Complex Manufacturing Systems Research Articles

Data-Based Estimation of the Dynamic Reliability and Performance Indicator of an Industrial Manufacturing System

The aim is to develop a more simple and effective method's performance and dynamic reliability assessment for complex industrial systems. By using the operating data of the industrial system characterized by a strong desynchronization and applying to it prediction algorithms of artificial intelligence applied to the time series, the model will have learned from the behavior of the complex manufacturing system allowing the operator or decision-maker to better orientate the maintenance, production, and quality policies. Furthermore, we propose this approach to avoid tedious mathematical methods related to dynamic reliability calculations and performance evaluation to make forecasts of the company's operation over a long period by identifying future bottlenecks in the system's behavior. The low-performance indicators and irrelevant reliability presented by many third-generation industries are due to the lack of efficient and simple tools for reliability assessment taking into account the dynamic aspect of the different elements of the production chain, maintenance department, production department, and quality department. We propose to develop a model that will abstract from conventional, complex, and inefficient mathematical methods for systems subject to combinatorial explosion problems in the manufacturing industry.

Design of Manufacturing Systems Based on Digital Shadow and Robust Engineering

In the era of digital transformation, industry is facing multiple challenges due to the need for implementation of the Industry 4.0 standards, as well as the volatility of customer demands. The latter has created the need for the design and operation of more complex manufacturing systems and networks. A case study derived from Process Industries (PIs) is adopted in this research work in order to design a framework for flexible design of production lines, automation of quality control points, and improvement of the performance of the manufacturing system. Therefore, a Digital Shadow of a production line is developed to collect, analyze and identify potential issues (bottlenecks). An edge computing system for reliable and low-latency communications is also implemented. The digital model is validated using statistical Design Of Experiments (DOE) and ANalysis Of VAriance (ANOVA). For the assessment of what-if scenarios, the Digital Shadow model will be used in order to evaluate and find the desired solution. Ultimately, the goal of this research work is to improve the design and performance of the industry’s production section, as well as to increase the production rate and the product mix.

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry.

Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process's performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line.

A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem

In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach

Digital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems.

Predicting future production system bottlenecks with a graph neural network approach

Predicting throughput bottlenecks has received an increasing research attention in the recent years. Several statistical and machine learning models have been developed, with the objective of identifying future bottleneck locations for more efficient and cost-saving productions. However, bottleneck prediction remains a challenging task due to the complexity of manufacturing systems and the interactive effects from many impacting factors. This paper proposed the BSTAN (bottleneck spatial-temporal attention network) – an interpretable modeling framework to improve system bottlenecks prediction capability for complex manufacturing systems by: (1) characterizing line topological features using graph representations; (2) modeling spatial-temporal relationships and interactions between stations using graph attention network with gated recurrent units. In this proposed framework, the production line is first transformed into the representation of a weighted undirected graph to depict network structural information of a production line. An attention-based graph neural network with gated recurrent units is applied to capture both the temporal trends and station interactions to predict each station’s blockage and starvation. Future bottleneck stations are identified by analyzing the forecasted blockage and starvation distribution. To evaluate the model performance, an industrial case study is performed with data collected from a one-year production at an automotive powertrain assembly line. With the proper model construction, BSTAN has outperformed both statistical and machine learning benchmark models, achieving up to 15% lower root mean square error compared to the best benchmark method in predicting overall system blockage and starvation time.

Simultaneous predictive maintenance and inventory policy in a continuously monitoring system using simulation optimization

Predictive maintenance (PdM) is a strategy which can help determine the appropriate timing for maintenance depending on the actual operational conditions of a manufacturing system. Many relevant studies have ignored the effects of resource dependency and only considered perfect maintenance to reduce the complexity and uncertainty of maintenance problems. In this study, we develop a novel and highly practical maintenance model to fill the gap. The proposed model simultaneously considers the predictive maintenance and inventory policies of spare parts. In light of the high degree of complexity in modern manufacturing systems and the profound stochasticity in component degradation, a metamodeling-based simulation optimization method is proposed to find the optimal component inventory policy and degradation level thresholds of each component. A numerical study is conducted to verify that the proposed model and solution method can find the optimal or nearly optimal maintenance cost effectively and efficiently. Furthermore, the impact of critical factors in the maintenance model on the optimal policy is analyzed and useful managerial insights are derived.

Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.

An assessment method of machine components for usage-based value creation in complex manufacturing systems

Rising demands on manufacturing technologies lead to an increase in the complexity of corresponding machines and systems. This complexity acts as an obstacle to the development and realization of digital, usage-based business models for complex manufacturing systems. Particularly in the case of pay-per-use business models, it is necessary to monitor the actual machine load in operation to minimize the business risk on the operator and supplier side. This complicates the transition towards data-oriented value creation in manufacturing system environment. To overcome this challenge, a novel method to assess machine components regarding their potential to monitor the actual load and usage behavior of the whole manufacturing system is proposed. On this basis, the transparency and therefore confidence in digital, usage-based value creation like pay-per-use business models can be increased. The resulting approach is demonstrated by taking the example of a sheet metal forming press.

A Real-Time Human-in-the-Loop Control Method for Complex Systems

Real-time control tasks are of great importance in complex production and manufacturing systems since real-time operation may not proceed as pre-planned mostly. In this paper, we focus on a realtime control problem in a complex system which mainly consists of decision-makers, operators, and mobile machines. Each decision-maker takes charge part of the machines and controls their visiting sequences towards an operator in real time with respect to a variety of practical constraints. Although massive sensing data is available, decision-makers may not be able to process all the data and control too many machines simultaneously in real time owing to their limited computing ability and working memory. To address the problem, we design a human-in-the-loop control method in which decision-makers only need to supervise the control actions generated by a controller and can change these actions based on their real-time observations and judgements. The designed method contains four major parts which are the data processing and checking component, the controller, the exception warning component, and the controller and decision-maker interaction component. We develop a decision support tool based on the proposed method and apply it in a real-life application of controlling the truck arriving sequences at the quay cranes of a container terminal during the loading operation.

An approach to Quantum Annealing-based workforce assignment in manufacturing systems

Workforce assignment (WA) is a crucial aspect of production planning and control. However, current global crises have led to workforce absence and skill shortages. To tackle these challenges, a short-term WA approach is necessary for manufacturing systems flexibility and workforce health. Conventional methods for WA face complexity issues due to complex manufacturing systems, requiring substantial computation time. A Quantum Annealing-based WA approach is proposed in this paper to allocate tasks and shifts to workers. The proposed method is validated in a use case, showing improved solution quality compared to traditional methods.

A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks

Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines

With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site.

Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance

Antifragility was introduced as a term no later than 10 years ago. As presented by Taleb, antifragility means that a system becomes more resilient and more robust with every harmful and/or stressful action it is confronted with. This paper is based on a study which aimed to use the concept of antifragility during the design stage of a self-improving system. This way, it is expected to obtain a fast adaptive system capable of functioning at optimal parameters even when it works under adverse conditions or faces unforeseen changes in the environment. Assuming that an antifragile system not only maintains its robust behavior when faced with stressful and harmful events but even benefits from them to optimize its performance, the paper offers a detailed description of the features that must be ensured when designing a self-improving antifragile manufacturing system. By ensuring the property of antifragility, complex manufacturing systems are much safer to exploit under uncertain conditions, which brings major benefits to the process management. Starting from consecrated solutions such as preventive maintenance (PvM) and predictive maintenance (PdM) and using techniques of artificial intelligence, we present the concept of antifragile maintenance (AfM).

Self-organized criticality in manufacturing system unscheduled downtime series and its application to major failure prediction

In this study, self-organized criticality theory was applied to analyze the unscheduled downtime (USDT) characteristic of a complex manufacturing system. The long-range correlation of the USDT series was verified through the Hurst exponent value. Then, we developed a model to depict the relationship between the frequency and USDT duration with the extreme value theory. The parameters of the model were estimated with the maximum likelihood method and updated with Bayesian inference when new data was available. Finally, the effectiveness of the proposed method was validated by the USDT data of the bottleneck system from a semiconductor assembly and test factory.

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.

Multi-channel data fusion and intelligent fault diagnosis based on deep learning

In complex manufacturing systems, multi-channel sensor data are usually recorded for fault detection and diagnosis. Most existing multi-channel data processing methods adopt tensor analysis technology, which cannot effectively describe the temporal and spatial structures of the multi-channel data. The obstacles in multi-channel data analysis are the temporal correlation between the time-series data of the single-channel and the spatial correlation between different channels. In this paper, a novel deep convolutional neural network model is proposed for multi-channel data fusion and intelligent fault diagnosis. First, features of the multi-channel data are extracted from two scales. The extracted features are then fused through a multi-layer neural network. Finally, a classifier of fault modes is established by using the improved Softmax function. The fault diagnosis performance of the proposed model is evaluated and compared with other common methods in both the simulation studies and real-world case studies. Results show that the proposed methodology has superior fault diagnosis performance for multi-channel data.

Fuzzified deep neural network ensemble approach for estimating cycle time range

Because of the high uncertainty associated with predicting the cycle time of a job in a complex manufacturing system, this task is a challenge for production planners. To replace an inaccurate cycle time forecast, the range of cycle time is estimated. To estimate the cycle time range of a job as precisely as possible, a fuzzified deep neural network (FDNN) ensemble approach is proposed in this paper. This approach involves the following steps. First, a deep neural network (DNN) is constructed to predict the cycle time of a job. The parameters of the DNN are then fuzzified to generate a fuzzy cycle time forecast that contains the actual cycle time. In contrast to existing methods that involve fuzzifying multiple parameters simultaneously, which is computationally intensive, the proposed methodology involves fuzzifying parameters independently to facilitate problem-solving. Subsequently, the cycle time ranges estimated by fuzzifying various parameters are aggregated through fuzzy intersection. The proposed FDNN ensemble approach was applied to a real case to validate its effectiveness. The experimental results indicated that the precision of cycle time range estimation was up to 38% higher with the FDNN ensemble approach than with the fuzzy backpropagation network approach.

Performance analysis of flexible flow shop with parallel manufacturing cells

Performance evaluation for stochastic manufacturing systems is significant for system design optimization, but it is difficult to exactly analyse these systems with general arrival processes and service times. This paper studies a flexible flow shop with parallel manufacturing cells and models it as a queueing network. Parallel manufacturing cells and finite pallets lead to strong dependence among stages, and existing methods cannot be directly employed in this problem. Thus, approximate analysis is proposed to estimate performance measures of the flexible flow shop with parallel manufacturing cells based on two-moment approximations for a parallel-input synchronization station and an aggregation procedure. Numerical studies show the accuracy and the efficiency of the proposed method by comparing it with simulation results of experiments. This work can provide a basis to analyse and design more complex manufacturing systems.