Assembly Line Production Research Articles

Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

ABSTRACT The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

QUALITY ASPECTS IN THE WELDING OF SOME ELEMENTS OF MICRO-MOTORS IN AUTOMOTIVE AND HOUSEHOLD APPLIANCES INDUSTRY

In the automotive and household appliances industry, there is a permanent concern to find fast and efficient welding methods and technologies, pressure welding being one of the usual joining methods, due to the minimum qualification requirements, cheap equipment, ease of control, high speed of operation, repeatability and suitability for automation or robotization for inclusion in high production assembly lines. In the paper, quality aspects are described in spot welding of some elements of micro-motors in the automotive industry and household appliances by using tools that allow highlighting the fact that the process is controllable from a statistical point of view, concerning two micro-motors from the production flow.

TV-ALP: A log dataset of television assembly line production under multi-person collaboration for process mining research

TV-ALP: A log dataset of television assembly line production under multi-person collaboration for process mining research

Robotic assembly line balancing considering the carbon footprint objective with cross-station design

Robotic assembly lines are widely applied in the manufacturing sector to produce a wide range of products because of their efficiency and multifunctionality. The robotic assembly line balancing problem (RALBP) is a combinatorial optimization problem where the decision variables are task assignment and robot allocation. However, RALBP considering carbon footprint, which is a very significant environmental concern, has scarcely been studied in the literature and a practical “cross-station” design is never mathematically formulated. In this paper, a mixed-integer programming model is proposed to optimize the two objectives according to the Pareto principle. A particle swarm algorithm (PSO) with some improvement rules is designed to solve the problem. To examine the efficiency of the algorithm, computational experiments including five medium-sized and five large-sized datasets are conducted. The results show that the efficiency of PSO is better than that of four other classic algorithms in terms of three evaluation metrics. Further, the production manager and assembly line designer can choose the appropriate production plan and upgrade the line configuration.

Solution approach using heuristic and artificial neural networks methods in assembly line balancing problems: A case study in the lighting industry

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

A deep learning approach for balance optimisation of patch panel assembly line

This paper introduces an integrated methodology to enhance the efficiency of patch panel assembly lines by harnessing advanced artificial intelligence (AI) techniques. The primary objective is to minimise the total completion time of patch panels, a pivotal metric for manufacturing efficiency. The study employs the Assembly Line Production Optimisation (ALPO) model to analyze and enhance the production capacity of the bottleneck station. Existing challenges related to patch panel assembly line task distribution, process content, and tooling layout are scrutinised against production process standards for operational measurements. To achieve improvement, Deep Neural Network (DNN) and Recurrent Neural Network (RNN) models are employed in hidden layers, supplemented by deep feed-forward (DFF) layers for output, in adherence to engineering design principles. The bottleneck station's load rate is successfully reduced to below 80%, contributing to an overall balance rate increase from 73% to 88%. These outcomes signify a notable enhancement in production efficiency and employee satisfaction. The research utilises a dataset derived from the Microsoft Azure AI-based PdM dataset. The paper illustrates how AI techniques offer a comprehensive solution to address the inherent complexities in patch panel assembly line optimisation.

Analyzing the Comprehensive Influence of Prada's Business Strategies

Prada, founded in Milan, Italy in the 20th century by Mario Prada, is popular as a century-old luxury brand for many reasons. It was founded from the unique business and fashion vision of its manager, Miuccia Bianchi Prada - granddaughter of Mario Prada. Her creativity and vision, and her sense of fashion trends have made her a very successful brand. Later cooperation with Patrizio Bertelli also brought a good influence to Prada. Different from the earliest pure handmade production process, Patrizio Bertelli brought the business model of assembly line production, greatly improved productivity, reduced the time cost of production, and made more quantity and better quality products in a shorter time. Prada has a high status in people's hearts as a world-renowned brand, and has a certain influence on people's daily dressing style. Secondly, Prada is liked for its simplicity and durability, using the Re-Edition 2000 Re-Nylon recycled nylon Hobo as an example. The material of nylon is durable and waterproof, easy to damage, light weight, large capacity, good to match all types of clothing, and relatively cheap price compared to other big brands, until this year many people are willing to buy this product. In addition, Prada's attention to the time environment also makes it have a good impression in people's minds and they are more willing to pay attention to the brand.

Data-driven analysis and human-centric assignment for manual assembly production lines

Flow production-oriented assembly production lines have become crucial in various industries with the development of Industry 4.0, leading to mass customization and personalized production. In addition, Industry 5.0 has emerged, which emphasizes human-centric design. Manual assembly production line balancing, considering worker ergonomics, has also gained substantial attention. This study aims to solve the line balancing problem through combination optimization considering ergonomic risks based on many studies on human-centric production lines. A framework of the data-driven analysis and assignment for manual assembly production lines is proposed herein. The proposed framework defines and analyzes the process difficulty and the worker’s process ability and assigns a skilled worker to a process through the presented heuristic algorithm. To demonstrate the applicability of the proposed framework, a case study was conducted on an experiment imitating a home appliance assembly line in the United States. This paper provides empirical evidence of a framework aimed at realizing human-centric business objectives through case study. Future studies on the data-driven analysis and assignment for manual assembly production lines are anticipated to exhibit a range of variations such as line balancing problems and evaluation methods.

Intelligent Collaboration and Control of Robots in Assembly Production Lines

Intelligent Collaboration and Control of Robots in Assembly Production Lines

Prediction of Remaining Execution Time of Business Processes With Multiperson Collaboration in Assembly Line Production

Prediction of Remaining Execution Time of Business Processes With Multiperson Collaboration in Assembly Line Production

Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites

This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.

Dirençli Punta Kaynağı Prosesinin KNN ve CART Makine Öğrenimi Teknikleri ile Değerlendirilmesi

Spot welding, a type of resistance welding, is a welding application widely used in the production area and it is a common method for joining metal sheets. The spot-welding process is widely used in many production areas, especially in the automotive industry, radiator, and wire mesh production. Spot welding in car production lines is mainly performed by robotic applications. Industry 4.0 and digital transformation trends have led to unprecedented data growth. Nowadays, the manufacturing industry benefits from the power of machine learning and data science algorithms to monitor production processes and make predictions for quality, maintenance, and production optimization. Applying machine learning algorithms reduces the duration and cost of experiments. This study aims to confirm whether the spot welding, applied by robotic arms, is within the ideal spot-welding norms, in real production area. The ideal parameter norms were evaluated by using KNN and CART machine learning algorithms. To use real production data, this study was executed in the body production assembly line, which is selected as the pilot area, at TOFAŞ factory. The data set used in this research consists of the welding parameters of the current year, 2023. By running machine learning algorithms on the dataset, the performance evaluation of each algorithm was examined and the most appropriate estimation method was determined. In the experiments, the best F1-Score value was obtained by the CART model with 93%.

Field and Numerical Study of the Bearing Capacity of Pre-Stressed High-Strength Concrete (PHC)-Pipe-Pile-Reinforced Soft Soil Foundations with Tie Beams

Pre-stressed high-strength concrete pipe piles (PHC pipe piles) have been widely used in actual soft foundation treatment projects due to their reliable quality, fast construction, assembly line production, and environmental friendliness. However, large-scale slip damage still occurs in construction projects. In order to reduce and avoid such accidents, a highway in Guangdong (section K31+100~K31+388) was taken as an example for this study. Plaxis 2D software (V22.01.00) was used to establish a PHC pipe pile composite roadbed model and investigate the effects of tie beam form, pile lengths, pile spacings, pile verticality, and embankment filling loading modes on the settlement and stability of the composite roadbed. The results show that the original treatment plan, which had the form of a PHC pipe pile with caps, had a low horizontal bearing capacity and a poor anti-disturbance ability, leading to the occurrence of a landslide accident. A comparison of different structural forms revealed that the longitudinal and transverse tie beam form was the most stable, followed by the transverse tie beam form, longitudinal tie beam form, PHC pipe pile form with caps, and PHC pipe pile form without caps. Compared to the structural form of PHC pipe piles with pile caps, the stabilities of the transverse tie beam form and the longitudinal tie beam form were improved by 42.47% and 38.61%, respectively, while that of the longitudinal and transverse tie beam form was improved by 50.87%. The application of longitudinal and transverse tie beams effectively reduced the settlement of the composite roadbed, as confirmed by both measured data and finite element analysis. This structure achieved the desired vertical settlement control and lateral anti-slip effects.

Enhancing static load test of pipe pile composite foundation with innovative elastic cushioning technology

Pipe piles have been widely used in composite foundation treatment by virtue of the advantages of assembly line production, easy control of pile quality, fast construction, high bearing capacity of single pile and green environmental protection. By introducing elastic cushion in the static load test method of pipe pile composite foundation, and using theoretical analysis and field test to study the change rule of settlement, pile-soil stress ratio and pile top stress of pipe pile composite foundation with different cushion stiffness. It is concluded that it is feasible to replace the sand cushion layer recommended by the current specification with elastic cushion layer. The study shows that the elastic cushion method of static load test of pipe pile composite foundation is closer to the bearing state of real working condition. The elastic cushion has better pile-soil stress adjustment effect, which is more responsive to the stress adjustment effect of actual engineering cushion, and the elastic cushion provides an improved method for the static load test method of composite foundation.

A multi-objective complex product assembly scheduling problem considering transport time and worker competencies

The hybrid flow shop scheduling problem (HFSP) is a research hotspot in the shop scheduling problem. However, few studies have investigated HFSP involving parallel machine scheduling and worker competencies. Therefore, this study investigates a complex product assembly line scheduling problem considering parallel team scheduling, worker competencies, and transport time. The makespan, transport time, imbalance degree of team workload, and imbalance degree of cycle time are taken as optimization objectives. First, a mathematical model considering transport time and worker competencies is developed, and then a mixed encoding and decoding method is proposed. Second, an algorithm that hybridizes the genetic algorithm and simulated annealing algorithm is proposed, involving an improved Nawaz-Enscore-Ham heuristic method and a strengthened elite retention strategy to improve the quality of the solution. Finally, based on nine examples generated from real production statuses, the proposed algorithm is compared with the other four algorithms. The results show that the proposed algorithm is superior in terms of the quality and efficiency of solutions.

Revitalizing Quality Control Circle Program In A Hibernate Line Production PT.Toyota Boshoku Indonesia

In an effort to increase customer trust PT TBINA regularly conducts QCC activities to analyze problems that exist in the field. However, in its application, this activity is not easy to carry out in each department, for the example is the Seat Assembly which sends an average of 1 group to represent 145 employees in the TBINA internal competition for the 2017-2019 period. The number is the same as the Needle Punch line but only consisting of 12 members, different with the Welding line which can sent 12 groups of 122 members.. This study aims to increase QCC activity in the Seat Assembly line. The method used in this study is the QCC analysis method. Revitalization is carried out through a leadership approach by conducting guidance and training both for employees and for leaders, grouping employees based on age and years of service, setting activity targets, making repair and improvement plans as well as evaluating and standardizing.After the QCC activity was carried out, there was an increase in operator knowledge of QCC, this encouraged active activities which can be seen in the QCC competition in 2023 where the line seat assembly can send 9 groups to represent 100 employees. QCC also has a positive impact on the achievement of work quality and productivity in the seat assembly production line.

Design Method for Automatic Assembly Production Line of Electric Valves in Space Propulsion Systems

This article proposes a design method for a valve automatic assembly production line in response to the automation assembly requirements of electric valve products in space propulsion systems and the engineering problems of inaccurate loading force control and low valve measurement accuracy in existing process methods. This method can achieve five assembly processes during the assembly process of electric valves, including pre-tightening force control, valve-core stroke measurement, performance testing, and shell structure welding. The article introduces the design of platform components such as process execution, positioning, and transportation, as well as the design and operation process of workstations. By combining the design of a three-axis motion mechanism, a small turntable, and a robotic arm, the product can achieve professional, positioning, full process automation, and equipment miniaturization design across multiple workstations. Through the design of precise control of loading force and non-contact optical measurement method of moving structure, compared with the original method, the parameters affecting product performance are precisely controlled and the precision is improved. And the multivariable decoupling of valve product performance is realized by this method. Through application verification, this automatic assembly production line can significantly improve the assembly efficiency of electric valve products and solve difficult problems in product engineering.

Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning

Winter jujube (Ziziphus jujuba Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known to be time-consuming and labor-intensive, resulting in significant difficulties for winter jujube resource management. The applications of deep learning in this regard will help manufacturers and orchard workers quickly identify fundamental quality information. In our study, the best fundamental quality of winter jujube from the correlation between maturity and fundamental quality was determined by testing three simple physicochemical indexes: total soluble solids (TSS), total acid (TA) and puncture force of fruit at five maturity stages which classified by the color and appearance. The results showed that the fully red fruits (the 4th grade) had the optimal eating quality parameter. Additionally, five different maturity grades of winter jujube were photographed as datasets and used the ResNet-50 model and the iResNet-50 model for training. And the iResNet-50 model was improved to overlap double residuals in the first Main Stage, with an accuracy of 98.35%, a precision of 98.40%, a recall of 98.35%, and a F1 score of 98.36%, which provided an important basis for automatic fundamental quality detection of winter jujube. This study provided ideas for fundamental quality classification of winter jujube during harvesting, fundamental quality screening of winter jujube in assembly line production, and real-time monitoring of winter jujube during transportation and storage.

Digital twin for a robot assembly line: Architecture and implementation

As an enabling technology and means to practice the concept of intelligent manufacturing, digital twin has attracted extensive attention from both industry and academia. In view of the lack of concrete implementation methods of digital twin robot assembly lines, and the complex implementation process, and long development cycle using existing methods, the implementation of digital twin robot assembly lines is a significant research problem. In view of this, this paper proposes an architecture as well as an implementation method of a digital twin system for a robot assembly line. Taking a robot assembly production line as an example, a digital twin architecture and modular development approach are proposed, and three key technologies for implementing the digital twin system, namely virtual model construction, real-time data acquisition and accurate virtual-real mapping, are elaborated. The architecture and implementation method can quickly and effectively facilitate building a digital twin system of the robot assembly line, and realizing real-time monitoring and intelligent analysis of all elements of the physical robot production line.

Choosing the optimal balance for the production assembly lines at the Volvo car manufacturing plant in Babylon

Choosing the optimal balance for the production assembly lines at the Volvo car manufacturing plant in Babylon