Smart manufacturing and energy systems

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

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 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.

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.

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

The purpose of this paper is to collect and structure the various features of Smart Manufacturing (SM). Researchers have previously identified various characteristics and technologies of Smart Manufacturing System (SMS); this paper collects, discusses and merges some of those characteristics and technologies available in the current body of knowledge. In the future, it is expected that this selection of characteristics and technologies will help to compare and distinguish other initiatives like Industry 4.0, smart factory, intelligent manufacturing, distributive manufacturing, etc. which are frequently used synonymous with SM. The result of this paper is a comprehensive list of characteristics and technologies that are associated with a SMS. As many of the listed items show variating overlaps, certain technologies and characteristics are merged and clustered. This results in a set of five defining characteristics and ten technologies that are considered relevant for a SMS. The authors hope to provide a basis for a broad and interdisciplinary discussion within the SM community about the defining technologies and characteristics of a SMS.

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.

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.

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.

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

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.

As a key part of digital transformation, digital twin is an important concept for achieving smart manufacturing. However, there remains many challenges about the digital twin concept and how it can be implemented in real manufacturing systems, especially among small and medium-sized enterprises. Relevant standards that provide common terminology and implementation guidelines can help address these challenges. This paper discusses the concept, definitions, applications, and recent standardization efforts of digital twins. In particular, International Organization for Standardization (ISO)/Draft International Standard (DIS) 23247, Digital Twin Framework for Manufacturing, is introduced. The scenarios of three digital-twin use cases in manufacturing are explored in depth and the procedures of implementing such use cases according to the ISO/DIS 23247 framework are presented. The designs of these use case scenarios can be implemented at the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems (SMS) Test Bed or on any manufacturing shop floor that has similar maturity level of digital transformation. In addition, the procedures of design and implementation of ISO/DIS 23247 can be used for developing additional applications of digital twins in manufacturing.

Industry 4.0 represents a new phase in the industrial revolution characterized by the integration of digital technologies such as IoT, AI, big data, and automation into manufacturing systems. Smart manufacturing systems (SMS) are central to Industry 4.0, enabling real-time data exchange, predictive analytics, and decentralized decision-making. This article explores the development and implementation of smart manufacturing systems, highlighting the key technologies that drive their success. We also examine the benefits and challenges associated with the transition to smart manufacturing, as well as the future potential of these systems in creating more efficient, flexible, and sustainable manufacturing environments.

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.

Smart manufacturing (SM) systems have to optimise manufacturing activities at the machine, unit or entire manufacturing facility level as well as adapting the manufacturing process on the fly as required by a variety of conditions (e.g. machine breakdowns and/or slowdowns) and unexpected variations in demands. This paper provides a framework for autonomic smart manufacturing (ASM) that relies on a variety of models for its support: (1) a process model to represent machines, parst inventories and the flow of parts through machines in a discrete manufacturing floor; (2) a predictive queueing network model to support the analysis and planning phases of ASM; and (3) optimisation models to support the planning phase of ASM. In essence, ASM is an integrated decision support system for smart manufacturing that combines models of different nature in a seamless manner. As shown here, these models can be used to predict manufacturing time and the energy consumed by the manufacturing process, as well as for finding the machine settings that minimise the energy consumed or the manufacturing time subject to a variety of constraints using non-linear optimisation models. The diversity of models used affords different levels of integration and granularity in the decision-making process. An example of a car manufacturing process is used throughout the paper to explain the concepts and models introduced here.