Generation Manufacturing Systems Research Articles

A new description model for enabling more general manufacturing systems representation in digital twin

The demand for high efficiency and flexible production has led to more complex and flexible production organizations in discrete manufacturing systems. How to rapidly construct/reconfigure a workshop digital twin in response to a configurable complex physical production organization becomes a challenging problem. To address this challenge, based on the existing SevenElement description model, we propose an improved Material Nodes-oriented SevenElements (MNOSE) description model. This model is not only capable of describing/expressing inter-device relationships among manufacturing equipment/devices but also intra-device internal relationships between the device and materials handled by it. Its potential impact on shop-floor digital twin is to make an updating of shop-floor digital twin easier and faster because it can easily express and rapidly (re)-construct a shop-floor digital shadow in response to a rapidly physically reconfigured workshop for a different production organization. In addition, the model can drive the logical unification of multiple digital twin services in a plant and improve the efficiency of integration between several information systems. The utility of the description model in describing shop floor is demonstrated by a case study and validated by modeling representations of typical production organizations.

Tolerance Analysis of Cyber-Manufacturing Systems to Cascading Failures

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

Human-Robot collaboration in the next generation manufacturing and logistics system

Human-Robot collaboration in the next generation manufacturing and logistics system

Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Researchers in the field of smart manufacturing have recognized the benefits of blockchain technology, which solves the trust problem in the open network without relying on any trusted third party. Blockchain technology enables interaction between otherwise competing manufacturing entities to satisfy increasing customer demands in a trustful way. However, existing blockchain networks are facing limitations, which are defined by the trade-off between scalability, decentralization, and security. The scalability of the blockchain network is defined as the ability of the network to support an increasing load of transactions and it is lower compared to the non-blockchain systems. In order to omit the effects of the limitations, scalability solutions are being presented. This research reviews the literature in the field of blockchain-supported manufacturing concerning scalability solutions. The selected literature has been reviewed and classified according to the type of scalability solution. For each type of scalability solution, the main features of the concepts and connection between blockchain technology and manufacturing system are highlighted and discussed. The main findings of the study are that Layer 1 scalability solutions are better represented in the literature and are predominating in the case of general smart manufacturing systems, whereas Layer 2 scalability solutions are better represented in the case of specific smart manufacturing systems. Based on insights obtained from the presented analysis, future directions and open issues regarding the scalability limitations and solutions in blockchain-supported manufacturing are presented.

Development and Evaluation of Control System in Mechatronics – A Systematic Survey

The advancement of mechatronic enabling technologies and the mechatronic design approach has resulted in sophisticated mechatronic systems. Automated mechatronic systems are becoming more complicated and more intelligent. Mechanical systems that enable the next generation of manufacturing systems will have whole new features and capabilities as a result of these modifications. Even basic monitoring has grown into self-optimizing performance in these gadgets. With the addition of bio-mechatronics and micro-mechatronics, the application fields of mechatronics have expanded. Bio or microcontroller-based applications are the focus of this publication, which aids researchers in building and testing control systems. Design considerations for mechatronic systems are addressed in this work. In order to implement more complicated control algorithms in an industrial setting, new controller design tools are required. The early evaluation of designs and the support of critical design choices may be made possible via the use of modelling and simulation technologies.

Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes.

Holistic management of next generation manufacturing system

The manufacturing system is a widely addressed subject. Many researchers have worked since a significant number of years in this field. Still, manufacturing system performance is capped by the insufficient level of accordance between manufacturing system and product. At the same time, global energy and global information networks have been created during recent years. Besides other applications, these networks can be turned in hard and soft infrastructure supporting the manufacturing system. For this reason, many specialists consider that the conceptual rebuilding of the manufacturing system is a notable challenge. This paper answers to this challenge by suggesting a holonic technique for manufacturing system management at basic, operational level. The proposed technique lays on the following hypothesis: i) next generation manufacturing system will be a modular cyber-physical system, ii) the typical management task is decision making, and iii) the procedure for decision making consists in options identification, followed by their comparative assessment. The approach is to manage the manufacturing system activity by holonic modelling of the system functioning, followed by the identification of a dedicated holarchy. The proposed technique aims to mitigate system performance in what concerns responsiveness capacity, investment efficiency and production costs. The technique is presented in architectural conceptual manner.

Architectural Holarchy of the Next Generation Manufacturing System

Nowadays, the global energy network can generate and transmit, between any two points belonging to it, high quantity of energy. During recent years, a global information network, able to process, store, and transmit huge amounts of information, has been developed as well. These networks entirely cover the industrial space, already giving the opportunity to make permanently available, in any of its points, at any time, as much as needed, both energy and information. On the other hand, the mass customization trend has led to the pronounced increase of “manufacturing to order” (MTO) production, taking place in a higher and higher number of small & medium enterprises. At this level, a given manufacturing system cannot be quickly and appropriately configured to a given product, due to production high variability in range. As consequence, the manufacturing system is, quite always, more or less unadjusted to the manufactured product, its performance being significantly affected. Starting from here, the challenge is to make a conceptual rebuilding of the manufacturing system, aiming to increase its degree of appropriateness to products, by taking advantage from the opportunities brought by the existence of global energy & information networks. This paper approach is to see the next generation manufacturing system as holonic modular cyber-physical system. System architecture permanently accords to the manufactured product requirements. The function, procedure, topology and holarchy model of the system are presented. The main features of the system are also revealed.

Profit Maximizing Smart Manufacturing Over AI-Enabled Configurable Blockchains

Based on the trustless feature of blockchain, this article designs a general configurable blockchain-enabled smart manufacturing system to achieve flexible manufacturing in response to large-scale manufacturing services. With a transaction pool containing all the pending manufacturing tasks but aligning with the logic flow, the complex manufacturing structure can be uniformly tackled. Furthermore, in virtue of the contradiction between large-scale manufacturing and limited blockchain throughput, we formulate a joint optimization of the block size, task scheduling, and the supply-demand configuration to maximize the customers’ net profit with the probabilistic delay requirements, which addresses the critical issue of efficiency and latency in the blockchain-based live manufacturing process. Meanwhile, the production quality and price preference are involved. For solution, a mixed online bipartite matching-based DQN algorithm is proposed, which circumvents the high dimensionality by separating the task-manufacturer matching from the time-correlated problem. Simulation results show that the proposed flexible framework can well adopt to dynamic customer population, and achieves better convergence.

Identification and classification of risk factors for human-robot collaboration from a system-wide perspective

Industry 4.0 systems in general and advanced manufacturing systems such as collaborative robots, in particular, are characterized by a high level of complexity leading to new safety concerns. Safety, specifically for collaborative robots, has been mainly addressed from a technical perspective, to safeguard the physical safety of the operator. Concerns have been raised regarding less focus in Industry 4.0 literature on how other factors, such as psychosocial can produce safety-related risks for the operator in human-robot collaboration.This paper identifies and classifies the risk factors in a human-robot collaboration that have been described in research papers in the last decade. The resulting five classes constitute dimensions that will be used as preliminary building blocks for a safety evaluation framework to be developed in the next step. By evaluating the resulting classes with the underlying dimensions of contemporary socio-technical thinking, this paper demonstrates that these five classes offer a comprehensive, system-wide perspective including risk factors beyond technological considerations.Topics emerging from new risks related to the impact of working with collaborative robots, such as psychosocial, ethical, and cyber risk factors will need to be taken into account in the risk factors that are important to identify, assess and mitigate before working with collaborative robots. Operator involvement and participation, especially throughout the risk assessment and mitigation cycle are recommended as new areas of attention in human-robot collaboration.Going forward, one challenge will be the agility and adaptability of legislation to at least keep track of risk factors emerging from continuously changing technologies and to translate them into practically applicable tools for enterprises and design engineers implementing collaborative applications. Another key challenge will be the measurement of the new emerging and sometimes less technological risks.

NSGA-II vs NSGA-III for the Sustainable Multi-Objective Process Plan Generation in a Reconfigurable Manufacturing Environment

Abstract Since global economic competition, a new generation of manufacturing systems has been developed for changing market needs while minimizing harmful environmental impacts. Reconfigurable manufacturing system (RMS) can increase the system sustainability and reactivity to satisfy the market needs. In this paper, we consider an environmental oriented multi-objective problem for a sustainable reconfigurable manufacturing system (S-RMS). First, in the case of a single unit process plan generation, a non-linear multi-objective integer program (NL-MOIP) is presented, where four objectives are optimized respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted from energy consumption and the hazardous liquid wastes. Second, to solve the problem, adapted versions of the well-known non-dominated sorting genetic algorithm respectively NSGA-II and NSGA-III are presented. Finally, to illustrate the comparisons between both meta-heuristics, some experimental results are presented and analyzed.

The Practical Applications of Industry 4.0 Technology to a New Plant for Both Manufacturing Technique and Manufacturing Process in New Product Introduction

Starting with a product quality problem, this paper presents the practical applications of Industry 4.0 technology to solve the problem in manufacturing technique and to establish manufacturing process control in new product introduction (NPI) with flexibility and reconfigurability, under the condition that this is a new plant with limited resources. With regard to solving the manufacturing technique problem, the Finite Element Method (FEM) has been used to analyze failure mode and to support design optimization, the regression analysis has been adopted to obtain the force-deformation relationship and to guide the improvement for manufacturing parameter adaptation. The analysis method that combined the FEM analysis and the regression analysis can be an effective approach for dealing with manufacturing technique problem. With regard to establishing the manufacturing process control, the Radio Frequency Identification (RFID) implementation functions as the medium for data transfer and for status confirmation, thus realizing the process control and the quality control in the form of cyber-physical system (CPS). Data strategy of the process control and the quality control can be applied to general manufacturing system. The Industry 4.0 implementations and methods mentioned in this paper can serve as a reference for the company that are planning to build a small or medium-sized manufacturing plant and to enhance its performance with Industry 4.0.

Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing

Intelligent manufacturing as the development direction of the new generation manufacturing system has become a hot research topic. Computer numerical control (CNC) machine tools are the core manufacturing equipment in discrete manufacturing enterprises, collecting and monitoring the data is an important part of intelligent manufacturing workshops. It has a great significance to improve the production efficiency of enterprises and eliminate information islands. The purpose of this article is to solve the problems of data acquisition and monitoring of CNC machine tools in the manufacturing workshop of enterprises. This article uses FOCAS data acquisition method to research and develop the data acquisition and monitoring system of CNC machine tools in intelligent manufacturing workshop. The research results show that the equipment information model based on MTConnect protocol and FOCAS can solve the data acquisition and storage functions of CNC machine tools well. Using the object-oriented Petri net model, it can solve various uncertain factors in numerical control (NC) machining tasks and realize the monitoring function of CNC machining tasks in the workshop. Based on the NC program analysis, the calculation method of machining time in the NC program can determine the preventive maintenance cycle of the machine based on the machine fault information. Based on VS2013 development environment, Qt application framework and SQL Server 2012 database, the numerical control machine tool data acquisition and monitoring prototype system was developed, and the system was verified in the workshop to prove the effectiveness of the system.

Optimal sequencing of part families and configuration selection for process improvement in reconfigurable manufacturing system

Reconfigurable manufacturing systems (RMS) are considered as next generation manufacturing systems that are capable of providing the functionality and capacity as and when required. Products are classified into several part families as per customer requirement and each of them are a set of similar products. In a shop floor, the manufacturers have to deal with varied number of orders for multiple part families and after completing the orders of a particular family, they need to change over to the orders of a different part family. Shifting from one part family to another may require the system's reconfiguration, which is a complicated method and requires tremendous cost and efforts. The complexity, effort and cost from changing one configuration to another depends on the existing initial configuration and the new configuration required for subsequent production of orders belonging to a different part family. There are different models available for process improvement. This paper focuses on determining optimal sequencing of part families formation and configuration selection for process improvement in RMS on the basis of minimum loss occurred for a given system configuration. The proposed methodology is explained and an example is given for illustration.

Design of a tri-adjustable automated heavy-duty handling system based on Industry 4.0 principles

Abstract Material handling is one of the most essential aspects within manufacturing processes and or industries. Transportation equipment used in manufacturing industries varies from pallet jack to forklift trucks and or cranes. Material handling equipment are mechanical equipment used for the movement, storage, control and protection of materials, goods and products throughout the process of manufacturing, distribution, consumption and disposal. The influence of global economy has directed to changes in the conventional approaches for manufacturing companies. In this case, manufacturing companies in South Africa and globally has taken into consideration several essential characteristics such as real-time reaction to changes, quick and quality response in sustaining and satisfying customer requests and or needs, in both hardware equipment and software modules by which the production processes are optimized for next generation manufacturing systems. Therefore, this paper aims at designing an Automated Heavy-Duty Material Handling System based on Industry 4.0 principles.

Robust Smart Energy Efficient Production Planning for a general Job-Shop Manufacturing System under combined demand and supply uncertainty in the presence of grid-connected microgrid

This paper is the first to introduce the concept of Smart Energy-Efficient Production-Planning (SEEPP) for a general Job-Shop manufacturing system in the presence of Grid-connected Microgrid with wind power generation. To cope with the unpredictability of wind speed and the uncertainties of demands, a novel risk-based Robust Mixed Integer Linear Programming (RMILP) model is mathematically formulated. The last aim of the model is to minimize the total day-ahead cost of the system considering peak demand charge. The results show the capability of the proposed integrated framework to obtain a constructive trade-off between scenario-based cost deviations, and heat and power imbalances considering the attitude of Decision Makers (DMs) toward risk. The results further indicate that the proposed SEEPP concept is able to produce products with at least 1.95% less cost than conventional manufacturing systems, although it supports a much wider range of demands for products. The performance of the model is analyzed and evaluated in terms of accuracy, robustness, and computational efficiency.

Effect of Cooperation on Manufacturing IT Project Development and Test Bed for Successful Industry 4.0 Project: Safety Management for Security

A new direction of the 4th industrial revolution in manufacturing and IT industries is presented in this study, wherein the manufacturing sector will be able to survive in this period by achieving rapid and flexible change through effective convergence between both industries. Under such an environment, manufacturing IT requires speedy development and a new distribution form, as well as a new method of IT project development which is adequate for that form. Thus, this study compares and analyzes the waterfall method which is being used in general manufacturing System Integration (SI) projects and the proposed DevOps method, which requires faster distribution and improvement. This study confirms that the required human resources are less than the existing SI projects when system improvement is made using the DevOps method. At the same time, this method provides much-improved quality for the same price. Therefore, future manufacturing IT projects would achieve a faster and more efficient development and operation form by adopting the DevOps method to perform continuous and repetitive improvement and operation through the convergence of manufacturing and IT. Also, some of the current smart factory models can be found in several ICT (Information & Communication Technology) advanced countries, and they have actually increased the efficiency in their factories and generated much value-added business. As with the production facilities, materials, and methods, human resource management occupies an important role in the smart factory system to increase efficiency. This study aims to validate such logic by proving the effectiveness of a Bluetooth beacon-based worker positioning system by which workers’ safety can be secured along with the security of the factory itself. This system can be foundational to establishing a safer working environment by controlling accesses to the critical production facilities and determining their whereabouts in case of an accident.

Biologicalisation: Biological transformation in manufacturing

A new emerging frontier in the evolution of the digitalisation and the 4th industrial revolution (Industry 4.0) is considered to be that of “Biologicalisation in Manufacturing”. This has been defined by the authors to be “The use and integration of biological and bio-inspired principles, materials, functions, structures and resources for intelligent and sustainable manufacturing technologies and systems with the aim of achieving their full potential.” In this White Paper, detailed consideration is given to the meaning and implications of “Biologicalisation” from the perspective of the design, function and operation of products, manufacturing processes, manufacturing systems, supply chains and organisations. The drivers and influencing factors are also reviewed in detail and in the context of significant developments in materials science and engineering. The paper attempts to test the hypothesis of this topic as a breaking new frontier and to provide a vision for the development of manufacturing science and technology from the perspective of incorporating inspiration from biological systems. Seven recommendations are delivered aimed at policy makers, at funding agencies, at the manufacturing research community and at those industries involved in the development of next generation manufacturing technology and systems. It is concluded that it is valid to argue that Biologicalisation in Manufacturing truly represents a new and breaking frontier of digitalisation and Industry 4.0 and that the market potential is very strong. It is evident that extensive research and development is required in order to maximise on the benefits of a biological transformation.

Scheduling Cluster Tools in Semiconductor Manufacturing: Recent Advances and Challenges

Cluster tools are automated robotic manufacturing systems containing multiple computer-controlled process modules. They have been increasingly used for wafer fabrication. This paper reviews the modeling and scheduling methods for cluster tools with both nonrevisiting and revisiting processes. For nonrevisiting processes, we focus on the modeling and scheduling problems of cluster tools with different constraints. Then, their solution methods are reviewed and compared. For revisiting processes, this paper first discusses the scheduling problem of some general manufacturing systems with revisiting. Then, the modeling and scheduling methodologies used to solve the scheduling problems of cluster tools with revisiting processes are reviewed. Future research directions and conclusions are finally discussed. Note to Practitioners —Semiconductor manufacturing systems are among the most advanced and complicated manufacturing systems. Their key equipment is highly automated robot-based cluster tools. With wafer residency time constraints, wafer revisiting, activity time variation, chamber cleaning requirements, and failure-prone process modules (PMs), it is very challenging to schedule and control them. This paper surveys their modeling and scheduling methods. Scheduling them requires one to schedule their robot tasks and processing activities simultaneously. Owing to wafer residency time constraints and the lack of buffers among PMs, it is difficult to conduct their optimal scheduling. This paper presents a thorough review of the state-of-the-art research results about modeling and optimal scheduling of clusters tools and indicates the future research directions.

Sustainable Living Factories for Next Generation Manufacturing

To be profitable and to generate sustainable value for all stakeholders, next generation manufacturers must develop capabilities to rapidly and economically respond to changing market needs while at the same time minimizing adverse impacts on the environment and benefiting society. 6R-based (Reduce, Reuse, Recycle, Recover, Redesign and Remanufacturing) sustainable manufacturing practices enable closed-loop and multi-life cycle material flow; they facilitate producing more sustainable products using manufacturing processes and systems that are more sustainable. Reconfigurable Manufacturing Systems (RMS) and its characteristics of scalability, convertibility, diagnosability, customization, modularity and integrability have emerged as a basis for living factories for next generation manufacturing that can significantly enhance the system sustainability by quickly adjusting system configuration and production processes to meet the market needs, and maintain the system values for generations of products. This paper examines the significance of developing such next generation manufacturing systems as the basis for futuristic sustainable living factories by adapting, integrating and implementing the RMS characteristics with the principles of sustainable manufacturing to achieve value creation for all stakeholders.