Discrete Manufacturing Research Articles

Data Acquisition Network Configuration and Real-Time Energy Consumption Characteristic Analysis in Intelligent Workshops for Social Manufacturing

To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy consumption data processing of manufacturing systems. Meanwhile, under the growing trend of personalization, social manufacturing is an emerging technical practice that allows prosumers to build individualized services with their partners, which produces new requirements for energy data processing. Thus, a real-time energy consumption characteristic analysis method in intelligent workshops for social manufacturing is established to realize data processing and energy efficiency evaluation automatically. First, an energy-conservation production architecture for intelligent manufacturing processes is introduced, and the configuration of a data acquisition network is described to create a ubiquitous manufacturing environment. Then, an energy consumption characteristic analysis method is proposed based on the process time window. Finally, a case study of coupling-part manufacturing verifies the feasibility and applicability of the proposed method. This method realizes a combination of social manufacturing and real-time energy characteristic analysis. Meanwhile, the energy consumption characteristics provide a decision basis for the energy-saving control of intelligent manufacturing workshops.

Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements. In this regard, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named hierarchical multi-agent proximal policy optimization (HMAPPO) is developed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO contains three proximal policy optimization (PPO)-based agents operating in different spatiotemporal scales, namely, objective agent, job agent, and machine agent. The objective agent acts as a higher controller periodically determining the temporary objectives to be optimized. The job agent and machine agent are lower actuators, respectively, choosing a job selection rule and machine assignment rule to achieve the temporary objective at each rescheduling point. Five job selection rules and six machine assignment rules are designed to select an uncompleted job and assign the next operation of which together with its successors in no-wait constraint on the corresponding processing machines. A hierarchical PPO-based training algorithm is developed. Extensive numerical experiments have confirmed the effectiveness and superiority of the proposed HMAPPO compared with other well-known dynamic scheduling methods. Note to Practitioners—The motivation of this article stems from the need to develop real-time scheduling methods for modern discrete flexible manufacturing factories, such as aerospace product manufacturing and steel manufacturing, where dynamic events frequently occur, and each job may contain several operations subjected to the no-wait constraint. Traditional dynamic scheduling methods, such as metaheuristics or dispatching rules, either suffer from poor time efficiency or fail to ensure good solution quality for multiple objectives in the long-term run. Meanwhile, few of the previous studies have considered the partial-no-wait constraint among several operations from the same job, which widely exists in many industries. In this article, we propose a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO uses three DRL-based agents to adaptively select the temporary objectives and choose the most feasible dispatching rules to achieve them at different rescheduling points, through which the rescheduling can be made in real time and a good compromise among different objectives can be obtained in the long-term schedule. Extensive experimental results have demonstrated the effectiveness and superiority of the proposed HMAPPO. For industrial applications, this method can be extended to many other production scheduling problems, such as hybrid flow shops and open shop with different uncertainties and objectives.

Research on Key Technologies of Intelligent Perception and Control in Discrete Manufacturing Industry based on IEC61499 Standard

This paper aims at the characteristics of discrete manufacturing, such as changeable process, complex process, multiple varieties and small batch, the mechanism of real-time perception and dynamic optimization of various production factors such as materials, equipment and personnel in the production process is established, the interaction and integration between the physical world and information space in the discrete manufacturing environment is established, and the distributed industrial intelligent perception and control solution is developed based on the industrial Internet IEC61499 standard.

Ontology-Driven Guidelines for Architecting Digital Twins in Factory Automation Applications

The rapid emerging technologies in various fields permitted the creation of simulation tools. These tools are designed to replicate physical systems in order to provide faster, cheaper and more detailed illustrative analysis of the physical system. In this regard, the concept of digital twins has been introduced to generally define these simulation tools. In fact, and according to the creator of the digital twin term Micheal Grieves, a digital twin is defined as a physical system, a digital replica of the physical system and information flow between the former parts. This definition is simple and generic for describing digital twins and yet, holistic. This broad definition creates a challenge for developers who target the development of such applications. Therefore, this paper presents a paradigm for architecting digital twins for manufacturing processes. The approach is inspired by the definitions of the ISA95 standard and the onion concept of computer applications to create multi-layer and multi-level concepts. Furthermore, and to satisfy the different required features by industries, the approach considers a multi-perspective concept that allows the separation of the digital twin views based on functionality. This paradigm aims at providing a modular, scalable, reusable, interoperable and composable approach for developing digital twins. Then, an implementation of the approach has been introduced using an ontology-based system and the IEC61499 standard. This implementation has been demonstrated on a discrete manufacturing assembly line.

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

Manufacturing Service Innovation and Foreign Trade Upgrade Model Based on Internet of Things and Industry 4.0

At present, the industrial Internet has gradually become a product of the integration of new generation of information technology and manufacturing. The development of the traditional manufacturing industry with transformation and upgrading faces various bottlenecks. These bottlenecks require the industry to make technological improvements through the concept of promoting sustainable development. At the same time, these issues also need intelligent support to match them. IoT technology can help enterprises realize product design and production, inspection and testing, risk early warning and post-sales, and other process links. The Internet of Things technology improved and innovated the industrial production process in the process. This technology has become the main means to promote industrialization and information integration, and it is an important basis for promoting manufacturing capabilities and networking forces. At present, the industrial Internet of our country is generally developing rapidly. However, this technology encountered many difficulties in the process of promoting discrete manufacturing and upgrading. There are still some difficulties in the entire technology landing application process. For example, there is still a significant gap in the breadth and depth of the Internet of Things applications. In particular, the value of data needs to strengthen. Based on the analysis of the risk solutions and development dilemma of artisanship, this article shows the benefits of the Internet of Things technology. Trend demand, practical application demand, production efficiency demand, after-sales service demand, and other aspects promote management innovation. This article first classifies the development of the industrial Internet in information transmission and development of the industry around the world. On this basis, this article has built a three-dimensional reference model for the industrial Internet to promote the transformation and upgrade of discrete manufacturing. In addition, this article proposes a strategy to promote the transformation and upgrade of discrete manufacturing. It includes three development stages: industrial Internet, industrial and commercial Internet, and industrial intelligence Internet. On this basis, the text explains the corresponding technical system and its challenges. Through the research results of this article, it provides theoretical guidance for the industrial Internet to promote the transformation and upgrade of discrete manufacturing.

The definition of smart economy and digital transformation of business in the concepts Industry 4.0 and 5.0

The object of the study is the relationship between economic entities in the smart economy and their digital transformation in the conditions of Industry 4.0 and 5.0. One of the most problematic areas is the definition and structuring of the smart economy and establishing the effects of digital business transformations in the conditions of Industry 4.0 and 5.0. The research used methods of grouping, system analysis, historical approaches to scientific research, synthesis and analysis, forecasting, etc. A qualitatively new definition and structuring of the smart economy was obtained, which is connected with the use of new business technologies in the conditions of total digital transformation in the concepts of Industry 4.0 and 5.0. In particular, new classification approaches to the structuring of the smart economy and digital transformation of business in the concept of Industry 4.0 and 5.0 are singled out. The conducted study of the state of the smart economy and digital transformation of business according to the concepts of Industry 4.0 and 5.0 indicate a significant potential for the use of digital technologies in business and a significant economic effect of their use, which requires further improvements. This puts the potential for investment in robotic manufacturing to reach 120.6 billion USD in 2025, followed by autonomous operations and 360-degree customer management at 90.9 and 74.7 billion USD, respectively. The industries that will have the highest spending on digital business transformation in the smart economy throughout the forecast period are discrete and technology manufacturing, followed by professional services and retail. The economic sectors that will experience the fastest growth in spending on digital business transformation in the smart economy, according to the forecast for 2020–2025, are construction (21.0 %), securities and investment services (19.2 %), and banking (19.0 %). Compared to similar well-known developments, all 19 industries considered in the digital transformation of business in the smart economy are predicted to provide double-digit growth over the five-year forecast. This provides significant economic advantages in the adaptation of economic agents to the conditions of introducing digital business transformation in the context of Industry 4.0 and 5.0.

Genetic Algorithm with Local Search for the Multi-Target Scheduling in Flexible Manufacturing System

Traditional large-scale process manufacturing is gradually transformed into customized discrete manufacturing with the fierce global competition. Production planning has an important impact on improving manufacturing efficiency in the ever-changing from the view of engineering management. However, many nonprocessing-related factors in the flexible manufacturing system make it different between the formulation and implementation of the production plan. We established a multi-target optimization model based on the scheduling data of a discrete manufacturing company. In order to optimize the local effect of the scheduling model, we proposed an improved genetic algorithm with local search (GALS). The results of the experiments show that GALS is far superior to the current genetic algorithm scheduling in terms of the number and quality of scheduling solutions. Compared with the current scheduling strategy of the enterprise, the scheduling strategy given by GALS achieved an average improvement of 29.61% in minimizing completion time, achieved 44.8% in minimizing transportation time, and achieved 44.64% in machine load balancing.

Research on the evaluation method of the operation status of digital workshop in discrete manufacturing industry

Research on the evaluation method of the operation status of digital workshop in discrete manufacturing industry

EveSyncIAI: Event Synchronization Industrial Augmented Intelligence for Fault Diagnosis

The fault diagnosis of complex timed discrete event systems remains a relevant and challenging problem in industry. As systems degrade, the synchronization required for normal operation associated with timed event sequences deteriorate. However, existing discrete event approaches cannot handle complexity well as there may be potentially hundreds of event sequences that still result in normal system behavior, and they do not scale well due to the state explosion problem. To circumvent these issues, a new data-driven augmented intelligence framework called EveSyncIAI is proposed that uses a Timed Petri Net of the normal process to enable selective extraction of discriminating time delay features, which are then used alongside machine learning for fault diagnosis. The developed methodology is applied to a case study of an operational discrete manufacturing system in the semiconductor industry. Multiclass machine learning approaches as well as binary anomaly detection algorithms with varying levels of supervision are directly compared with and without EveSyncIAI-extracted features. With precision, recall, and F1-score values above 0.90 being achieved for multi-fault classification and AUC scores exceeding 0.98 for semi-supervised and unsupervised anomaly detection approaches, the framework presented is promising for the fault diagnosis of timed sequence-sensitive systems.

Advances in Adaptive Scheduling in Industry 4.0

The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.

Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things

Abstract This article proposes a software fusion processing method for multi-sensor monitoring the Internet of Things based on the fractional differential filtering algorithm. The purpose is to solve the difference between the detection data caused by the difference in each production information sampling node’s equipment performance and working environment. The article applies the above algorithm to the remote detection data fusion processing experiment of the discrete manufacturing system of the Internet of Things. The research found that the algorithm realizes the remote high-precision measurement of the Internet of Things sensor information. The fractional integral operator has high fusion accuracy in remote detection data fusion processing.

Calculus Logic Function in Integrated Manufacturing Automation of Single Chip Microcomputer

Abstract First, this paper uses the Lyapunov stability function to construct the time-space implicit calculus equation model of the integrated manufacturing of single-chip microcomputers for the abnormal diffusion phenomenon. Secondly, we use the differential data fusion algorithm modeling of the discrete manufacturing system and introduce the implicit calculus equation modeling method. Finally, the article uses the singular boundary method to give the simulation results. The study found that the implicit calculus equation modeling method is simpler than the existing model, and the physical and statistical concepts are clear.

Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

This paper presents a data-driven approach for the prognosis of the gradual behavioural deterioration of conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems. The approach relies on the knowledge of the power consumption of a conveyor belt motor driver. Data are collected for two separate cases: the static case and dynamic case. In the static case, power consumption data are collected under different loads and belt tension. These data are used by a prognostic model (artificial neural network (ANN)) to learn the conveyor belt motor driver’s power consumption pattern under different belt tensions and load conditions. The data collected during the dynamic case are used to investigate how the belt tension affects the movement of pallets between conveyor zones. During the run time, the trained prognostic model takes real-time power consumption measurements and load information from a testbench (a discrete multirobot mobile assembling line) and predicts a belt tension class. A consecutive mismatch between the predicted belt tension class and optimal belt tension class is an indication of failure, i.e., a gradual loss of belt tension. Hence, maintenance steps must be taken to avoid further catastrophic situations such as belt slippages on head pulleys, material slippages and belt wear and tear.

An interoperable and flat Industrial Internet of Things architecture for low latency data collection in manufacturing systems

Industrial Internet of Things seeks to integrate Cloud-Edge-End computing architecture into existing manufacturing systems as an emerging and prospective paradigm of intelligent manufacturing to increase productivity and efficiency. Nowadays, most existing systems adopt a traditional pyramidal architecture for data collection. However, with the increasing of heterogeneous devices and diverse communication protocols, interoperability and latency become a challenging and open issue that brings difficulty to the data collection. In this paper, we propose an interoperable and flat Industrial Internet of Things architecture for low latency data collection based on two emerging technologies, OPC UA and Software-Defined Networking (SDN). The OPC UA-based edge intelligent gateway shields the heterogeneity of devices by providing a unified semantic information model and standard data access interface. SDN delivers flexible configuration and centralized control of the network resource and scheduling, which improves resilience and manageability. Moreover, we implement the proposed architecture on a discrete intelligent manufacturing production line testbed to evaluate its feasibility. The experiment results demonstrate that the proposed architecture can improve interoperability and latency for legacy manufacturing systems.

A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques

Adequate energy awareness is an essential prerequisite for energy-efficient production planning and operation in manufacturing systems. Nonetheless, a lack of awareness of the impact of managerial and operational energy-influencing factors on energy consumption can be observed in practice due to irrational and inexplicable energy benchmarks. To this end, this paper presents a dual energy benchmarking methodology that specifically promotes energy awareness in terms of managerial and operational factors. The presented methodology uses data mining techniques to excavate energy data as well as managerial and operational data, consisting of three steps: (i) data preparation to provide reliable multi-source heterogeneous data; (ii) dynamic energy benchmarking that uses the decision tree algorithm to classify different energy consumption patterns; (iii) load shape benchmarking that interprets energy use behaviors based on load shape features. To demonstrate the effectiveness and practicality, the methodology was implemented in a die-casting workshop. The results showed that a total of nine dynamic energy benchmarks were created primality under the impact of capacity utilization, two of which generated five representative load curves to characterize the workshop's daily energy use behaviors. It was concluded that the methodology could provide practical recommendations on energy-efficient production planning and operation in practical applications.

A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness

Abstract A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.

A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop

ABSTRACT Accurate order remaining completion time (ORCT) prediction provides an essential criterion for dynamically triggering the adjustment of production plans and establishment of dispatching strategies, which helps to improve the plan rationality and production efficiency, thus guaranteeing delivery orders on time. With the extensive deployment of Industrial Internet of Things in the workshop, the data for ORCT prediction is perceived in real-time. However, data quality, knowledge inconsistency, and data distribution variation make ORCT prediction more difficult. Hence, a stacking denoising auto-encoder with sample weight (SW-SDAE) method is proposed to improve the robustness and applicability of ORCT prediction. Firstly, a four-layer SDAEs is constructed to extract high-level and robust features. Secondly, a dynamic updating method of sample weight for regression prediction is designed to guide the training of prediction model parameters and improve the prediction accuracy. Thirdly, model-based transfer learning is employed to adapt to the data distribution change over time and ensure the prediction applicability. Finally, different prediction models are applied to an actual case for comparison. The experimental results show that the proposed prediction method is effective for ORCT prediction and superior to other methods.