Assembly Station Research Articles

Enabling Manual Workplace Optimization Based on Cycle Time and Musculoskeletal Risk Parameters

Recently the concept of Industry 5.0 has been introduced, reinforcing the human-centric perspective for future industry. The human-centric scientific discipline and profession ergonomics is applied in industry to find solutions that are optimized in regard to both human well-being and overall system performance. It is found, however, that most production development and preparation work carried out in industry tends to address one of these two domains at a time, in a sequential process, typically making optimization slow and complicated. The aim of this paper is to suggest, demonstrate, and evaluate a concept that makes it possible to optimize aspects of human well-being and overall system performance in an efficient and easy parallel process. The concept enables production planning and balancing of human work in terms of two parameters: assembly time as a parameter of productivity (system performance), and risk of musculoskeletal disorders as a parameter of human well-being. A software demonstrator was developed, and results from thirteen test subjects were compared with the traditional sequential way of working. The findings show that the suggested relatively unique parallel approach has a positive impact on the expected musculoskeletal risk and does not necessarily negatively affect productivity, in terms of cycle time and time balance between assembly stations. The time to perform the more complex two-parameter optimization in parallel was shorter than the time in the sequential process. The majority of the subjects stated that they preferred the parallel way of working compared to the traditional serial way of working.

Enhanced Decision Support for Multi-Objective Factory Layout Optimization: Integrating Human Well-Being and System Performance Analysis

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.

Effects of the Human Presence among Robots in the ARIAC 2023 Industrial Automation Competition

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling Tool

OCCUPATIONAL APPLICATIONS In the context of Industry 5.0, our study advances manufacturing factory layout planning by integrating multi-objective optimization with nature-inspired algorithms and a digital human modeling tool. This approach aims to overcome the limitations of traditional planning methods, which often rely on engineers’ expertise and inputs from various functions in a company, leading to slow processes and risk of human errors. By focusing the multi-objective optimization on three primary targets, our methodology promotes objective and efficient layout planning, simultaneously considering worker well-being and system performance efficiency. Illustrated through a pedal car assembly station layout case, we demonstrate how layout planning can transition into a transparent, cross-disciplinary, and automated activity. This methodology provides multi-objective decision support, showcasing a significant step forward in manufacturing factory layout design practices.

Design of the New Production Line in the Automotive Company

Innovation is one of the key competitiveness strategies for small, medium, and large companies. Production line innovations using Lean methods are currently one of the most important ways to change the shape of the production line in a positive direction. Emphasis must be placed on the resulting innovation and the form of the production line, which should be as efficient and functional as possible and meet the required parameters of productivity and quality assurance. The primary goal of production line innovations using Lean methods is the elimination of bottlenecks in the production process that do not add value and the balancing of individual assembly stations. The aim of the contribution was to design a semi-automatic line for the completion and testing of seat locks under the name D3P2. The result of the solution is the creation of a robotic workplace that will ensure a higher production capacity and the related packaging of finished products. On the assembly line, an analysis of line balancing was carried out using the Yamazumi graph to eliminate unnecessary assembly process activities, Cycle time and Tact time. An analysis of the measurements of individual production activities and places was carried out on the line, and the proposal for a new production line was subsequently shared. Its main goal was to meet all customer requirements through high flexibility. The result of the analysis and applied measures was a reduction in the cycle time of the new line, balancing of individual posts and economic benefits.

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

Designing Manual Workplace Systems in Engineer-to-Order Enterprises to Improve Productivity: A Kano Analysis

<div>Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality. Ultimately, the study offers a list of requirements that are needed at ETO assembly stations to satisfy employees and improve efficiency of the production processes.</div>

Machine Learning-Driven Maintenance Order Generation in Assembly Lines

The automatic generation of maintenance orders facilitates the prompt detection and root cause analysis of deviations or failures in the assembly process. The aim of this project is to use supervised learning models to recognise deviations in the throughput times of a fully automated assembly process carried out by robots. The model identifies errors, categorises their causes and transmits the information back to the Enterprise Resource Planning (ERP) system. The data collected comes from a development and test assembly station with an industrial robot. The data set from the assembly station was expanded using agent-based simulation in order to train the four models Support Vector Machine, K-Neares Neighbour, Naive Bayes and Decision Tree. The SVM model proved to be the most suitable model for automatic fault detection with an accuracy of 99.51 %. The model was integrated into the assembly station and an algorithm was developed to automatically generate maintenance messages to transmit the failure code to the ERP system.

Development of a Multi-layered Quality Assurance Framework for Manual Assembly Processes in the Aviation Industry

The increasing complexity of products and processes, shorter development cycles, and widely varying functionalities caused by customer requirements make it increasingly difficult to detect sources of error in advance. Therefore, quality assurance plays an important role in every process step to avoid rework or product loss. Manual assembly processes in particular lead to a disproportionately high amount of quality problems on an industrial scale. The reasons for these problems are learning-curve effects, complex and less intuitive assembly steps, and the large number of different products and components per employee. This paper presents a new approach to quality assurance in the aviation industry. In addition to the recognized rules of technology and regulations, which are already very strict (certification specifications, ASTM international standards, etc.), we introduce an additional “cyber-physical” layer. On the hardware side, it consists of a series of optical sensors at the assembly station, a head-mounted device, and intelligent tools. On the software side, we are developing an adaptive framework for optical sensors (AFOS), which provides optical data, data analysis, and evaluation of assembly activities. A confirmation and tacit data acquisition system (CTDS) is used to derive the correctness of each assembly step from optical and machine data provided by smart tools like an intelligent torque tool (ITT). Every step is recorded and confirmed automatically. Both systems form a backbone for worker self-guidance. Using a realistic industrial process as an example, we demonstrate the applicability of the developed systems and the potential savings in terms of costs, throughput time, and rework times.

Intelligent Order Release In Matrix-Structured Assembly Systems

Due to routing and sequence flexibility, matrix-structured assembly systems require new approaches for order release. A prescriptive scheduling and booking of orders on specific assembly stations cannot meet the flexibility requirements. Therefore, this paper presents a novel approach for capacity-oriented order release considering a multidimensional load evaluation. An agent-based simulation environment is used to investigate its performance and compare it to ConWIP control. It can be shown that the novel approach is able to perform intelligent release decisions while leading to better utilization of assembly stations and outperforming other order release procedures.

Task Allocation in Human-Robot Collaboration: A Simulation-based approach to optimize Operator's Productivity and Ergonomics

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future.

The impact of the degree of automation on the configuration of a car battery assembly line

This paper presents the concept of an automotive battery assembly line. The line includes in its structure fully and semi-automated assembly sockets, as well as manual assembly sockets. The transport between the sockets is carried out using an AGV autonomous trolley system. The selection of the assembly process execution method was based on an analysis of the technical feasibility of the process as well as an analysis of the occupation time of individual assembly stations. The resulting line structure indicates to what extent the automation of the assembly process contributes to a change in material flow and influences the way the process is carried out, including a variety of technical means used. The paper presents an analysis of the use of particular workstations. This analysis was used to optimise the flow of the component assembly line. Maximum utilisation of the resources involved in the production process was used as a criterion for optimisation.

Optimizing Manual Assembly Operations: Ergonomics Analysis and Human-Robot Collaboration

Abstract This paper aims to provide a comprehensive overview of the field of ergonomics, with a specific focus on the application of virtual simulation in the context of manual assembly stations assisted by collaborative robots. The theoretical part of this paper presents a concise introduction to the origins and key figures in the field of ergonomics, highlighting the importance and relevance of this discipline. It also discusses the current methods of ergonomic evaluation and the necessary steps involved in conducting such assessments. Furthermore, it delves into the existing standards, associations, and organizations related to ergonomics, as well as the software solutions available for ergonomic analysis. A case study is presented which demonstrates how to perform an ergonomic analysis using the Ergonomics Evaluation module within the 3D Experience platform. The methodology follows a systematic approach, starting with a physical environment simulation to identify key positions for virtual evaluation. These positions are then simulated using a representative 3D model of the assembly station and selected manikins. The chosen ergonomic analysis method is tailored to the specific movements involved in the assembly activity.

PLC Controlled Assembly Automation for Spot Welding Work Station

Automation has created an eminent need in the present trend of manufacturing industries to boost its throughput with reduced manual intervention and benefit in terms of good product quality, reduced scrape rate, and efficient usage of resources. Assembly process necessitates more perfection to achieve good quality products, which can only be possible by employing highly skilled and experienced operators which costs more and hence Automation is the best choice to mitigate this problem. The present work is an effort to upgrade the manual assembly station for spot welding process to assemble three sub-components called cup, fastener and mesh together through spot welding process with an automatic part handling mechanism, controlled by sensor-based Programmable Logic Controllers (PLCs). The automated assembly station resulted in flaw less welded components with reduced cycle time, which enhanced the assembly process productivity from 2 parts/min to 6 parts/min.

The Lunar Space Elevator: A Key Technology for Realising the Greater Earth Lunar Power Station (GEO-LPS)

The Greater Earth Lunar Power Station (GE -LPS) is a multi-purpose concept that aims to solve several critical issues related to lunar development and terrestrial energy production. As the GE -LPS concept and its energy production functions may be scaled to any dimension, larger versions could be positioned in Earth orbit to provide clean solar energy for terrestrial purposes and thereby reduce the mass needed to be launched from Earth to build SPS units by 80% or more. The construction of the GE -LPS from mostly lunar materials requires the establishment of industrial-scale automated mining and manufacturing processes on the Moon. A key technology is a Lunar Space Elevator (LSE) deployed as a transportation system to move SPS components from an anchor point on the surface of the Moon to a docking and assembly station at Earth-Moon Lagrange point 1 (EM-L1). Significantly, a LSE could be built today with existing tether materials such as Dyneema or Zylon which are already commercially available. Additionally, lunar sourced basalt fibre may be sufficient for reinforcing and extending the LSE once it becomes operational. An Earth-pointing LSE could become a valuable cislunar infrastructure asset – “the Suez Canal of cislunar space” – linking the Earth and Moon economies. Note: The ‘O’ symbol is an ancient European symbol for planet Earth: it is used here to mean “Greater Earth”, a region defined by the Earth’s gravitational field, which includes the Moon. Keywords: Solar Power Satellite, Lunar ISRU, Lunar Space Elevator, Energy, Europe

Development and Verification of a Simulation Model of an Automated Assembly Line

Simulation models are integral to IT solutions built according to the Industry 4.0 concept. They are often the basis for developing digital twin solutions, which is why development environments are constantly developed and improved to meet the growing requirements of manufacturing enterprises. This work presents the development and verification of a simulation model of an automated assembly line located in the Smart Factory laboratory of the Poznań University of Technology. One of the criteria for designing the assembly process on this line is the time it takes for transport pallets to pass through individual stations. The work is a response to the need to quickly analyze the designed product assembly scenarios to choose the best one. The operation of the automated line with six assembly stations and allowing for the simultaneous transport of six pallets were considered. The main purpose of the work was to build a simulation model that would allow us to obtain assembly times close to real. The model was created using the Tecnomatix Plant Simulation 16.0 software, and verification work was carried out through a comparative analysis of the results of the simulation time of various assembly scenarios in relation to the actual operation of the line.

Source and Accumulation Analysis of Deviation during Multi-Level Assembly of an Aircraft Rear Fuselage Frame

During the production process of aircraft assembly, weakly rigid parts are gradually assembled into rigid support structures in the aircraft skeleton through several assembly stations. The assembly deviations of this structure determine the quality of the aerodynamic shape of the aircraft. In this paper, we consider multiple sources of deviation (manufacturing deviation, fixture positioning deviation, assembly contact deviation) and investigate the interaction between these sources. Based on the state space approach, a state space equation is developed to reveal the transformation, accumulation and transfer of deviations in the multi-level assembly process (MAP) of weakly rigid parts, and a model is established to accurately simulate and predict the transfer of deviations in the MAP of weakly rigid parts. In this model, the part manufacturing and fixture positioning deviations in typical dimensional planes are regarded as rigid deviations, while the deviations in atypical dimensional planes are regarded as flexible deviations. A spatial triangle penetration detection algorithm based on part measurement point deviations is proposed, combined with the theory of linear elasticity, to describe the relationship between part deviations and assembly contact forces. An example analysis of the assembly process of an aircraft rear fuselage frame structure illustrates the validity of a multi-level assembly deviation transfer model for weakly rigid parts.