Smart Manufacturing: Characteristics and Technologies

Chapter 4 - Analysis of factors influencing smart and sustainable manufacturing systems using a multi-criteria decision making tool

The research proposes a metric referred to as Smart Manufacturing Performance Measures (SMPMs), which are mapped with manufacturing decisions (structural and infrastructural) and manufacturing outputs (cost, quality, delivery, flexibility, and innovativeness). The mapping between manufacturing decisions, manufacturing outputs, and SMPMs, facilitates the organization in choosing SMPMs to deliver desired output as represented by the competitive priority. The approach provides a direction for the practitioners to implement and operate smart manufacturing systems efficiently and effectively by devising a performance measurement system. A total of 22 performance measures are identified through an in-depth literature review. These are further classified into hard and soft measures, which allow decision-makers to measure SMPMs and focus on a specific performance measure. Further, SMPMs is linked to 13 enablers, which are the driving technologies for implementing smart manufacturing systems. Smart manufacturing is evolving, and its implementation is still in the nascent phase. The proposed approach plays a vital role in redefining the manufacturing strategy while deploying smart manufacturing systems. The research contribution is a step-in assessment of smart manufacturing to understand the ‘what’ and ‘how’ of technological changes are transforming the operations and driving outputs.

Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

A framework for human-centered production planning and control in smart manufacturing

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.

Cyber security is still the main argument in any system development lifecycle that needs more concern. There are still numerous challenges associated with conducting a threat modeling approach for smart manufacturing systems. One of these challenges is the lack of a threat database based on the expertise of highly skilled researchers in this field. Hence this study attempts to address this gap by developing a components catalog and a rule-based threat database to address potential security threats in smart manufacturing systems saving time and effort. Specifically, it performs STRIDE-based threat modeling against a smart factory use case using Microsoft Threat Modelling Tool. The threat evaluation process is conducted with this research to determine the severity of threats and give a preliminary estimation of the overall system's risk.

In this paper, the authors proposed a set-theoretic model of the functioning of Smart Manufacturing (SM) system. As part of the work, an analysis of existing scientific works in the subject area was carried out. We focus on the description of the model of SM functioning as a cyber-physical system, as a result of which the physical and informational level of the model functioning are considered. The conditions and tasks for the SM functioning are defined, various types of representation of the information space are considered, and the system of access control to elementary messages is described. As one of the components of digital production, a description of the logistic system was proposed and the main parameters of its functioning were determined.

Today's manufacturers face ever-increasing demands of variability-greater customization, smaller lot sizes, sudden supply-chain changes and disruptions. Successful manufacturers will have to choose and incorporate technologies that help them quickly adapt to rapid change and to elevate product quality while optimizing use of energy and resources. These technologies form the core of an emerging, information-centric, Smart Manufacturing System that maximizes the flow and re-use of data throughout the enterprise. The ability of disparate systems, however, to exchange, understand, and exploit product, production, and business data rests critically on information standards. This report provides a review of the body of pertinent standards-a standards landscape-upon which future smart manufacturing systems will rely. This landscape comprises integration standards within and across three manufacturing lifecycle dimensions: product, production system, and business. We discuss opportunities and challenges for new standards, and present emerging activities addressing these opportunities. This report will allow manufacturing practitioners to better understand those standards useful to integration of smart manufacturing technologies.

Developed countries and global manufacturing enterprises are leading the way for developing smart manufacturing systems (SMS) to improve competitiveness and possibly make technological breakthroughs. SMS is based upon the integration of information and communication technology with manufacturing technology; and all the heterogeneous technologies must be seamlessly connected. However, there is a lack of guidance in what technologies should be deployed and how they may be used. This paper introduces a reference activity model and describes various ways in which it can be used as guidance for deploying smart manufacturing technology.

The Industrial robots are widely used in smart manufacturing environments to improve productivity and automation, but their increasing energy consumption has become a critical issue. Traditional time-based control methods update control signals at fixed sampling intervals, which often leads to actuator redundancy and energy inefficiency, especially in steady-state or low-dynamic operating modes. To address this issue, this paper proposes an energy-efficient event-based control strategy for energy-efficient operation of industrial robots in smart manufacturing systems. The proposed approach integrates an event-based mechanism with nonlinear robot control so that control adjustments are made only when tracking accuracy or energy-related metrics exceed specified thresholds. This design significantly reduces unnecessary control signals while ensuring satisfactory trajectory tracking accuracy. A Lyapunov function-based analysis is performed to demonstrate the stability of the closed-loop control system using the proposed event-based method. Furthermore, the triggering mechanism guarantees a strictly positive lower bound for the intervals between events, thereby eliminating the Zeno effect and ensuring practical feasibility. The energy efficiency of the proposed strategy is further investigated by analyzing the behavior of the actuators and the overall energy consumption. Experimental results show that the proposed approach achieves significant energy savings compared to traditional time-based control methods without compromising control performance. This highlights its potential for energy-efficient operation of industrial robots in smart manufacturing applications.

In the manufacturing industry, technologies of The Fourth Industrial Revolution have been integrated into Smart Factory and is evolving into an intelligent factory. In other words, smart factories create an optimal economic production environment by exchanging information through the Internet of Things and by providing real-time monitoring, analysis of all processes. The recent decline in birthrate, aging population and rising labor costs require automated production plants. At the same time, the more diversi- fied demands of consumers become, the shorter the life cycle of products becomes and the capability of customized mass production has emerged as the core of competitiveness. Therefore, smart factories are an essential product of innovation to adapt to these socioeconomic changes. In addition, smart factories provide breakthrough results such as high quality, reduced defect rate and improved productivity. In this study, we will first explore the cases of the operations of German automated factories, the place of origin of the Fourth Industrial Revolution and the world’s strongest manufacturer, to derive elements that can be benchmarked to Korean small and medium-sized enterprises (SME’s). Based on this, the ultimate purpose of this study is to suggest implications for the governmental projects supporting compa- nies that want to build, maintain and spread smart factories in Korea. In this study, prior literary studies on domestic and foreign papers and research reports and empirical case studies through corporate visits were used as research methodologies. While the existing research is an indirect study through domestic and international literature or information on the Internet, this study is differentiated in that it is an empirical case study of visiting demonstration. The implication is that in terms of corporate strategy, smart factories should first commence with manufacturing processes that require automation and then spread them sequentially in line with the corporate management environment (financial, market competition structure, and growth potential). Furthermore, various information and new technologies in the smart manufacturing process should be actually integrated to create optimal synergy, and various internal and external management resources such as human resources and environmental energy should be planned and implemented in an integrated manner. In terms of policy support, the focus should be on customized modular support that companies need. At the same time, it is also necessary to standardize the process of smart manufacturing through joint cooperative support for smart factories among the same companies and induce mutual utilization of generated data, minimization of use costs and maximum synergy efficiency. In particular, it is necessary to seek ways to utilize idle smart factories when ordering small or excessive amounts of orders. At the same time, expansion of smart factory supporting platforms and managemental aid are also required through a consortium between professional companies that supply new technologies to smart factories (e.g. Robots, Sense, AI, Total solution SW, VR etc.). Environment-friendly smart manu- facturing and optimal utilization of energy and materials should also be considered comprehensively. To this end, it is necessary to have a smart management organization, or a research advisory group, that studies human and environment-friendly smart manufacturing within the “Smart Manufacturing Innovation Promotion Group” to induce social innovation. It will be necessary to minimize the waste of policy support by thoroughly managing and supervising the implementation of the beneficiary entities. Finally, support for the introduction and application of smart factories for SME’s and Midsize Business should be strengthened to promote the smart manufacturing of SME’s to increase their competitiveness.

Smart manufacturing and energy systems

This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.

Condition monitoring systems conduct important roles in manufacturing process lines by monitoring condition status of machines to prevent accident faults of them. Those systems need to be improved in their functionalities by adapting more intellectual, self-reconfigurable, and self-decisional mechanisms for supporting smart manufacturing operations. Whereas, many enterprises in manufacturing industry have in trouble with well-designing these smart condition monitoring systems for fitting their own purpose in their specific domains. In this paper, we propose a systems engineering approach to designing smart condition monitoring systems. The systems engineering is powerful methodology to design complex systems in many multi-disciplinary domains, so we have tailored the systems engineering processes for designing the system architecture of our system-of-interest (i.e. smart condition monitoring systems). The deliverables of this research work can contribute to provide a technical solution for well-designing other smart manufacturing systems in several industrial domains. In the end, our research works can resolve the market domains such as high-productivity and production of high-quality products in manufacturing process lines.