Flexible Manufacturing Systems Research Articles

From Nash Q-learning to nash-MADDPG: Advancements in multiagent control for multiproduct flexible manufacturing systems

The emergence of flexible manufacturing systems (FMS) capable of processing multiple product types is a result of the growing demand for product customization and personalization. Such multiproduct systems are characterized by a higher level of uncertainty and variability when compared to traditional manufacturing systems. This paper proposes a Nash integrated multiagent deep deterministic policy gradient method (Nash-MADDPG) to control the mobile robots’ assignment in a multiproduct FMS to enable intelligent decision-making, interaction, and dynamic learning capabilities. A mathematical model of a multiproduct FMS from a previous study is described, and system dynamic property is characterized by permanent production loss (PPL). Then, by observing PPL of the system and market demand for each product type, the multi-agent control scheme is developed to assign mobile robots to load/unload various product types at various machines. First, a Nash game is developed among the mobile robots and to improve the cooperation, a collaboration cost is defined. This collaboration cost is then used in the reward function of the multiagent deep deterministic policy gradient (MADDPG) algorithm. Second, the actions are jointly defined based on the action values of MADDPG, and the mobile robots’ strategies in the Nash game, which update the environment to a new state. The performance of the proposed method is verified by comparing it with conventional Nash Q-learning, vanilla MADDPG, Q-learning based single agent reinforcement learning (SARL) and a first-come-first-serve (FCFS) based control. The results demonstrate that the multi-agent control scheme under the proposed Nash-MADDPG is effective in dealing with cooperative FMS environment that involves complicated dynamics and uncertainties.

Investigating the Efficacy of a Virtual Reality-Based Testing Station of Flexible Manufacturing System: A Usability and Heuristic Evaluation

This study presents a comprehensive evaluation of a virtual reality-based testing station designed for flexible manufacturing systems. Given the intricate nature of flexible manufacturing systems and the demand for precision in learning, the integration of virtual reality emerges as a promising approach to enhance both student competence and engagement. By employing a combined assessment with the System Usability Scale and heuristic evaluation conducted by 36 students and 5 experts, respectively, the virtual reality-based testing station achieved an average usability score of 72.78, indicating good usability. Noteworthy heuristic challenges, particularly in the domains of ‘Realistic Feedback’ and ‘Navigation and Orientation Support,’ have been identified, providing valuable insights for potential refinements to the testing station. The outcomes of this study not only guide immediate improvements but also pave the way for future research endeavors aimed at elevating the learning outcomes in flexible manufacturing systems courses.

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

Variability propagation in manufacturing systems: the impact of the processing time distribution

ABSTRACT In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration.

Development of flexible manufacturing system

The focus of this project is the development and analysis of a Flexible Manufacturing System (FMS) for the production of a range of tables. Tables are typically used in homes and offices, where different variants are selected for different environments and uses. By implementing a flexible manufacturing system, different versions of tables can be produced in the same factory to fulfil different customer needs and preferences. This project designed and evaluated four different variants in the FMS, demonstrating aesthetic and functional differences while maintaining core similarities; this demonstrates FMSs flexibility and adaptability and provides insights into the trade-offs between design choices and manufacturing considerations. The article uses Mejabi and Solberg analysis for detailed FMS design and analysis. This analytical approach allows for thoroughly examining the FMS design, including factors such as productivity, efficiency and cost-effectiveness. The analysis further helps understand and optimise the implementation of the FMS to ensure that it effectively meets the production goals. This paper paves the way for enhancing productivity, increasing product range and improving customer satisfaction in the furniture manufacturing industry.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

Design methodology for the control logic of flexible production systems and related logistics

The ever-shorter product life cycle entails a shortening of the life cycle of the production site and production systems as well. One of the biggest advantages of flexible production systems is that new products can be introduced to them continuously, so that their life cycle can be significantly extended. Flexible manufacturing systems, on the other hand, also require flexible control, the logic of which must be defined already in the design phase. The main elements of the design methodology of the production and logistics layout that make up the research are the information relationship chart and the related decision tables, which are suitable for speeding up the design and later for facilitating the creation of a digital twin and control logic programming.

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems

AbstractIn the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees’ nodes represent the independent variables, while the trees’ leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach.

Reliability evaluation of flexible manufacturing systems based on three-layer index system with time-varying weights

With the development of industrial manufacturing, flexible manufacturing system provides an effective way to improve the machining quality and efficiency, as well as reduce the manufacturing cost. Fflexible manufacturing system reliability has been one focus of recent studies. But a systematic, effective and executable reliability evaluation method has not yet proposed in existing researches. This work firstly presented a three-layer index system for flexible manufacturing system reliability evaluation, which is composed of the whole layer indexes, subsystem layer indexes and equipment layer indexes determined by system composition and running features analysis. Time-varying weights of each layer are calculated through the modelling of the reliability and reliability importance based on the classical two-state failure hypothesis. Then, considering multi-state failure modes of elements, a fuzzy evaluation model of flexible manufacturing system reliability is established based on fault grading and statistics, considering grade and number distributions of faults within a certain statistical period. Moreover, through a simulation case of a box-parts finishing flexible manufacturing system, the influence of failure rates and service time on the index weights distribution is firstly revealed, which can provide theoretical basis for the improvement of flexible manufacturing system reliability; then the effectiveness of the proposed method is verified through the comparison of results obtained by the previous model based on two-state failure and the proposed model based on multi-state failure.

Multi-camera tracking of mechanically thrown objects for automated in-plant logistics by cognitive robots in Industry 4.0

AbstractEmploying cognitive robots, capable of throwing and catching, is a strategy aimed at expediting the logistics process within Industry 4.0’s smart manufacturing plants, specifically for the transportation of small-sized manufacturing parts. Since the flight of mechanically thrown objects is inherently unpredictable, it is crucial for the catching robot to observe the initial trajectory with utmost precision and intelligently forecast the final catching position to ensure accurate real-time grasping. This study utilizes multi-camera tracking to monitor mechanically thrown objects. It involves the creation of a 3D simulation that facilitates controlled mechanical throwing of objects within the internal logistics environment of Industry 4.0. The developed simulation empowers users to define the attributes of the thrown object and capture its trajectory using a simulated pinhole camera, which can be positioned at any desired location and orientation within the in-plant logistics environment of flexible manufacturing systems. The simulation facilitated ample experimentation to be conducted for determining the optimal camera positions for accurately observing the 3D interception positions of a flying object based on its apparent size on the camera’s sensor plane. Subsequently, a variety of calibrated multi-camera setups were experimented while placing cameras at identified optimal positions. Based on the obtained results, the most effective multi-camera configuration setup is derived. Finally, a training dataset is prepared for 3000 simulated throwing experiments where the initial part of the trajectory consists of observed interception positions, through derived best multi-camera setup, and the final part consists of actual positions. The encoder–decoder Bi-LSTM deep neural network is proposed and trained on this dataset. The trained model outperformed the current state-of-the-art by accurately predicting the final 3D catching point, achieving a mean average error of 5 mm and a root-mean-square error of 7 mm in 200 real-world test experiments.

An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.

Adaptive fuzzy-genetic algorithm operators for solving mobile robot scheduling problem in job-shop FMS environment

Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality.

Vision based trajectory dynamic compensation system of industrial robot

This study aims to enhance the flexibility of robots in the manufacturing process to meet the growing demand for flexible manufacturing systems by constructing a dynamic compensation system. The system enables the end effector of the robot to track the trajectory contour on the horizontal plane, even if there is a deviation between the teaching trajectory and the target trajectory contour. The proposed system employs an innovative mechanism design and algorithm that enables dynamic compensation of the robot’s absolute position in the XY-plane, even when the end effector is obstructed. The system uses an XY-stage and two industrial cameras, with each axis being separately controlled. To detect and compensate for errors in the end effector and target trajectory, the two cameras are aligned with the X- and Y-direction linear stage, respectively. The cameras are fixed with respect to the linear stage, and the end effector is placed on the stage work surface. Unlike traditional stereo cameras, the cameras in this system do not require calibration for their positional relationship. To verify the system’s effectiveness, this study set a group of teaching and target trajectory contours with a maximum deviation of over than 8 mm. Under the compensation of the system, the maximum error between the end effector and the target trajectory contour was 22.8 pixels (1.34 mm), with an average absolute error of 5.4 pixels (0.32 mm). Compared to the maximum error of 8.45 mm before compensation, the error has been reduced by 84 %.

Mathematical Simulation of Isotope Exchange in Flexible Manufacturing Systems with Electrochemical Phase Flux Reversal

Mathematical Simulation of Isotope Exchange in Flexible Manufacturing Systems with Electrochemical Phase Flux Reversal

Exploiting symmetry for the job sequencing and tool switching problem

The Job Sequencing and Tool Switching Problem (SSP), a well-known combinatorial optimization problem in the domain of Flexible Manufacturing Systems (FMS), is studied in this article. The aim of this research is to improve the currently known exact solution methodology for this NP-hard problem. We propose a new integer linear programming approach with symmetry-breaking and tightening cuts that provably outperformed the existing methodology described in the literature. The computational study conducted on a data set with 1,050 instances showed that the developed method can solve hard instances that were not solvable with the state-of-the-art methods presented in the literature.

Correction to: Data‑driven prioritization of performance variables for flexible manufacturing systems: revealing key metrics with the best–worst method

Correction to: Data‑driven prioritization of performance variables for flexible manufacturing systems: revealing key metrics with the best–worst method

Reorganization of the manufacturing system of engineering enterprises: the emergency period

The manufacturing system of the majority of domestic engineering enterprises is built on the basis of long-term contracts with suppliers of the materials and tools, and currently is in an unstable condition. This determines the acuteness of the problem of reorganization of the manufacturing system. To tackle this problem the authors solved the following tasks. The enterprises were classified into groups which possess specific opportunities to adapt to the external environment. The authors identified the basic prerequisites for reorganization of the national manufacturing system among which of great importance are external financial and logistical restrictions, greatly increased demand of the national economy in various complicated technical facilities and spare parts, and the general instability. Withdrawal of foreign investors from the Russian market has created conditions to expand the market share of the domestic companies and increase their output. The authors suppose that the projects on the reorganization of the domestic enterprises of the engineering complex should be based on flexible manufacturing systems capable of integrating into production lines and responding promptly to external influences.

Group technology in flexible manufacturing

Current engineering production is characterised by rapid changes. These changes arise due to the shortening of product life cycles. Manufacturers are forced to introduce upgraded products at shorter intervals based on customer requirements. In this paper, we focus on the possibilities of production upgrading by using a group technology in the flexible manufacturing system. The production capabilities of one of the production machines in the manufacturing system will be analysed. This production machine is a lathe that produces one of the essential parts of a finished product. Determine what variations of this part can be produced without changing the physical configuration of the machine.

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems.