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AbstractThe automation and assembly lines of factories have faced the need for reliable handling of porous, fragile, and soft objects. Current soft grippers have difficulty robustly grasping flat, flexible surfaces without distorting them. This paper proposes a soft gripper based on annulus microwedge adhesive for grasping flat objects using negative pressure actuation mechanisms, which can achieve controllable adhesives to reliably grasp and easily release smooth flat objects. This is achieved through negative pressure actuation, which provides a centripetal load to the annular microwedge adhesives for strong attachment, by returning to normal pressure, the centripetal load is rapidly removed, allowing for easy detachment from the adhesive surface. Additionally, the relationship between the geometrical parameters of the deformation chamber and the contact area of annulus microwedges is investigated by using finite-element method models and experiments. The results show that the wall thickness is a key parameter in designing the deformation chamber. The results of the payload test have determined the geometrical and pressure parameters of the deformation chamber with the best adhesion performance. Finally, a demonstration shows that the gripper can handle various objects for potential applications, such as in clean environmental industries. [ABSTRACT FROM AUTHOR]

This paper deals with a scheduling problem arising at the tactical decision level in aeronautical assembly line. It has the structure of a challenging multi-mode resource-constrained project scheduling problem with incompatibility constraints, a resource leveling objective and also a large number of tasks. We first present a new event-based mixed-integer linear programming formulation and a standard constraint programming formulation of the problem. A large-neighborhood search approach based on the constraint programming model and tailored to the resource leveling objective is proposed. The approaches are tested and compared using industrial instances, yielding significant improvement compared to the heuristic currently used by the company. Moreover, the large-neighborhood search method significantly improves the method proposed in the literature on a related multi-mode resource investment problem when short CPU times are required. [ABSTRACT FROM AUTHOR]

In recent years, the automotive industry has witnessed the increasing adoption of mixed-model assembly lines (MMALs) to meet the demands of mass customization. However, the diverse components of various end products are posing significant challenges to the material feeding processes in MMALs. To address this issue and enhance the material feeding efficiency, this paper proposes an innovative automatic monorail shuttle system (AMSS). The AMSS is designed based on the layout of line-integrated supermarkets and incorporates the load-exchangeable shuttles and crossovers. Considering the importance of cost control in manufacturing enterprises, the objective of this paper is to minimize the total costs associated with the material feeding system, which includes the installation costs of crossovers, as well as the input and operation costs of load-exchangeable shuttles. A mathematical model is thereby established to describe the problem, and its accuracy is validated through the Gurobi solver. Recognizing the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper-heuristic (SBHH) algorithm is developed to allocate and schedule the shuttles and crossovers, which adopts the shuffled frog leaping algorithm as the high-level heuristic for selecting low-level heuristics. To further improve the performance, dynamic decision unit is introduced to raise the solution accuracy and the convergence speed. Simulation results verify the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other benchmark optimization algorithms. Managerial applications indicate the effectiveness and practicality of the proposed approach, offering implications for practitioners in the automotive industry. [ABSTRACT FROM AUTHOR]

The pulp and paper industry, which is closely related to national economic and social development, is an important industry but also contributes high carbon emissions. Therefore, with the advent of the low-carbon economic era, ways to reduce the carbon emissions and to bring about a low-carbon industrial transition of the pulp and paper industry is becoming one of the important academic projects. A system for carbon footprint assessment, namely the Publicly Available Specification (PAS) 2050 methodology, is introduced in this paper. Based on the analysis and assessment of the carbon footprint (CO2 equivalent emissions) for the Coated Ivory Board production lines, it was used to provide a scientific basis and approach for reduction of carbon emissions and formulate the corresponding measures for carbon emissions reduction of China's pulp and paper industry. The business to business carbon footprint, for which steps of the life cycle are included in Coated Ivory Board production, was analyzed and calculated. The results showed that there were 888 kg of CO2 equivalent emissions per metric ton of Coated Ivory Board, in which the largest part, accounting for 57.5%, was associated with purchased electricity, followed by fuel oil at 40.2%, and others accounted for 2.3% of the CO2 equivalent emissions. [ABSTRACT FROM AUTHOR]

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings. [ABSTRACT FROM AUTHOR]

In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines. [ABSTRACT FROM AUTHOR]

Mixed and multi-model assembly line sequencing problems are more practical than single-product models. The methods and selection criteria used must keep up with the constantly increasing level of variability, synchronize flows between various—often very energy-intensive production departments—and cope with high dynamics resulting from interrupted supply chains. The requirements for conscious use of Earth's limited natural resources and the need to limit energy consumption and interference in the environment force the inclusion of additional evaluation criteria focusing on the environmental aspect in optimization models. Effective sustainable solutions take into account productivity, timeliness, flow synchronization, and the reduction of energy consumption. In the paper, the problem of determining the sequence of vehicles for a selected class of multi-version assembly lines, in which the order restrictions were determined taking into account the above criteria, is presented. Original value of the paper is the development of the Grey Wolf Optimizer (GWO) for the mixed-model assembly lines sequencing problem. In the paper, a comparative analysis of the greedy heuristics, Simulated Annealing and GWO for a real case study of a mixed vehicle assembly line is presented. The GWO outperforms other algorithms. Overall research performance of the GWO on the sequencing problem is effective. [ABSTRACT FROM AUTHOR]

With the growth of industrialization, the global manufacturing industry is continually evolving and reforming in the direction of intelligence and green production. Industrial robots have replaced human workers because of the benefit of production efficiency. However, the large-scale application of robots requires a large amount of energy consumption and generates a large amount of CO2, which will lead to energy waste and environmental pollution. In addition, in term of performing some particular tasks, current robot technology cannot achieve the same level of intelligence as human. Therefore, the design trend of assembly lines in industry has shifted from traditional configuration to human-robot collaboration to achieve higher productivity and flexibility. This paper investigates the human-robot collaboration (HRC) assembly line balancing problem, taking cycle time and carbon emission as primary and secondary objectives. A new mixed-integer programming model that features a cross-station design is formulated. A particle swarm algorithm (PSO) with two improvement rules is designed to solve the problems. The comparative experiments on ten benchmark datasets are conducted to assess the performance of the proposed algorithm. The experimental results indicate that the improved particle swarm algorithm is superior to the other two heuristics: simulated annealing (SA) and the late acceptance hill-climbing heuristic (LAHC). [ABSTRACT FROM AUTHOR]

Purpose: In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs). Design/methodology/approach: A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs. Findings: Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified. Originality/value: To the best of the authors' knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation. [ABSTRACT FROM AUTHOR]

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers' actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others. [ABSTRACT FROM AUTHOR]

Detecting damages on the body of the saury is important for packaging in the fisheries industry. Moreover, the appearance of a fish is important in fisheries. Fish damage detection relies on manual assembly lines, which are labor‐intensive and time‐consuming. This paper proposes a novel saury damage detection method that automatically localizes the saury body region, determines the head‐tail orientation, and detects damaged parts from the body region. We collected a saury image dataset for training and built a simple naive convolutional neural network (CNN) model without transfer learning for damage discrimination. Seven evaluation metrics, such as accuracy, F1‐score, Area under the ROC curve (AUC), and prediction time, were comprehensively considered to select the optimal parameter configuration including the number of layers, number of filters, filter size, and input image size. We also utilized the transfer learning of eight pretrained CNN models to classify the saury into two categories for damage discrimination. Inception‐v3 and ResNet‐101 achieved the highest accuracy of 98.2% for damage discrimination. Through extensive experiments and discussion of the results, this paper presents clear guidance for choosing pretrained networks for transfer learning in damage detection tasks. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Purpose: Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted. Design/methodology/approach: A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy. Findings: The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod. Originality/value: This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production. [ABSTRACT FROM AUTHOR]

Purpose: Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant. Design/methodology/approach: This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms. Findings: The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach. Originality/value: This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently. [ABSTRACT FROM AUTHOR]

In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas. [ABSTRACT FROM AUTHOR]

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods. [ABSTRACT FROM AUTHOR]

This paper introduces a web-platform system that performs semi-automatic compute of several risk indexes, based on the considered evaluation method (e.g., RULA—Rapid Upper Limb Assessment, REBA—Rapid Entire Body Assessment, OCRA—OCcupational Repetitive Action) to support ergonomics risk estimation, and provides augmented analytics to proactively improve ergonomic risk monitoring based on the characteristics of workers (e.g., age, gender), working tasks, and environment. It implements a body detection system, marker-less and low cost, based on the use of RGB cameras, which exploits the open-source deep learning model CMU (Carnegie Mellon University), from the tf-pose-estimation project, assuring worker privacy and data protection, which has been already successfully assessed in standard laboratory conditions. The paper provides a full description of the proposed platform and reports the results of validation in a real industrial case study regarding a washing machine assembly line composed by 5 workstations. A total of 15 workers have been involved. Results suggest how the proposed system is able to significantly speed up the ergonomic assessment and to predict angles and perform a RULA and OCRA analysis, with an accuracy comparable to that obtainable from a manual analysis, even under the unpredictable conditions that can be found in a real working environment. [ABSTRACT FROM AUTHOR]

An efficient part feeding is among the top challenges of many mass producers applying mixed-model assembly lines, for instance, in the automotive industry. This paper introduces a novel part feeding policy applied by a large German assembly plant for car engines: In-line kitting. Under this policy, the first stations of the line do not execute assembly operations, but are reserved for picking parts while passing containers of stock-keeping units (SKUs) arranged along the line. In this way, the parts are collected in traveling kits moving along with each workpiece on the conveyor, so that later assembly stations have the required parts directly available and do not lose precious labor time for unproductive parts handling. A major operational challenge when applying this part feeding policy is the walking effort for the human pickers while putting the SKUs of their respective stations into the traveling kits of the passing workpieces. Due to a high product variety, a large number of comparatively bulky SKU containers have to fit into each station, so that the walking distance to be covered by a worker during a work shift exceeds multiple kilometers. We show that this physical burden can be reduced significantly by balancing the workload among stations and optimizing the storage assignment of SKU containers within each in-line kitting station. We formulate the resulting optimization problem and provide suited solution procedures. Our computational study shows that the walking distance of pickers can be reduced significantly without producing any additional costs. [ABSTRACT FROM AUTHOR]

The business world is becoming more competitive. Therefore, it is crucial to increase the flexibility of production by decreasing the time used in the processes of preparing the production lines for new items' production, reducing changeover and setup times. This paper presents a case study where the main goal is to reduce the setup time of welding robots. Single Minute Exchange of Die (SMED) was implemented, using other tools such as the Spaghetti Diagram, ERCS Analysis (Eliminate, Rearrange, Combine, Simplify), Gemba Walk, Standardized Work, Flowcharts, and Pareto Diagram. The setup time decreased by 36% in the welding robots studied, decreasing the motions by 43% during the changeover process and reducing the time from the categories: "transportation", "main", "other", and "waiting". In addition to SMED implementation, this study offers an integrated study of several Lean tools and Quality tools to achieve the maximum reduction of changeover and setup times. [ABSTRACT FROM AUTHOR]

As a result of constant competitive pressure, companies are forced to constantly look for ways to produce more efficiently. To increase production efficiency and competitiveness, the actual situation needs to be analyzed and measures proposed. However, proposals, when put into practice, do not always result in improvements, due to various errors and omissions in their design. A helpful tool to minimize failures is to apply computer simulations, in which the proposed solutions are being tested and a proven solution implemented into practice afterwards. This is especially important when a proposed solution requires increased investment costs and time. This paper deals with streamlining technological processes on an assembly line of a turbocharger with an electrically controlled actuator, by automating a section of the assembly line (reduced number of operators and defective products), based on analysis and identified deficiencies. The Tecnomatix Plant Simulation software was chosen to analyze the assembly line's condition, as well as to simulate proposed measures' impact on the line's improvement. The design brings increased efficiency of a turbocharger assembly process and a reduced number of workers. According to the simulation results, when proposed improvements are applied, the annual production of the assembly line increases from 89,575 pieces to 98,139 pieces, which is 8,564 pieces more, i.e., an increase of 9.56%, while reducing the number of workers from four to three. [ABSTRACT FROM AUTHOR]

The vigorous development of the Internet of Things and information technology promotes the transformation of the manufacturing industry from traditional management to intelligent informatization. In order to discuss the intelligent production line monitoring and sensing system, this paper selects the garment mechanical parts processing production line as the object. Firstly, on the production line data collection, the characteristics and existing problems of the garment mechanical parts processing production line are analyzed. Then, the production line's awareness monitoring demand is summarized. Secondly, the production line's data sources and acquisition methods are discussed. Based on the configurable idea, a real-time data acquisition system for the garment mechanical parts processing production line is proposed. In addition, in terms of quality prediction, based on the Back Propagation Neural Network (BPNN), the Multiple Population Genetic Algorithm (MPGA) is introduced to build the MPGA-BPNNNN quality prediction algorithm. Finally, the performance of the system and algorithm is tested based on simulation experiments. The results show that the system's data acquisition and concurrent client pressures are 1.86ms and 650 users, respectively, meeting the requirements. Compared with the traditional BPNN, the MPGA-BPNNNN algorithm has a more accurate prediction result, with a root mean square error of 0.0645, which is also relatively small. The design of a perceptual data acquisition system and the construction of a quality prediction algorithm for garment mechanical parts processing production lines can provide a path for transforming traditional manufacturing production lines into intelligent digital perceptual systems. [ABSTRACT FROM AUTHOR]

With the rapid development of industry, the production and demand of automobiles have increased significantly. The generation of cars often requires tens of thousands of processes, consisting of hundreds of assembly lines to complete. Chain conveyors are widely used in the transportation of automobile assembly lines. The existing conveyor chains are roller chains. With the improvement of the requirements for conveyor reliability, synchronization, and environmental friendliness, the characteristics of roller conveyor chains restrict the further development of chain conveyors. It is urgent to study a new conveyor chain system to improve the synchronization, reliability, and environmental friendliness of chain conveyors on hundreds of assembly lines. In this paper, the mathematical modeling, meshing analysis, reliability, and environmental friendliness of the parameters of the components of the double pitch time-sharing meshing toothed conveyor chain system are studied. In addition, a novel fault diagnosis method of double pitch time-sharing meshing toothed conveyor chain transmission system based on neural network model is proposed in this paper. [ABSTRACT FROM AUTHOR]

In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and F1-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs. [ABSTRACT FROM AUTHOR]

Purpose: One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower. Design/methodology/approach: In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter. Findings: 12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems. Research limitations/implications: One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training. Originality/value: In this paper, a mathematical model was developed for multi-model assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NP-Hard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study. [ABSTRACT FROM AUTHOR]

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the most important processes in mass production. However, this process is currently impeded by the lack of feedback for the next process how to plan an excellent assembly line. In this paper, we introduce the priority graph model, develop the subassembly recognition method, and design the selection algorithm with feedback weights for assembly line design in topological sequencing. We show that the method can quickly plan a satisfactory sequence compared to heuristic algorithms. Due to the different weights from assembly line designers, satisfactory assembly sequences with different adaptability can be planned. This work has implications for co-design of assembly sequence planning and assembly line design. [ABSTRACT FROM AUTHOR]

In this paper, the authors investigate a mixed-model assembly line problem with the intention of maximizing work efficiency. There are some objectives for various points, such as minimizing work removes congestion and line stop time. In the case of minimizing line stop time, a longer cycle time compresses the stop time and improves the evaluation value. However, it increases idle time and results in lower work efficiency. In other words, a short cycle time increases the line stop time, but there is a possibility to increase work efficiency. Many studies have reported job sequence with a fixed cycle time. An assembly line system achieves higher work efficiency if the cycle time of the system can be changed. This paper focuses on an assembly line system that is able to adjust the conveyer speed to change cycle time and requires restart time after a line stop. In order to consider remove work efficiency including idle time, line stop time and restart time, the objective function is set to minimize the makespan as the length of a schedule. The objective function value is the time when the last product is completed at the final work station. The solution of this problem consists of job sequence and conveyer speed. The authors propose some methods using remove simulated annealing. The effect of the proposed methods is shown and the influence of the problem conditions on the makespan is investigated. [ABSTRACT FROM AUTHOR]

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case. [ABSTRACT FROM AUTHOR]

The European Union (EU) promotes arms collaboration as a stepping-stone towards the evolution of an integrated European defence technology and industrial base. It will necessarily comprise prime contractors and their attendant supply chains, with the latter particularly important because they represent a refined regional division of labour, promoting efficiencies through skill-based specialisation. Paradoxically, however, Europe's largest military aerospace collaborative venture, the Eurofighter Typhoon, possesses a complex supply chain subject to political and institutional strictures, as well as potential inefficiencies. Partner nations prioritise national sovereignty objectives through duplicated assembly lines and work allocation arrangements based on juste retour (fair share) rather than market-driven competitiveness criteria. The purpose of this paper, then, is to explore Typhoon's supply chain complexity, especially the impact of juste retour policy. The findings from this analysis will highlight important policy issues influencing the future supply chain model of Europe's successor 6th-Generation fighter programme. [ABSTRACT FROM AUTHOR]

Assembly lines (conveyors) are traditional means of large-scale and mass-scale productions. An assembly line balancing problem is needed for optimizing the assembly process by configuring and designing an assembly line for the same or similar types of final products. This problem consists of designing the assembly line and distributing the total workload for manufacturing each unit of the fixed product to be assembled among the ordered workstations along the constructed assembly line. The assembly line balancing research is focused mainly on simple assembly line balancing problems, which are restricted by a set of conditions making a considered assembly line ideal for research. A lot of published research has been carried out in order to describe and solve (usually heuristically) more realistic generalized assembly line balancing problems. Assembly line designing, balancing and scheduling problems with not deterministic (stochastic, fuzzy or uncertain) parameters have been investigated in many published research works. This paper is about the design and optimization methods for assembly and disassembly lines. We survey the recent developments for designing, balancing and scheduling assembly (disassembly) lines. New formulations of simple assembly line balancing problems are presented in order to take into account modifications and uncertainties characterized by real assembly productions. [ABSTRACT FROM AUTHOR]

Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL. [ABSTRACT FROM AUTHOR]

Acoustic lining assemblies with millions of high-density array holes are key structures for high-performance and low-noise aero-engines. However, the material specificity, variable curvature shape structure, high-order number of holes, and small-sized hole spacing of acoustic lining assemblies pose great challenges to the hole-making technology. In this paper, in view of the machining requirements of millions of acoustic lining holes, a hole matrix unit design method for batch hole making is proposed. The optimal hole matrix unit dimension is calculated with the angle deviation of normal vector as the constraint by matching the structural features of the acoustic lining assembly, dividing and interpolating the hole-making surface, and solving the normal vector of the hole position. The experimental results show that the proposed hole matrix unit design method can effectively control the angle deviation of normal vector of the acoustic lining holes and ensure the processing accuracy of the holes. [ABSTRACT FROM AUTHOR]

Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the volume of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is no longer possible effectively. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of product type changes on the Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree technology, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice. [ABSTRACT FROM AUTHOR]

This scientific paper emphasizes the paradigm of Industry 4.0 from the perspective of cybersecurity, in which if there are digitalized factories and a digital chain connected to them, there is also the need of an amplified security system, and the usual IT security procedures are not enough to protect the production lines. This article treats general concepts from IT security’s perspective, as well as OT’s security, and also about the joining of those two into a holistic approach combining people, processes and technology to properly defend against threats and build a security baseline. Digitization matter and analysis of industrial system evolution is taken into consideration in this paperwork, correlating them with cybersecurity. Ever evolving environment of technology influences the growth and diversity in the complexity of threats and security risks, such as cyberattacks, and related works in the IT domain gives new security challenges. [ABSTRACT FROM AUTHOR]

In the process of detecting materials inside a tray in an automated production line, it is necessary to detect not only the known materials and blank space in the designated area, but also the unknown materials misplaced inside the tray. However, the supervised detection algorithm based on deep learning can only detect the known and blank material areas. Therefore, this paper proposed a phased material detection. The first stage is to detect the tray and then identify the material area in the second stage. In order to improve the tray detection accuracy during the first stage under the condition of a high intersection ratio, an improved YOLOv5s tray detection method is proposed. The structure of YOLOv5s is improved using the SENet. This paper proposes to use the rich geometric information of the shallow network and the high‐level semantic information to integrate the bypass features. MAP@0.5:0.95 of the improved model increased from 95.7% to 96.6% and MAP@0.95 from 78.5% to 90.8%. The challenge of detecting unknown wrong materials on a tray can be resolved through the recognition of material area segmentation images processed by using the improved pre‐detection algorithm, together with the relative position reference between the material area and the tray. The experimental results showed that the improved method proposed meets the industrial detection requirements with an overall recognition accuracy of 91% within a 250 ms detection interval. [ABSTRACT FROM AUTHOR]

Since greening scheduling problems are drawing increasing attention from researchers and modern manufacturing enterprises, and the energy consumption is a substantial problem regarding the greening and sustainability, the aim of this paper is to construct an energy-saving scheduling scheme to carry out the part feeding tasks of mobile robots in the automobile mixed model assembly lines. The objective of minimizing the total energy consumption of mobile robots is jointly incorporated with the operational criterions when implementing part feeding tasks. Due to the NP-hardness nature of the proposed greening problem, a multi-objective disturbance and repair strategy enhanced cohort intelligence (MDRCI) algorithm is established to deal with the multi-objective problem. Computational results indicate that the enhanced strategies are of great significance to the MDRCI algorithm and it outperforms the other benchmark algorithms on both global search capability and search depth. In addition, the energy-saving strategy and disturbance and repair strategy are validated by comparison experiments. Furthermore, managerial insights are illustrated to make trade-offs between the total line-side inventory level and the energy consumption, jointly making it helpful in the greening scheduling process of the practical production. The achievements acquired in this paper may be inspiring for further researches on the energy-related production scheduling problem. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) offers a promising set of technologies to digitally simulate industrial processes and interaction between humans and machines. However, the use of immersive VR simulations is still limited in industry due to the uncertainty of benefits in respect with traditional digital tools, and the lack of structured methodologies to effectively implement immersive virtual simulations in practice. This paper deals with the application of VR to create virtual manufacturing simulations with the aim to design assembly lines in compliance with factory ergonomics. It proposes a methodology to allow the virtualization and simulation of assembly tasks using a combination of VR tools by replicating, or rather anticipating, what would happen at the shop floor. The adopted tools are Unity 3D for virtual environment generation, HTC VIVE to immerse the user in the virtual factory layout, Xsens as tracking system, and Leap Motion for gesture recognition. The paper also compares the new VR-based procedure with a more traditional desktop-based digital simulation on industrial cases. Results show that the new methodology is more precise to detect the operator's comfort angles and more powerful to predict process criticalities and optimize factory layout design. At the same time, it is less sensitive to errors during ergonomic assessment related to the expert's subjectivity during the analysis. [ABSTRACT FROM AUTHOR]

Gesture recognition is gaining popularity in many fields, including gesture control, robotics, or medical applications. However, the technology is barely used in industrial manufacturing processes due to high costs, a time-consuming configuration, and changes in the workflow. This paper proposes a minimally invasive approach to recognize workers' hand motions in manual assembly tasks. The novelty of this approach is the use of only one camera instead of any other sensors and the application of state-of-the-art graph neural networks. The method relies on monocular RGB video data to predict the basic motions of the industry standard motion-time system Methods-Time Measurement-1. Our two-stage neural network composed of hand key point extraction and adaptive graph convolution delivers accurate classification results in real-time. To train and validate the model, we created a dataset containing 22,000 frames of real-world assembly tasks. The data produced by this method in a production line can be used for motion time verification, assembly-line design, or assembly cost estimation. In a use-case study, we show that the proposed approach can generate Methods-Time Measurement analysis tables. These have so far only been accurately created by human experts. Source code: https://github.com/alexriedel1/Hand-Gesture-Recognition-in-manual-assembly-tasks-using-GCN [ABSTRACT FROM AUTHOR]