Development of Smart Manufacturing Systems for Industry 4.0

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

Successful implementation of smart manufacturing systems requires tight connectivity and intensive information and knowledge exchange. There is still a need for achieving certain level of smartness and beyond (i.e. cognitive manufacturing) by identifying semantic relations between heterogeneous data sources, extract actionable information and subsequently automate the process of knowledge generation and target-oriented recommendation in industrial context. The missing linkage is the quality of knowledge generation and protection process, which requires further investigation along with standard planning and controlling measures. This can be achieved through application of ontologies as a mean to support interoperability, reasoning and decision making. Considering various alternative approaches, this paper describes a research framework for ongoing study on ontology-based planning and controlling in smart manufacturing systems..

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

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

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 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.

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.

Smart manufacturing and energy systems

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.

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

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.

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.

The utilization of artificial intelligence (AI) techniques for the scheduling and sequencing of manufacturing systems has been investigated. These studies have included respective comparisons with "traditional" techniques. In addition, these studies have resulted in the development of an effective scheduling and sequencing methodology that considers the interrelation among the different elements of the manufacturing system. This methodology integrates neural networks, genetic algorithms, and operations research (OR) techniques. This methodology has evoked interest from industry and has the potential to support the development and implementation of agile manufacturing systems. This paper describes the methodology and the initial efforts toward the development of a two-level scheduling prototype.

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.