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In recent years, the topic of smart manufacturing systems (SMS) has become the focus of attention for researchers and production experts because it enables intelligent optimization of production processes. Enterprises have started to use SMS and technologies to develop complex products, accurately predict customer needs, minimize production costs, increase flexibility in production, and analyze risks. However, enterprises needs more knowledge about the requirements and features which should be in place for SMS. Customizing SMS is more costly and takes more time than traditional manufacturing. For this reason, the system must be considered in its entirety during the design process and the requirements must be met. The features of the SMS technology to be used must also be determined during the design process. In this study, 6 main and 30 sub-features of the SMS are defined to enable its implementation. The objective is to analyze the impact of these features on the SMS technology. The weighting coefficients of the defined main and sub-features were calculated using the Fuzzy Full Consistency Method (F-FUCOM), one of the multi-criteria analysis (MCA). Later, these coefficients were used in the Fuzzy Measurement Alternatives and Ranking according to COmpromise Solution (F-MARCOS) method to determine SMS technologies. The analysis results provide some important information for companies planning to switch to the intelligent production system. When examining the results related to the main criteria, it was found that the best ranking was Internet of things (IoT), the second best ranking was cyber-physical systems (CPS), and the third best ranking was big data. For the sub-criteria, the best score was CPS, the second best score was IoT, and the third best was big data. Overall, the results show enterprises should prioritize IoT, CPS, and big data.

Various countries in the world have issued policies on smart manufacturing and put forward relevant system architecture, including its hierarchy and component elements. However, from the perspective of implementation, it is necessary to further expand and study the relationship between the elements of the framework and the specific application scenarios. The focus of this study is to plan the specific implementation schemes from top-level planning for industrial practice in smart manufacturing on the basis of existing resources, elements, foundations, environment, standards, and norms. In this paper, industrial application scenarios for Smart Manufacturing Implementation System (SMIS) are deduced through a framework of SMIS. Then, an evaluation method for SMIS using Fuzzy DEMATEL is proposed. This study can provide guidance for enterprises to implement the scheme and steps of smart manufacturing. In addition, the industrial scenarios of SMIS proposed in this paper can provide reference for enterprises’ top-level planning.

Purpose: This study addresses the gap in existing Industry 4.0 (I4.0) readiness models, which overlook the foundational role of lean manufacturing (LM) in enabling digital technology adoption. It explores how lean capabilities enhance I4.0 readiness, supporting manufacturing firms leveraging their prior investments in LM for digital transformation.Design/methodology/approach: This study adopts a systematic literature review (SLR) of 20 I4.0 readiness models to investigate the extent to which LM concepts are incorporated. To reinforce the analysis, a content analysis was conducted using a comprehensive spreadsheet that captured each model’s dimensions. Key readiness dimensions were synthesised alongside assimilated lean concepts. Based on this analysis, a novel lean-centric I4.0 readiness model was developed, comprising six core dimensions and 39 sub-dimensions, which reflect essential lean capabilities aligned with digital transformation.Findings: The findings reveal a substantial gap in existing I4.0 readiness models in explicitly incorporating core LM concepts. The proposed lean-centric I4.0 readiness model emphasises the need to balance technology, organisational, and process maturity enablers, contrary to models prioritising technological factors alone.Research limitations/implications: The review is based on the published literature on manufacturing where LM practices may have different degrees of adoption in different manufacturing sectors. Also, the review was confined to the literature published in English, which may limit the generalisability to other regions. Future research can validate this model empirically across diverse manufacturing contexts.Practical implications: The novel lean-centric I4.0 readiness model developed in the study enables manufacturing firms to assess readiness through a lean lens. It can also aid their understanding of how their existing LM practices enable digital transformation readiness. The model facilitates strategic decision-making, resource allocation, and priority setting, reducing the risk of digital transformation failures.Originality/value: This is the first study to examine I4.0 readiness models for the extent of integration of LM concepts. The proposed lean-centric I4.0 readiness model with a range of dimensions and sub-dimensions enriches the in-depth integration of LM and I4.0 literature. It offers a foundation for further empirical studies on lean-centric readiness assessment.

This study investigates the application of a smart manufacturing execution system (SMES) based on the current controlling structure in a medium-sized company in the Czech Republic. Based on existing approaches on the architecture of SMESs, this paper develops a sample architecture grounded in the current controlling structure of small and medium-sized enterprises (SMEs). While only a few papers on approaches to the given topic exist, this approach makes use of operative production controlling data and uses a standardisation module to provide standardised data. The sample architecture was validated with a case study on a Czech SME. This case study was conducted on two different entities of one production company suggesting differences in the entities due to the nature of production. The research showed that simple tasks with intelligent welding equipment allow for a working SMES architecture, while complex assembly works with a high extent of human labour, and a high number of components still remain an obstacle. This research contributes to gathering more understanding of SMES architectures in SMEs by making use of a standardisation module.

Purpose: This paper aims to provide a flexible solution to include additive manufacturing into a process chain complying with Industry 4.0 pillars, overcoming major drawbacks in terms of reliability and experimental effort. Design/methodology/approach: The study is based on the combination of real experimental activities and simulated ones. Findings: The main findings of this work consist into validation of the proposed process chain, which proves to be effective in terms of process flexibility (additive manufacturing, burnishing and process simulation acting synergistically), cost and time reduction and final output quality, encouraging customer involvement towards customization. Originality/value: This paper contributes to current research on the application of burnishing process, an easy to implement and environmentally friendly post-processing method to improve the performance of AM products, by providing a unique perspective integrating a reliable simulation model. Other researchers can employ these outcomes towards manufacturing of the future.

The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from the Web of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment.

Along with the change of global economic landscape and the development of manufacturing technologies, cyber–physical-system-integrated smart manufacturing system has become a general solution for both developed and developing countries to upgrade their manufacturing industries. To bridge the gap from those theories developed without much practice to those strategies put forward in recent years by typical countries, a framework of smart manufacturing workshop is proposed in this work, trying offer a possible solution in workshop level to the intellectualization of manufacturing processes. To measure the unbalanced development levels, a comprehensive model for quantitative capability maturity evaluation of smart manufacturing workshops is developed, providing directive guidelines and roadmap for the transformation of manufacturing companies. A complete and detailed application of automotive body-in-white manufacturing is also given to demonstrate the implementation and potentials of the framework.

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and deployment onto constrained edge devices. This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The review explores the key architectural advancements proposed at each iteration, followed by examples of industrial deployment for surface defect detection endorsing its compatibility with industrial requirements.

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.