•Identified 16 specific requirements for manufacturing Small and Medium-sized Enterprises (SME).•Literature review of 15 Smart Manufacturing / Industry maturity/assessment models.•Identified specific ...research gaps by matching SM maturity models to SME requirements.
The objective of this paper is to critically review currently available Smart Manufacturing (SM) and Industry 4.0 maturity models, and analyze their fit recognizing the specific requirements of Small and Medium-sized Enterprises (SMEs). To this end, this paper presents features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0. The results of this study show that only a limited number of the SM and Industry 4.0 roadmaps, maturity models, frameworks and readiness assessments that are available today reflect the specific requirements and challenges of SMEs. The main findings include: (1) the current standard starting “level 1″ (base level) of most maturity models appears to be disconnected from the real digitization and smart manufacturing maturity level of many SMEs. Therefore, we propose a “level 0″ specifically designed to reflect the ‘real - base level’ for SMEs; (2) the transition from this new base level, “level 0″, to the current standard “level 1”, requires significant effort including a mind-set change; (3) maturity models and readiness assessments can be associated with an SM toolkit, and (4) SMEs need to develop their own, unique SM or Industry 4.0 vision and roadmap. This study provides insights that help towards developing a realistic SM (Industry 4.0) maturity model for SMEs that reflects their industrial realities more accurately. With the help of SM maturity models that are more customized to the SME specific requirements, the SMEs’ stakeholders will be able to better define their SM (Industry 4.0) vision, roadmap, and strategic projects. It will ultimately lower the entry barrier and reduce the risk of the transition process towards SM and Industry 4.0 and support the critical change in culture. Summarizing, we identified manufacturing SMEs’ specific requirements, conducted a literature review of current SM maturity models, and discussed how these maturity models reflect the SME specific requirements.
•Methods to construct high-fidelity digital twin for automation systems is introduced.•Network of interfaces enabling communications among system components is built.•Manufacturing intelligence is ...realized by training Deep Reinforcement Learning.•A smart dynamic scheduler is developed for continuous process optimization.
Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.
•Digital twins technologies could promote the smart manufacturing system design (SMSD).•A Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework for SMSD.•The definitions, ...frameworks, models, enabling technologies, cases, and research directions of digital twins-based SMSD.
A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.
Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real ...manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.
Digital Twin in Industry: State-of-the-Art Tao, Fei; Zhang, He; Liu, Ang ...
IEEE transactions on industrial informatics,
04/2019, Letnik:
15, Številka:
4
Journal Article
Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and ...physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.
This work makes the case for the integration of the circular economy (CE) and large-scale data (LD), also known as big data. The paper is one of the first to integrate conceptual and practical trends ...regarding: (a) the ReSOLVE based models of the circular economy; (b) key stakeholders roles in pursuing a more sustainable society; and (c) the volume, velocity, variety, and veracity (4V's) of large-scale data (LD) management. This study's contributions include: (1) introducing a new integrative framework to enhance the understanding of the CE-LD nexus; (2) a relational matrix which illustrates the complexity of large-scale data and stakeholders management; and (3) a research agenda, with clear research propositions and future research direction. The proposed CE-LD integrative framework provides socio-technical insights for academics, practitioners, managers, and policy decision-makers.
•This paper integrates the disparate fields of the circular economy (CE) and large-scale data (LD).•Discusses how large-data can support the circular economy's business models.•An integrative framework for CE-LD is proposed.•A research agenda for the circular economy and large-data integration is proposed.•Implications for challenges and opportunities of sustainable production in a digital world.
Digital twin in smart manufacturing Li, Lianhui; Lei, Bingbing; Mao, Chunlei
Journal of industrial information integration,
March 2022, 2022-03-00, Letnik:
26
Journal Article
Digital twin creates the virtual model of physical entity in digital way, promotes the interaction and integration of physical world and information world, and builds a reliable bridge for industrial ...information integration. With the rapid evolution of digital twin, the application of digital twin has found an increasingly wide utilization in smart manufacturing. In view of the practical problems encountered by the current smart manufacturing enterprises, this paper aims to carry out quantitative green performance evaluation of smart manufacturing (GPEoSM) driven by digital twin-based industrial information integration system. Based on the mapping between entity and model of smart manufacturing projects, the integration of digital twin information and the interaction of GPEoSM approach, a GPEoSM framework is constructed. According to the framework, a green performance evaluation case for smart manufacturing project of an air conditioning enterprise is carried out. The result shows that the digital twin driven GPEoSM framework is effective and enhances the green performance evaluation of smart manufacturing.
Inherent complexities in pharmaceutical manufacturing lines of modern industrial facilities make precise and timely detection of malfunction occurrences necessary. In fact, unpredicted malfunctions ...in a production line can often provoke a cascade of adverse effects that can occur everywhere in the production chain bringing the manufacturing line to a halt for undefined time periods. Such events can have unfortunate consequences that are not always confined to the damaged part itself but propagate throughout the production line. Nevertheless, modern production lines are equipped with a multitude of data sensors that enable the real-time and fine-grained monitoring of each constituent part of the production process providing a richness of information that can be exploited by intelligent data processing methods.
In this work, we present ManuTrans, a deep learning-based model for monitoring real-time raw sensor data, deriving the condition of a pharmaceutical manufacturing line and predicting the next moment in time when a malfunction can occur. The model is further able to predict the severity of the next malfunction and can contribute adjunct information in corporate decision-making. The suggested approach exploits the capacity of deep transformer models for extracting both long- and short-term correlations as well as patterns in sequential data and, combined with a linear output layer, conducts both classification and regression. The proposed approach was tested on a real dataset comprising raw data from two manufacturing lines, and it achieved promising results.
Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic ...user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.
The paper outlines key characteristics of smart manufacturing, data-driven, networked, connected, resource sharing, resilient, and sustainable. Manufacturing resiliency and sustainability have ...received limited attention in the literature and they are the focus of this paper. Both are related and offer challenges that may become differentiators of smart manufacturing. Resiliency provides businesses with defenses against natural and human caused adversities. The list of attributes provided in the paper is intended for comprehensive assessment of manufacturing resiliency. Solutions are needed to make businesses more resilient and sustainable. Research on business models equating sustainability with an industrial activity is suggested. A scheme for labeling environmental friendliness of materials makes as a token contribution to sustainability.
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