ASSEMBLY PLANNING TO INCLUDE HUMAN LEARNING AND PREVIOUS EXPERIENCE: A SIMULATION STUDY

When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly.

A new method for functional assembly plan generation and evaluation. Implementation in CapLog, an efficient software

A new method for functional assembly plan generation and evaluation. Implementation in CapLog, an efficient software

Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency

Product assembly is often the last step in the manufacturing process. This task is usually performed by an assembly worker who needs to have practical experience and expertise. For complex products, the assembly may require a long time to study assembly plans. This paper presents a custom-designed Pick-to-Light system using Poka Yoke principles to make this activity easier. The created modular system with two-stage verification serves to guide the assembler precisely. It shows him on the display which parts he should use in a strictly defined assembly step. Our proposal aims to shorten assembly time and reduce the number of errors, which was supported by a case study in a small company with 30 employees. After analysing the data, we can declare that the proposed system significantly reduces the time required for assembly from 7 to 35% and reduces the error rate by 35%. The solution is scalable and flexible, as it can be easily adapted to display assembly steps for a different product.

Evaluation of anxiety among participants in concrete structure assembly schemes based on cognitive behavior analysis

BackgroundAnxiety can seriously impact patients’ psychological and emotional well-being, and sometimes, it can also affect their physical health. Cognitive behavioral analysis is based on cognitive psychology, emphasizing the influence of individual anxiety on emotions and behavior, and is suitable for intervention and treatment of various psychological problems. Therefore, the study uses cognitive behavioral analysis to evaluate the anxiety psychology of participants in prefabricated concrete structure assembly schemes.Subjects and MethodsThe study conducted an EMBase search over the past decade using keywords such as anxiety and psychotherapy to obtain data on alleviating anxiety. Thirty participants in the concrete structure assembly plan were randomly divided into two groups: one group received cognitive behavioral intervention, and the other served as the control group for four weeks. Participants were evaluated for anxiety before and after the experiment using the Self Rating Anxiety Scale (SAS) and the Self Rating Depression Scale (SDS).ResultsThe results showed significant differences in SAS and SDS values between the participants (P<0.05). The group receiving cognitive behavioral intervention showed a significant decrease in SAS and SDS values, while the other group showed no significant changes.ConclusionsParticipants’ anxiety is an essential factor affecting the smooth completion of assembly plans, and timely intervention and treatment are urgently needed. Cognitive behavioral analysis can effectively alleviate participants’ anxiety in prefabricated concrete structure assembly schemes, which is of great significance for the smooth completion of projects.