Smart Manufacturing Research Articles

Strategical and tactical supply chain optimisation for smart production planning and control 4.0

Industry 4.0 (I4.0) technologies generate new opportunities for developing smart models, algorithms and tools to support supply chain (SC) production planning and control (PPC), or smart PPC (SPPC 4.0). Paired with opportunities, challenges arise in integrating sustainability and resilience in SC network design (SCND) according under I4.0. The main novelty of this paper is to propose two multi-objective models that integrate strategical and tactical PPC decisions into a sustainable-resilient SC under I4.0 towards SPPC 4.0. Sustainability is incorporated in terms of reducing costs and CO2 emissions, and a job creation factor. Resilience is incorporated in terms of contracting support suppliers. To solve the multi-objective models, the augmented epsilon constraint method (AUGMECON) was used. This algorithm minimises a target objective function while it treats the others as constraints. Our experiments indicate that AUGMECON is sensitive to the choice of epsilon values. Furthermore, a synthetic data generation tool called SR1-SR2_SynthDataGenerator was developed. This tool generates input datasets to validate and evaluate our models against benchmarks. A stochastic model (SR2) is also proposed that, with small datasets, takes 8.39 seconds to solve. The proposed models can be useful for industries that seek sustainable and resilient SCs to adapt to changing environments.

DESIGN AND DEVELOPMENT OF A SMART FACTORY USING INDUSTRY 4.0 TECHNOLOGIES

This systematic literature review examines the operational and organizational impacts of Industry 4.0 technologies on smart factories, drawing on insights from 120 peer-reviewed articles published between 2010 and 2024. The study follows the PRISMA guidelines to ensure a transparent and rigorous review process, focusing on the key enablers of smart manufacturing, including cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning (ML). The findings reveal that smart factories offer significant benefits, including enhanced flexibility and customization, predictive maintenance that reduces downtime by up to 50%, and improved supply chain integration through real-time data sharing. Big data analytics plays a crucial role in optimizing operations by allowing factories to perform continuous real-time adjustments, improving efficiency and reducing resource waste. The review also highlights the evolving role of the workforce, with a growing need for technical skills and increased human-machine collaboration in smart manufacturing environments. However, challenges such as interoperability, cybersecurity, and the economic feasibility of large-scale smart factory implementations remain underexplored in the literature. Emerging technologies like blockchain and 5G offer promising solutions, but further research is required to assess their full potential. Overall, this review provides a comprehensive understanding of the current state of smart factory technologies and outlines key areas for future research, particularly in addressing gaps related to standards, workforce adaptation, and security concerns.

Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

Circular supply chain transformation: leveraging evolving technologies for enhanced performance

ABSTRACT Circular supply chains (CSC) and evolving technologies work together to enhance supply chain performance. To assess this, we examine the integration of technologies, such as Industry 4.0, smart manufacturing, and smart logistics in a CSC setup. The study focuses on two product types: smart and normal (non-smart) products. Smart products feature sensing, connectivity, networking, and monitoring capabilities, while normal products lack these features. Using a multi-objective optimisation technique, we analyse the CSC network to estimate the benefits of evolving technologies. The study aims to maximise profits and minimise carbon emissions across the supply chain. Eleven models are examined based on varying demand percentages of smart and normal products. The gamultiobj algorithm (a variant of NSGA II) is used to generate Pareto frontiers. The findings show that a 2-5% increase in technology adoption costs can boost profits by 15-30% and reduce emissions by 20-30% for smart products.

Smart Factory Operation Management System and Innovation in the Era of Smart Manufacturing

This study is dedicated to the innovative research of smart factory operation management systems in the era of smart manufacturing. Against the backdrop of the rapid evolution of the information revolution, smart manufacturing, represented by a new generation of information technologies such as mobile Internet, big data, and artificial intelligence, is rapidly emerging. As an information-physical fusion system, smart factories interpenetrate physical and virtual resources by interweaving multi-scale production systems, forming an open, complex giant system with multiple levels and dimensions. It is found that smart factories face a complex and variable structure, and their production and operations involve a large number of cross-domain, cross-industry, and cross-region manufacturing resources, requiring the establishment of a new operation management system framework and breakthroughs in key theories and methods. This study provides comprehensive and in-depth theoretical and technical support for constructing a smart factory operation management system. It lays the foundation for the smart upgrading of the manufacturing industry.

Anomaly detection in smart manufacturing: An Adaptive Adversarial Transformer-based model

In Industry 5.0, smart manufacturing brings additional intricacies and novel data processing challenges. Given the evolving nature of manufacturing processes and the inherent complexity of data, including noise and missing entries, achieving accurate anomaly detection becomes even more intricate. Conventional methods often miss nuanced anomalies, especially when dealing with high-dimensional, multivariate, non-stationary data. These data types are typical of smart manufacturing environments. Hence, many recent approaches have embraced deep learning to confront these challenges, making use of diverse attention mechanisms to acquire data representations. However, in manufacturing, where the dynamics of time series data change over time, methods relying solely on pointwise or pairwise representations often fall short. Thus, ensuring product quality and operational integrity calls for even more advanced methodologies. The deficiency lies in the capability of state-of-the-art models to effectively capture abnormal patterns while considering both local and global contextual information. This challenge is compounded by the rarity of anomalies, making it exceedingly challenging to establish substantial associations between individual abnormal points and the entire time series. To tackle these challenges, we introduce the “Adaptive Adversarial Transformer” as a novel deep learning technique that combines Transformer architecture with an anomaly attention mechanism and Adversarial Learning. Our Model effectively captures intricate temporal patterns, distinguishes normal and anomalous behaviors, and dynamically adjusts thresholds to align with the evolving dynamics of time-series data. Empirical validation on four benchmark datasets and three real-world manufacturing datasets demonstrates our model’s effectiveness compared to the state-of-the-art, as evidenced by the F1-Score.

Examining the factors affecting sustained use in smart interactive cabinet design: A comparative analysis using SEM and fsQCA

Understanding the factors that contribute to sustained user use is critical to the design and marketing of smart interactive cabinets, which have gained great popularity in recent years. This study investigates the factors that impact users' intentions to continue using smart interactive cabinets and examines how these factors influence product design and marketing strategies. However, existing research has mainly focused on the development and performance evaluation of smart interaction technologies, with less attention paid to users' continued willingness to use smart interaction cabinets and their determinants. To address this gap, 304 valid samples were randomly collected for empirical analysis and hypothesis testing, and a questionnaire survey was administered to 304 young Chinese urban Chinese users born in the 1980s and 1990s. By synthesizing and analyzing the usage patterns and influencing factors among young consumers, the study employs a combination of covariance-based structural equation modeling (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to evaluate the data.The analysis highlights the significance of perceived usefulness, ease of use, and innovation in predicting users' intentions to continue using smart interactive cabinets. This study offers insights into the factors influencing continued use and provides valuable guidance for manufacturers and marketers in designing and marketing smart interactive products to enhance user experience and satisfaction.

Accelerating smart manufacturing technology adoption: an interdependency perspective on technology adoption

ABSTRACT Adopting smart manufacturing technology (SMT) is critical for the intelligent transformation of manufacturing enterprises. However, an inappropriate SMT adoption (SMTA) path will lead to negligible economic benefits, which seriously restricts intelligent transformation. Using a combined network method based on the Grey-DEMATEL and social network analysis, this paper considers SMTA as a system and identifies the importance of individual SMTA. Firstly, the SMTA network is constructed by identifying 16 SMTs and calculating the interdependencies among individual SMTA. The results show that the SMTA system has a relatively high level of interdependency. The important rankings of SMTs are obtained by single and combined indicators. The roles of SMTA are divided into amplifier, absorber, carrier and neutral. Amplifiers play an important role in the adoption of other SMTs and the paths that successively pass through amplifiers can accelerate the whole SMTA. This paper provides a valuable contribution to SMTA theory from a technological interdependency perspective. This new perspective provides a foundation for designing SMTA paths for manufacturing enterprises and entire industries.

An integrated prototyping tool to enhance interdisciplinary communication in smart product design

The increasing integration of smart products into society is noteworthy in the field of Prospective Ergonomics (PE), as they herald the emergence of novel products featuring yet-to-be-discovered interaction modalities. Literature highlights a lack of front-end prototyping approaches that transcend disciplinary skills and aid communication across diverse design fields, highly relevant in early-stage smart product development. This paper introduces an integrated, low-fidelity prototyping tool to enhance interdisciplinary communication and understanding of future products’ interactive and environmental qualities. A survey provides preliminary validation of the integrated tool’s format and content. Findings endorse the tool’s structure and flexibility in supporting understanding and communication of interactive and environmental qualities of smart product concepts across a range of design disciplines. As such we advance the discourse on PE by envisioning a low-fidelity prototyping tool to create intermediary objects supporting design efforts towards future products and their new and emerging interaction modalities.

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

Learning-based production, maintenance, and quality optimization in smart manufacturing systems: A literature review and trends

With the introduction of manufacturing paradigms, including Industry 4.0, production research has shifted its focus to enabling intelligent manufacturing systems within industrial environments. These systems can efficiently schedule and control processes and operations using artificial intelligence methods, including machine learning and deep learning. Since 1995, relevant literature has presented several examples of such implementations, addressing topics, for example equipment fault diagnosis and quality inspections. To this end, the present paper strives to present a state-of-the-art review of the learning-based scheduling and control frameworks, which are exploited in the production research. The review is limited to the relevant research between the years 1995 and 2024, surveying approaches in the domains of manufacturing, maintenance, and quality control. To this end, the paper follows a meta-analysis method for the selection and evaluation of relevant research articles. Moreover, research questions are formulated to analyze the obtained findings and seek out insights on aspects of the relevant research, including the inclusion of decision-making models and dissemination of literature. The provided answers, among others, reveal trends and limitations of the state-of-the art research in relation to learning-based scheduling and control.

The Impact of Industrial Robot Applications on Global Value Chains Division Position

This paper surveys the GVC division position across forty-five countries and fourteen manufacturing sub-sectors during the period from 1999 to 2018 to illustrate the impact of applications of industrial robots on the GVC position. The study proves that an increasing level of utilization of industrial robots significantly improves the GVC division position in the manufacturing sector. By automating repetitive and precision tasks, robots enhance efficiency and productivity, therefore allowing deeper specialization in specific stages of the manufacturing process. This technological progress allows for a more connected global production network and hence better integration into GVC. The study also evaluates the spillover effects across value chains and the moderating impact of industrial agglomeration on smart manufacturing.The empirical results show that the application of industrial robots enhances productivity and encourages innovation in related sectors, thereby driving downstream industry growth and promoting industry specialization.Despite the improvement in GVC division positions, robots also contribute to making global value chains more diversified and resilient. Flexible and adaptive manufacturing systems can adapt to disruptions, such as pandemics and geopolitical tensions, ensuring continuity in production and increasing stability and competitiveness in smart manufacturing systems.

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.

IoT and Edge Computing for Smart Manufacturing: Architecture and Future Trends

The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies. Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.

Application of digital twin for industrial process control: A case study of gauge-looper-tension optimized control in strip hot rolling

Background During the hot rolling process, the performance of most control systems gradually degrades due to equipment aging and changing process conditions. However, existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies. Methods To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling, based on the digital twin method, this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling. First, a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform. Subsequently, relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process. Additionally, for controllers with poor Hurst index values, the crayfish optimization algorithm is employed for adjusting controller parameters to maximum the Hurst index. Results A real case of hot rolling steel production was used to validate the proposed data-driven optimized control method. Experimental results demonstrate that the proposed evaluation method can effectively recognize the state of gauge-looper-tension controller. Moreover, after optimizing the controller parameters through crayfish optimization algorithm (COA), the value of Hurst index showed significant improvement. Conclusions The proposed digital twin -based optimized control method effectively enhance the manufacturing capability and maintain strategy of hot rolling steel production. Through the proposed method, the Hurst index of gauge-looper-tension system increasing from 0.574 to 0.862.

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

AbstractSelf-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates interoperable communication from adaptive manufacturing control as isolated areas of study. To address this gap, this paper introduces Knowledge Graph-enhanced Multi-Agent Reinforcement Learning (MARL) method that integrates interoperable communication via Knowledge Graphs with adaptive manufacturing control through Reinforcement Learning. We hypothesize that implicit domain knowledge obtained from historical production job allocation records can guide each agent to learn more effective scheduling policies with accelerated learning rates. This is based on the premise that machine assignment preferences effectively could reduce the Reinforcement Learning search space. Specifically, we redesign machine agents with new observation, action, reward, and cooperation mechanisms considering the preference of machines, building upon our previous MARL base model. The scheduling policies are trained under extensive simulation experiments that consider manufacturing requirements. During the training process, our approach demonstrates improved training speed compared with individual Reinforcement Learning methods under the same training hyperparameters. The obtained scheduling policies generated by our Knowledge Graph-enhanced MARL also outperform both individual Reinforcement Learning methods and heuristic rules under dynamic manufacturing settings.

PolyPhasic Sleep: Effects on a Human Body

The purpose of this paper is to assess the impact of polyphasic sleep on the level of productivity, cognitive skills and health in general. The studies were conducted for 6 months, during which we experimented on ourselves within the framework of three polyphasic sleep patterns: Uberman, Everyman and Dymaxion. The target was to find out if polyphasic sleep could increase efficiency without affecting health, both mentally and physically. Well-known personalities like Nikola Tesla, Leonardo da Vinci, Thomas Edison, etc. to name a few are frequently mentioned when discussing the benefits of polyphasic sleep, but there is scanty documentation backing up the effects of the phenomenon in the modern day. Therefore, this study attempted to do just that by assessing the burden placed on cognitive, emotional and physical health by each of those sleep schedules having employed lots of instruments such as smart watches, productivity applications, and cognitive tests.

Linking smart manufacturing technologies and sustainable corporate performance: evidence from emerging economy

Linking smart manufacturing technologies and sustainable corporate performance: evidence from emerging economy

An Integrated Smart Manufacturing System for Customer Design Experience

This study focuses on the development of an integrated smart manufacturing system (ISMS) that centers on custom design experiences, utilizing virtual reality (VR) and cloud integration to enhance operational value. Through a Design for Excellence (DFX) approach, this system aims to seamlessly incorporate customer-specific designs into the manufacturing process, thus aligning closely with consumer needs. The research contributes significantly by (1) implementing a customer-focused design system that enhances sales benefits, (2) enabling a smooth transition from design drawings to automated production, thereby increasing manufacturing efficiency, and (3) establishing a digital transformation framework that integrates design, production, and marketing to boost overall business value.