Control Of 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.

ARJUNA: An Electrochemical Interface Mapping Probe for Solid-State Batteries

Solid-state batteries (SSBs) are promising candidates for next-generation energy storage, although their performance can be compromised by interfacial heterogeneity within the electrolyte. Furthermore, ensuring the quality of large form-factors electrolyte film is crucial for establishing a robust manufacturing platform for solid-state batteries. Herein, we report on the use of ARJUNA, an electrochemical interface mapping system, to characterize heterogeneities at solid electrolyte interfaces and to serve as a quality control system for SSB manufacturing. In addition to spatial mapping, the proposed system can also probe the interface behavior as a function of pressure and temperature. We present the operating principle, design, instrumentation, and evaluation of the system alongside a typical hybrid solid electrolyte produced using two common manufacturing processes. This report showcases the capability of ARJUNA to probe the heterogeneity and quality of processed solid electrolyte films.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems.

A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning

Short-term energy consumption forecasting is the foundation of anomaly detection, scheduling and energy-saving control in manufacturing system. Existing methods do not reasonably distinguish between energy consumption nodes, but rather adopt overall prediction, resulting in poor prediction of target nodes. Consequently, this paper proposes a short-term energy consumption prediction method for the Attention Mechanism based on spatio-temporal deep learning (ST-Attention). Convolutional Neural Network (CNN) is utilized to extract feature extraction to acquire local high-dimensional features; Local long-term trend features are extracted using Gate Recurrent Unit (GRU) to reduce the inaccuracy of prediction due to the mixing of raw data;Finally, the Attention Mechanism is utilized to dynamically assign the weight of energy consumption nodes attribute to effectively deals with the difference, anisotropy and correlation between energy consumption nodes. The attention mechanism fuses extracted temporal and spatial features through weight assignment to enable short-term energy consumption prediction for manufacturing systems. The actual data collected from the aluminum profile factory is used to verify that the ST-Attention model providing better performance for short-term energy consumption prediction of the manufacturing system.

Game Theory Based Dynamic Event-Driven Service Scheduling in Cloud Manufacturing

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

Time Series Manufacturing Data Edge Monitoring and Visualization to Support Industrial Maintenance Teams

Traditional manufacturing control systems such as Manufacturing Execution Systems (MES) or SCADA (Supervisory Control And Data Acquisition) were not designed for Industry 4.0 paradigm. Industry 4.0 implies that more data variables must be automatically monitored and data must be captured at a higher frequency: from one value of a few key variables to values of several variables captured at frequencies of seconds. Thus, new architectures and tools are required to merge Information Technology (IT) and Operation Technology (OT) fields and to meet Industry 4.0 requirements. This paper proposes a lightweight architecture based on micro-services and time series data requirements to connect to manufacturing process controllers, and to capture, store, monitor and visualize relevant data about the process. Moreover, a reference implementation based on Open Source tools is presented. This implementation has been validated by members of the maintenance team of a factory from Lecta, a paper manufacturer. The implementation has proven to be a new valuable tool providing further insights and customized alarms of the manufacturing process.

Radial projection-based adaptive sampling strategies for surrogate-assisted many-objective optimization

Intelligent manufacturing and industrial control systems frequently encounter expensive many-objective optimization problems (EMaOPs). Surrogate-assisted evolutionary algorithms (SAEAs) build predictive models to substitute the expensive fitness evaluation, enabling them to solve optimization more efficiently. In SAEAs, to enhance the exploration of optimization and the generalization of the surrogate model, the diversity of infill offspring and training database should be well-maintained, which is challenging in high-dimensional spaces or problems with disconnected Pareto front. This paper suggests a radial projection-based surrogate-assisted framework for solving EMaOPs. The radial projection can map the high-dimensional objective space into a 2-dimensional radial space. Based on this, a dynamic quadratic division method is proposed to enhance the diversity of solutions. Furthermore, an adaptive infill sampling criterion is introduced based on the distribution of selected convergent solutions, and a training database updating strategy is designed under the premise of maintaining its diversity and the model training efficiency. The presented framework exhibits a notable level of flexibility and adaptability as it can be effortlessly combined with other multi-objective optimization algorithms. Several experimental results on a set of expensive multi/many-objective test problems have demonstrated the superiority of the framework.

Agent-based Decision Making and Control of Manufacturing System Considering the Joint Production, Maintenance, and Quality by Reinforcement Learning

Taking an integrated approach towards production, maintenance, and control in manufacturing systems is crucial due to the profound impact of their interconnections. Investigating these aspects in isolation may lead to infeasible solutions. This research focuses on the real-time and autonomous decision-making process concerning joint production planning, maintenance, and quality problems in a stochastic deteriorating production system with limited maintenance activities. Formulating the problem as a continuous semi-Markov decision process accounts for the complexities of the real production system and the occurrence of events over an uneven and continuous period. While dynamic programming is a common tool for addressing joint optimization problems, it has limitations, such as the curse of dimensionality. In this study, the optimal policy of the decision-maker agent is obtained by the goal-directed machine learning method called (R-SMART) and agent-based modeling. To the author's knowledge, the proposed approach is novel, and there is little research on such an implementation of the joint optimization problem. The quality of the optimal policy is evaluated through heuristic and simulation-optimization methods in various scenarios. The results demonstrate that the proposed RL-based method outperforms others in most scenarios, achieving a stable, integrated optimal policy.

Optimal Management of Reconfigurable Manufacturing Systems

The market demands more and more varied and personalized products and services. The work aims to improve the conceptual control of reconfigurable manufacturing systems, available to companies that work on the basis of orders, generically called "make-to-order" MTO, companies that require the most efficient reconfigurable manufacturing systems. Also, increasing the economic performance of these reconfigurable manufacturing systems, already in operation, is a goal of this work.

Integrated Control of the Manufacturing System

In this paper, we propose a method to control the entire production process, starting with customer enquiry up to product delivery, for the make-to-order (MTO) manufacturing system. Method for integrated control of the job shop type manufacturing system proposed in this paper aims to facilitate the connection between the two departments and to achieve integrated control of job shop type manufacturing system on the basis of Earning Power (EP) evaluation. It gives a more accurate picture of a firm's profitability than gross income. The purpose of this paper is to demonstrate the ability of the proposed method on a real case and to illustrate the applicability of mathematical models that were proposed. The main problems for a MTO company manager, i.e. problems related to order acceptance and machine control are solved by the new integrated control method, which is included in this paper.

Li-Ion Battery Electrode Manufacturing Control System in Winding Process: Tension Control in Industrial Complex Roll-to-Roll Winding Machine Via SMC-FLC Hybrid Control Method

Abstract This paper introduces a new control method for web tension control on a complex roll-to-roll winding machine used in battery production. A traditional web tension control method cannot perform well enough under high winding speed: the parameter tuning process is time-consuming, the disturbance rejection performance is not satisfying, and the control performance is not stable. A hybrid control method is proposed, and it is easy to be implemented on common programming platform for commercial winding machines with an easy tuning process, while providing superior control performance to the traditional control method. The system modeling used in the control method is much simpler than the modeling in most of the tension control researches, providing better feasibility for industrial application.

Reinforcement learning for energy-efficient control of parallel and identical machines

Nowadays, the growing interest in industry for enhancing the sustainability of manufacturing processes is becoming a major trend. Energy consumption can be lowered by controlling machine states with energy-efficient control policies that switch off/on the device. Recent studies have shown that Reinforcement Learning algorithms can effectively control manufacturing systems without the requirement of prior knowledge about system parameters. This is a significant factor since full information on system dynamics is difficult to obtain in real-world applications. This work proposes a new Reinforcement Learning-based algorithm to apply energy-efficient control strategies to a single workstation consisting of identical parallel machines. The model goal is to achieve the optimum trade-off between system productivity and energy demand without relying on full knowledge of the system dynamics. Numerical experiments confirm effectiveness, applicability, and generality of the proposed approach, even when applied to a real-world industrial system from the automotive sector.

Multi-layer parallel transformer model for detecting product quality issues and locating anomalies based on multiple time‑series process data in Industry 4.0

Smart manufacturing systems typically consist of multiple machines with different processing durations. The continuous monitoring of these machines produces multiple time-series process data (MTPD), which have four characteristics: low data value density, diverse data dimensions, transmissible processing states, and complex coupling relationships. Using MTPD for product quality issue detection and rapid anomaly location can help dynamically adjust the control of smart manufacturing systems and improve manufacturing yield. This study proposes a multi-layer parallel transformer (MLPT) model for product quality issue detection and rapid anomaly location in Industry 4.0, based on proper modeling of the MTPD of smart manufacturing systems. The MLPT consists of multiple customized encoder models that correspond to the machines, each using a customized partition strategy to determine the token size. All encoders are integrated in parallel and output to the global multi-layer perceptron layer, which improves the accuracy of product quality issue detection and simultaneously locates anomalies (including key time steps and key sensor parameters) in smart manufacturing systems. An empirical study was conducted on a fan-out, panel-level package (FOPLP) production line. The experimental results show that the MLPT model can detect product quality issues more accurately than other methods. It can also rapidly realize anomalous locations in smart manufacturing systems.

Optimising production, maintenance, and quality control for imperfect manufacturing systems considering timely replenishment

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy.

The transition from batch to continuous manufacturing for tablet manufacturing performance comparison and control system review

The pharmaceutical industry is motivated to improve tablet productivity in order to adapt to the expanding drug market. One innovative way is the transition of the traditional batch tablet manufacturing process to a continuous process. This paper aims to review the performance of continuous tablet manufacturing processes. From the comparison of continuous to traditional batch processes, the continuous tablet manufacturing process demonstrates improvements in production flexibility, robustness, and ultimately process profitability. The continuous process is proven to mix drug particles with more evenly distributed size and less segregation resulting in tablets with better quality compared to the traditional batch process. The improvement on the continuous tablet manufacturing process requires robust control system for process automation to counteract process noise and disturbances in process feed. A well-tuned Proportional Integral Derivative (PID) control is sufficient for the process to produce a desirable product. Developing a new algorithm for model predictive control (MPC) and enhancing the control system with MPC-PID control can further improve the system performance. Overall, the transition from batch to continuous tablet manufacturing is supported by pharmaceutical companies as well as organizations like Food and Drug Administration (FDA).

A Systematic Literature Review: Industry 4.0 Based Monitoring and Control Systems in Additive Manufacturing

Three-dimensional printing, also referred to as additive manufacturing, offers a wide range of product diversity, design flexibility, and competitive product costs, making it a key technology in the Industry 4.0 era. With a growing demand for customer-oriented manufacturing strategies in the industry, 3D printing holds the potential to revolutionize traditional manufacturing systems by enabling the production of high-value-added complex products at reduced costs. This systematic literature review paper aims to analyze the ongoing research on Industry 4.0-based digital solutions in the field of monitoring and control to facilitate the adoption of 3D technologies. The study utilizes a systematic literature review method to provide detailed analyses. Specific keywords and a comprehensive database are employed for this study. Furthermore, the paper surveys the existing advancements in 3D printing machinery, focusing on process monitoring and control methods, as well as their impact on sustainability. The discussion section evaluates the literature review results for potential implementation in small and medium-sized enterprises.

Robust Higher-Order Spatial Iterative Learning Control for Additive Manufacturing Systems

In this article, a higher-order spatial iterative learning control (HO-SILC) scheme is proposed, targeting heightmap tracking for a class of 3-D structures fabricated by repetitive addition of material in a layer-by-layer fashion using additive manufacturing (AM) technology. AM processes are innately iteration-varying, resulting in large model uncertainties due to iteration-varying system parameters and surface variations. HO-SILC has been shown to be useful in repetitive systems with model uncertainties by improving system performance with respect to convergence speed and robustness. In this article, HO-SILC is used to iteratively construct a feedforward control signal to improve part quality in multilayered AM constructs. The system dynamics are approximated by discrete 2-D spatial convolution kernels that incorporate in-layer and layer-to-layer variations. The proposed HO-SILC framework incorporates data available from previously printed devices, as well as multiple previously printed layers, to enhance the overall performance. The condition for robust monotonic convergence (RMC) of the iteration-varying HO-SILC algorithm is based on the Lyapunov stability criteria. Simulation results of an AM process termed electrohydrodynamic jet (e-jet) printing demonstrate that a well-designed HO-SILC framework is effective and can improve the performance by 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> . In addition, HO-SILC is robust to iteration-varying model uncertainties, especially at higher layers where iteration-varying surface variations are more pronounced.

Reliability and performance analysis on stages to control manufacturing system using Copula technique

Reliability and performance analysis on stages to control manufacturing system using Copula technique

Continuous-flow simulation of manufacturing systems with assembly/disassembly machines, multiple loops and general layout

Performance evaluation methods are important to design and control manufacturing systems. Approximate analytical methods are fast, but they may be limited by the restrictive assumptions on the system. On the contrary, simulation has not specific limitations in its applicability, but the time to model and analyse a manufacturing system can increase as the level of detail addressed by the model increases. The main contribution of this study is presenting a computationally efficient methodology to simulate single-part continuous-flow manufacturing systems with assembly/disassembly machines, multiple loops, general layout and general inter-event time distributions. By using graph theory, a new method is presented to identify the machines causing slowdown, blocking and starvation in a general layout and determine the time before the occurrence of a state transition for each machine and the time before the fulfilment or depletion of each buffer. By advancing the time clock to the next event-time accordingly, the number of discrete events needed to be simulated is decreased compared to a discrete-event simulation with discrete flow of parts. As a result, the proposed method is on average 15 times faster than DES methods in the analysis of discrete-flow systems, and 110 times faster on average in the analysis of continuous-flow systems. The low computational time of the proposed method allows to simulate systems under general assumptions and in a very short time.