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The industry has embraced digitalisation leading to a greater reliance on models derived from data. Understanding and getting insights into the models generated by machine learning algorithms is a challenge due to their non-explicit nature. Explainable artificial intelligence (XAI) is to enhance understanding of the digital models and confidence in the results they produce. The paper makes two contributions. First, the XRule algorithm proposed in the paper generates explicit rules meeting user's preferences. A user may control the nature of the rules generated by the XRule algorithm, e.g. degree of redundancy among the rules. Second, in analogy to federated learning, the concept of federated explainable artificial intelligence (fXAI) is proposed. Besides providing insights into the models built from data and explaining the predicted decisions, the fXAI provides additional value. The user-centric knowledge generated in support of fXAI may lead to discovery of previously unknown parameters and subsequently models that may benefit the non-explicit and explicit perspectives. The insights from fXAI could translate into new ways of modelling the phenomena of interest. A numerical example and three industrial applications illustrate the concepts presented in the paper. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

In four-dimensional additive manufacturing (4DAM), specific external stimuli are applied in conjunction with additive manufacturing technologies. This combination allows the development of tailored stimuli-responsive properties in various materials, structures, or components. For shape-changing functionalities, the programming step plays a crucial role in recovery after exposure to a stimulus. Furthermore, precise tuning of the 4DAM process parameters is essential to achieve shape-change specifications. Within this context, this study investigated how the structural arrangement of infill patterns (criss-cross and concentric) affects the shape memory effect (SME) of compression cold-programmed PLA under a thermal stimulus. The stress–strain curves reveal a higher yield stress for the criss-cross infill pattern. Interestingly, the shape recovery ratio shows a similar trend across both patterns at different displacements with shallower slopes compared to a higher shape fixity ratio. This suggests that the infill pattern primarily affects the mechanical strength (yield stress) and not the recovery. Finally, the recovery force increases proportionally with displacement. These findings suggest a consistent SME under the explored interval (15–45% compression) despite the infill pattern; however, the variations in the mechanical properties shown by the stress–strain curves appear more pronounced, particularly the yield stress. [ABSTRACT FROM AUTHOR]

In the context of global economic integration and Industry 4.0, digital manufacturing has become crucial. As one of the economic cores of China, the digitization process of the manufacturing industry in the Yangtze River Delta is particularly critical to the overall economic growth. Based on the theory of Industry 4.0 and digital manufacturing, this study deeply analyzes the current digital development of the manufacturing industry in the Yangtze River Delta. More importantly, this paper successfully constructs a fuzzy control model to quantitatively evaluate and guide the process of digital transformation of manufacturing industry in this region. The empirical results of the model reveal how key factors such as capital, talent, technology and data security affect the digitization process, and provide concrete and operational transformation strategies for the Yangtze River Delta region. In addition, combined with industrial advantages, policy support, technological progress and market demand, this paper predicts the digital development prospects of the manufacturing industry in the Yangtze River Delta. Overall, the study not only provides in-depth insights on the digitization of manufacturing in the Yangtze River Delta, but also provides practical guidance for actual operation, which has high theoretical and practical value. [ABSTRACT FROM AUTHOR]

Precise material architectures and interfaces can render conventional ceramics tough, deformable and damage-resistant, offering a wide range of possibilities beyond those provided by conventional ones. This study explores the mechanical properties of topologically interlocked ceramic panels by systematically varying architectural parameters (i.e., interlocking angles and building block sizes) using a combination of experimental testing and finite element modeling based on COMSOL Multiphysics®. Three distinct designs were fabricated using digital laser manufacturing, categorized as designs with constant interlocking angles and tile sizes, constant interlocking angles and variable tile sizes, and variable interlocking angles and tile sizes and subjected them to out-of-plane quasi-static loads. The results show substantial enhancements in toughness (up to 110%) and strength (up to 120%) for ceramics with varying building block sizes, compared to those with constant block sizes. Additionally, these ceramics exhibit increased flexibility (up to 130%) compared to plain ceramics. Larger interlocking angles contribute to superior mechanical properties, fostering increased energy absorption and stability. The study also investigates post-failure behavior, revealing that increasing length ratios consistently augment the energy absorption. This research highlights the potential of these materials for flexible protection and the importance of controlling architectural parameters based on the interlocking angle and building block size to optimize performance while minimizing damage. [Display omitted] • Enhanced toughness, strength, and stiffness in variable-sized ceramic panels. • Novel digital laser manufacturing for controlled architectural parameters. • Precise interlocking angles and building block sizes optimize ceramic performance. • Experimental and finite element modeling unveil superior mechanical properties. • Potential applications in flexible protection, emphasizing damage resistance. [ABSTRACT FROM AUTHOR]

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. [ABSTRACT FROM AUTHOR]

Digital transformation and emerging technologies create new business opportunities that differ from traditional requirements. Digital twins, referring to digital replicas of a physical entity, are rapidly developing and creating novel innovation opportunities. This study focuses on increasing the understanding of the implications of the use of digital twins for innovation, and case studies were used to gather evidence. The study offers an interesting contribution to the literature on digital twins, presenting a framework that demonstrates how digital twins are characterised, and how they contribute (impact) and are used (scope) in innovation processes in the context of innovation and technology management. In addition, the findings provide interesting implications for different practitioners interested in the utilisation of digital twins. Using the results of this study, managers can enhance their companies' digital transformation by acknowledging the multiple uses of digital twins. [ABSTRACT FROM AUTHOR]

The main objective of this research is to identify current trends in Industry 4.0 within the manufacturing sector through bibliometrics. A dataset of 1069 documents from 2020 to 2024 obtained from the Web of Science is processed. Using the R-Bibliometrix package, research trends, leading authors, and institutional contributions are identified. The accelerated growth rate of 30.77% in publications confirms research interest. Thematic exploration reveals the convergence of Industry 4.0 with sustainability, AI, the Internet of Things, smart manufacturing, and digitalization as dominant themes. The transition towards smarter and more efficient systems is evident, with an emphasis on integrating sustainability into Industry 4.0 practices. Challenges persist in management adjustment, technological integration, and strategy for digital transformation. The study identifies sustainability and machine learning as critical enabling factors for Industry 4.0, while security and collaboration have emerged as key focus areas in recent years. A wide geographic distribution of research contributions with substantial international cooperation is observed, highlighting India, Italy, and China. Major journals like Sustainability and Journal of Manufacturing Systems emerge as influential platforms for disseminating research on the topic. The analysis of citation networks, co-occurrence, and thematic evolution underscores the multidimensional impact of Industry 4.0 technologies on manufacturing. [ABSTRACT FROM AUTHOR]

Purpose – Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals. Design/methodology/approach – The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”. Findings – A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO2) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses. Originality/value – The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO2 emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

Prosthetic socket manufacturing is experiencing a revolutionary shift towards using digital methods, such as 3D scanning and 3D printing. However, using digital methods requires the clinician to transfer their skills from making sockets by hand to making sockets with a computer. This shift in practice may change the socket geometry and fit; however, to what extent is unknown. Thus, the aim of this study was to explore the feasibility of analyzing geometric and clinical differences between digitally- and manually-designed sockets. Nine adult inpatients with below-knee amputation were recruited. Two sockets were 3D printed for each participant from 3D socket models that were developed from: (1) 3D scanning a manually-modified hand-casted positive mold of the residuum; and (2) a digitally-modified 3D scan of the residuum. Manual and digital procedures were compared for three measures: final socket geometry, the Socket Comfort Score, and a patient experience survey. Feasibility data were collected to measure protocol implementation fidelity to inform a future larger study. These data revealed that 89% of participants followed the intended protocol, no participants dropped out, and only one adverse event was report. As no significant geometric differences were found and participants experienced similar comfort scores between manually- and digitally-designed sockets, study feasibility was determined to be successful. Thus, a randomized control trial study will be conducted to draw statistically relevant conclusions from these outcome measures that may provide meaningful information for improving digital design procedures.

This detailed paper explores the complex dynamics of Digital Manufacturing in Industry 4.0, with the explicit aim of addressing and remedying significant gaps identified in academic literature. The paper provides extensive insight into how technological innovations, including additive manufacturing, the Industrial Internet of Things (IIoT) and Big Data analytics, are transforming industrial infrastructure and operations. By critically evaluating existing studies and current trends, the paper aims to elucidate not only the impact of emerging technologies, but also to reveal how they can be effectively integrated to increase efficiency and innovation capacity across various industrial sectors. The methodology adopted includes a thorough literature review, comparative analysis of relevant case studies and exploitation of current data, all with a view to developing pragmatic and actionable recommendations. Emphasis is placed on identifying and analyzing the key challenges that accompany the adoption of digital technologies, such as the substantial up-front investment required and the difficulties in preparing the workforce for the new technological requirements. The paper also explores how these challenges can be turned into opportunities to improve the industry's flexibility and adaptability to rapid market changes. The results highlight the significant benefits of implementing Digital Manufacturing, including process optimization, defect reduction, production customization and improved operational sustainability. The conclusions drawn emphasize the critical role of digital innovations in shaping the industrial future and recommend close collaboration between the public and private sectors to facilitate the widespread adoption of these technologies. Finally, the paper suggests that through continued investment in technology development and the implementation of well-designed integration strategies, Digital Manufacturing can lead to profound and sustainable transformations that stimulate economic growth and contribute to positive societal change. [ABSTRACT FROM AUTHOR]

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The opportunity to create magneto‐responsive soft materials (MSMs) with in‐process tailorable and locally controllable magnetic properties is highly desirable across many technological and biomedical applications. In this paper, this capability is demonstrated for the first time using computer‐controlled dual‐material aerosol jet printing (DMAJP) technology. This approach allows controlled variation of composition between the aerosols of a magnetic nanoparticles (MNPs) ink and a photocurable polymer during the printing process. The mixing ratio of the two aerosols determines the MNPs loading in the nanocomposite, which can be used to locally control the magnetic properties of the printed structures. The printing process is structured in a layer‐by‐layer fashion in combination with a sacrificial layer approach for building fully freestanding MSM structures that combine magnetoactive and non‐magnetoactive elements in a single process multi‐material printing method with no further assembly requirements. Using this method, the direct manufacturing of small‐scale multi‐material soft objects with complex shapes and programmable functions whose movements can be controlled by the application of an external magnetic field is demonstrated. [ABSTRACT FROM AUTHOR]

Objectives: Material chemistry and workflow variables associated with the fabrication of dental devices may affect the biocompatibility of the dental devices. The purpose of this study was to compare digital and conventional workflow procedures in the manufacturing of acrylic-based occlusal devices by assessing the cytotoxic potential of leakage products. Methods: Specimens were manufactured by 3D printing (stereolithography and digital light processing), milling, and autopolymerization. Print specimens were also subjected to different post-curing methods. To assess biocompatibility, a human tongue epithelial cell line was exposed to material-based extracts. Cell viability was measured by MTT assay while Western blot assessed the expression level of selected cytoprotective proteins. Results: Extracts from the Splint 2.0 material printed with DLP technology and post-cured with the Asiga Flash showed the clearest loss of cell viability. The milled and autopolymerized materials also showed a significant reduction in cell viability. However, by storing the autopolymerized material in dH2O for 12 h, no significant viability loss was observed. Increased levels of cytoprotective proteins were seen in cells exposed to extracts from the print materials and the autopolymerized material. Similarly to the effect on viability loss, storing the autopolymerized material in dH2O for 12 h reduced this effect. Conclusions/Clinical relevance: Based on the biocompatibility assessments, clinical outcomes of acrylic-based occlusal device materials may be affected by the choice of manufacturing technique and workflow procedures. [ABSTRACT FROM AUTHOR]

Prosthetic socket manufacturing is experiencing a revolutionary shift towards using digital methods, such as 3D scanning and 3D printing. However, using digital methods requires the clinician to transfer their skills from making sockets by hand to making sockets with a computer. This shift in practice may change the socket geometry and fit; however, to what extent is unknown. Thus, the aim of this study was to explore the feasibility of analyzing geometric and clinical differences between digitally- and manually-designed sockets. Nine adult inpatients with below-knee amputation were recruited. Two sockets were 3D printed for each participant from 3D socket models that were developed from: (1) 3D scanning a manually-modified hand-casted positive mold of the residuum; and (2) a digitally-modified 3D scan of the residuum. Manual and digital procedures were compared for three measures: final socket geometry, the Socket Comfort Score, and a patient experience survey. Feasibility data were collected to measure protocol implementation fidelity to inform a future larger study. These data revealed that 89% of participants followed the intended protocol, no participants dropped out, and only one adverse event was report. As no significant geometric differences were found and participants experienced similar comfort scores between manually- and digitally-designed sockets, study feasibility was determined to be successful. Thus, a randomized control trial study will be conducted to draw statistically relevant conclusions from these outcome measures that may provide meaningful information for improving digital design procedures. [ABSTRACT FROM AUTHOR]

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research. Physiological measures can provide the basis for dynamic operator assistance in supervisory process control tasks. This review synthesises the existing evidence, highlighting both the aggregated empirical support and the heterogeneity of the results. To advance the status quo, a larger emphasis on physiological measures as predictors of operator performance is needed. Abbreviations: HF/E: Human factors and ergonomics; CNS: Central nervous system; PNS: Peripheral nervous system; HR: Heart rate; HRV: Heart rate variability; IBI: Interbeat interval; AVNN: Average of RR intervals; SDRR: Standard deviation of RR intervals; CVRR: Coefficient of variation in RR intervals; RMSSD: Root mean square of successive; pNNX: Percentage of successive RR intervals; MAD: Median absolute deviation; LF: Power of the low-frequency; MF: Power of the mid-frequency; HF: Power of the high-frequency; TP: Total power. [ABSTRACT FROM AUTHOR]

Directed energy deposition additive manufacturing (DED-AM) has gained significant interest in producing large-scale metallic structural components. In this paper, a knowledge-based machine learning (ML) approach, combining both physics-based simulation and data-driven modelling, is proposed for a study on thermal variables of DED-AM. This approach enables both forward and backward predictions, which breaks down the barriers between the basic process parameters and key process attributes. Process knowledge plays a critical role to enable the prediction and enhance the accuracy in both prediction directions. The proposed ML approach successfully predicted the thermal variables of wire arc based DED-AM for forward modelling and the process parameters for backward modelling, typically within 7% errors. This approach can be further generalised as a powerful modelling tool for design, control, and evaluation of DED-AM processes regarding build geometry and properties, as well as an essential constituent element in a digital twin of a DED-AM system.

The intricate design of real complex injection molds poses significant challenges. Mold design validation often falls to operators with tool-handling experience but limited CAD proficiency. Unlike other industries, the scale and costs of injection mold fabrication hinder prototyping before production. Virtual reality (VR) has emerged as a revolutionary solution offering a safe, immersive, and realistic experience and accessible using QR codes. This paper presents a new multimodal virtual environment tailored to validate mold design complexities. Integrating knowledge-enriched visual tools like interactive 3D models and dynamic visualizations enables users to explore complex mold designs. Statistical analyses, including the Wilcoxon test, unveil significant differences in interference detection, internal topology tracking, and validation of assembly and disassembly accessibility for both small and large mold components when comparing validation conducted through traditional means using solely CAD systems versus the utilization of multidimensional validation methods. Efficiency gains in using VR devices for mold design validation in a hybrid environment in the analysis of relative frequencies. The present study surpasses the state of the art illustrating how VR technology can substantially reduce manufacturing errors in injection molding processes, thereby offering important advantages for manufacturers emerging as an essential tool for this impact industry in the next years. [ABSTRACT FROM AUTHOR]

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed. [ABSTRACT FROM AUTHOR]

We propose a monitoring system for detecting illicit and copyrighted objects in digital manufacturing (DM). Our system is based on extracting and analyzing high-dimensional data from blueprints of three-dimensional (3D) objects. We aim to protect the legal interests of DM service providers, who may receive requests for 3D printing from external sources, such as emails or uploads. Such requests may contain blueprints of objects that are illegal, restricted, or otherwise controlled in the country of operation or protected by copyright. Without a reliable way to identify such objects, the service provider may unknowingly violate the laws and regulations and face legal consequences. Therefore, we propose a multi-layer system that automatically detects and flags such objects before the 3D printing process begins. We present efficient computer vision algorithms for object analysis and scalable system architecture for data storage and processing and explain the rationale behind the suggested system architecture. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical component in cleaner production, and evaluating the opportunities for saving energy could improve energy efficiency by reducing electricity consumption and increasing competitiveness. In this context, the aim of this study is to examine different scenarios that can lead to better energy efficiency in a short-cycle time flowshop, which is performed with the aid of digital manufacturing software. It has been widely acknowledged in the literature that changing the energy state of machines in short-cycle time flowshop manufacturing is impossible due to the high production volume, which requires the machines to operate full time. We used computational simulation, via digital manufacturing software, to examine the potential for improvements in energy indicators through various scenarios. The scenarios were built using energy and manufacturing data from a real system. The main contribution is in showing that, by controlling the buffers' occupation, the feeding systems of the machines and planned introduction stop. In addition, it is possible to consider new energy states for the machines and, consequently, enhance the energy, as well as the sustainability, indicators in this type of manufacturing process. [ABSTRACT FROM AUTHOR]

BACKGROUND: The environmental impact on industrial X-ray tomography systems has gained its attention in terms of image precision and metrology over recent years, yet is still complex due to the variety of applications. OBJECTIVE: The current study explores the photothermal repercussions of the overall radiation exposure time. It shows the emerging dimensional uncertainty when measuring a stainless steel sphere by means of circular tomography scans. METHODS: The authors develop a novel frame difference method for X-ray radiographies to evaluate the spatial changes induced in the projected absorption maps on the X-ray panel. The object of interest has a simple geometry for the purpose of proof of concept. The dominant source of the observed radial uncertainty is the photothermal effect due to high-energy X-ray scattering at the metal workpiece. Thermal variations are monitored by an infrared camera within the industrial tomography system, which confines that heat in the industrial grade X-ray system. RESULTS: The authors demonstrate that dense industrial computed tomography programs with major X-ray power notably affect the uncertainty of digital dimensional measurements. The registered temperature variations are consistent with dimensional changes in radiographies and hence form a source of error that might result in visible artifacts within the 3D image reconstruction. CONCLUSIONS: This contribution is of fundamental value to reach the balance between the number of projections and radial uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of China's technological level, internet business has also increased, resulting in a shortage of wireless spectrum resources. Therefore, relevant scholars have introduced device direct communication technology into communication networks. Traditional cellular networks improve utilization efficiency and system capacity by introducing device through technology, and can also reduce the transmission pressure of base stations. However, this solution allows cellular users and device through users to share resources simultaneously, and each user may cause interference before. When the interference is severe, users cannot use the communication system. Therefore, this article proposes a communication resource allocation algorithm based on a proportional algorithm, which can enable users to experience better usage effects and leverage the performance advantages of device direct communication technology. Intelligent manufacturing can reflect the industrial competitiveness of every country, and China also attaches great importance to the development of intelligent manufacturing and increases efforts to support intelligent manufacturing. The manufacturing industry of intelligent systems is carried out through the industrialization of computers, promoting system optimization and upgrading. This article studies and explores the clustering of industrial IoT and digital intelligent manufacturing, applying these technologies to wireless resource allocation, thereby promoting the development and improvement of intelligent systems. [ABSTRACT FROM AUTHOR]

This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated artifacts. The top surface point-cloud containing top surface geometry and quality information is pre-processed and passed to an improved deep Hybrid Convolutional Auto-Encoder decoder (HCAE) model used to statistically describe the artifact's quality. The HCAE's output is comprised of 9 × 9 segments, each including four channels with the segment's probability to contain one of four labels, Under-printed, Normally-printed, Over-printed, or Empty region. This data structure plays a significant role in command generation for fabrication process optimization. The HCAE's accuracy and repeatability were measured by a multi-label multi-output metric developed in this study. The HCAE's results are used to perform a real-time process adjustment by manipulating the future layer's fabrication through the G-code modification. By adjusting the machine's print speed and feed-rate, the controller exploits the subsequent layer's deposition, grid-by-grid. The algorithm is then tested with two defective process plans: severe under-extrusion and over-extrusion conditions. Both test artifacts' quality advanced significantly and converged to an acceptable state by four iterations. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging tool involving a decentralized machine learning (ML) paradigm. It shows lots of promise encompassing viable solutions to address data privacy and efficiency in the manufacturing industry. Though quite expensive, FL is a key process wherein input settings, data collection and analysis in the manufacturing sector become important. These aspects hinder the advanced ML and data-driven methods, which needs to be addressed through offline training program. FL plays a crucial role in effectively tackling significant issues in digital manufacturing (DM) and its advanced version additive manufacturing (AM). The paper provides a thorough analysis of the application of FL in the context of DM and AM. It also reviews the current state of the manufacturing industry with a focus on the difficulties and potential advantages associated with contemporary production methodologies. [ABSTRACT FROM AUTHOR]

This research aims to investigate the accuracy of 3D scanner by conducting a comparative analysis of AI-based photogrammetry method and Agisoft Metashape software, a more affordable and accessible approach compared to expensive commercial solutions. The proposed method uses photogrammetry and CAD (Computer-Aided Design) to measure propeller blade geometry through a three-stage process. In the first stage, the images are taken with a digital camera with calibrated and encoded targets. Additionally, reconstruct the propeller blades in Geomagic Studio. Taking photos of objects only takes a short time and is professional. Subsequently, in the second stage, the 2D images are processed using Agisoft Metashape software and Artificial Intelligence (AI) algorithms to generate an accurate 3-dimensional (3D) model. The third and final stage employs CAD programs to measure various radii of each blade in the 3D virtual model. To validate the accuracy of the measurements, the results are compared with those obtained through machine CMM measurements of each area on the propeller blade. The results show that this method successfully automates the entire reverse engineering process at low cost, lower shooting time, and producing 3D models with high accuracy. The validation shows that the 3D results using Agisoft Metashape have a smaller error (%) value compared to the 3D results using AI. Ultimately, Agisoft Metashape yields higher accuracy results compared to AI, but the results suggest that AI can serve as an alternative for the reverse engineering process in this modern era.

Integrating IIoT into manufacturing has significantly enhanced connectivity and production precision, but it also introduces a complex cybersecurity landscape, particularly in digital manufacturing systems. Current vulnerability assessment tools are often system-specific and need more scalability for large IIoT networks. While CVSS offers a standardized framework for assessing vulnerabilities across entire systems, practical adaptations for specific manufacturing contexts are yet to be developed. To address this gap, we present a novel framework to evaluate CVSS impact metrics tailored to the unique environmental and operational contexts of wine manufacturing. This approach leverages the correlation between wine characteristics and quality to assess potential threats and vulnerability exposures in IIoT wine sensors. Our findings show that vulnerability scores derived from CVSS 4.0 demonstrate greater resilience against cyber-attacks than CVSS 3.1 due to the incorporation of newly developed system impact and threat metric assessments. A pair-wise t-test reveals a significant difference between CVSS 4.0 and 3.1 scores, with a p-value of 0.002, highlighting the comprehensiveness of CVSS 4.0 that incorporates system impact and threat metric values assessed by our proposed framework. The proposed methodology is adaptable for evaluating security vulnerabilities in various manufacturing systems, tailored to their specific applications and deployment contexts.

Energy efficiency has become a significant challenge for manufacturing companies. Although it is possible to improve efficiency by applying new and more efficient machines, decision makers tend to look for less expensive alternatives. Furthermore, the current reality of manufacturing companies, brought about by Industry 4.0, requires more flexibility of production systems and increase the complexity off machine rescheduling without compromising sustainable requirements. This study contributes to the subject by applying machine learning techniques in a job shop to reduce the makespan and estimate the total energy consumption. First, an artificial neural network (ANN) was trained to estimate the total electrical energy consumption in the system. A new input variable for the network was defined to assist in energy estimation. This variable is called the Priority Factor (PF) and helps capture the different patterns in the job shop. Second, as the ANN was trained, a Genetic Algorithm (GA) was used to reduce the makespan. Therefore, it is possible to reduce the makespan and know in advance the total electricity consumption in production. This solution supports a more sustainable manufacturing process, and is completely developed in a digital manufacturing environment.

During the manufacturing process of a piece, different factors influence the process so that errors of a systematic and random nature are added, affecting both the dimensions and the final geometry. The strategy presented in this research seeks to identify, monitor, and computationally control the errors arising from the arrangement of the actuators that can be derived from errors in assembly, wear of the components, and thermal deformations. This article proposes an algorithm to prevent and correct geometric and dimensional errors in manufacturing parts as a compensation strategy. The algorithm obtains the references of the manufacturing process and estimates the process’s deviation, calculating the manufactured part’s error concerning the projected piece. Error compensation is performed through reference points that alter the location of the target points in the opposite direction to the resulting error to nullify it kinematically. The validation of the strategy is carried out through the computational implementation of kinematic algorithms of a machine tool with a Cartesian structure of two degrees of freedom on which position and orientation errors are induced. The presented results allow for verifying the proposed algorithm’s effectiveness against tests with significant errors. The compensation strategy presented allows projecting this algorithm as an online software calibration method, reducing the number of stops due to mechanical maintenance of the CNC machine and making closed-loop manufacturing possible with real-time compensation.

Globalisation of the manufacturing and service industry has increased complexity of the flow of materials and goods, magnified dependency on the underlying network, and made the industry more vulnerable to the changing market conditions. As manufacturing and service industry undergo transformation, an opportunity to rethink the design of future enterprises has emerged. Six enablers of miniversal manufacturing are discussed: digitisation, open manufacturing, service orientation, shared manufacturing, sustainability, and resilience. These enablers will form properties of universal manufacturing, with adaptability and affinity that are emerging. A universal manufacturing enterprise will be formed based on the distributed manufacturing facilities. The emerging standards for interoperability of systems needed for universal enterprises are discussed. The data and modelling standards will enable the synthesis of digital models into universal enterprises. Though there is no global standard for the representation of digital manufacturing models in a cloud, the existing process modelling methodologies and languages may offer the solutions needed. The evolution of production systems is illustrated with three snapshots, dedicated manufacturing, distributed manufacturing, and universal manufacturing. The modelling approach followed in this paper is bottom-up rather than top-down followed in the literature on modern manufacturing. [ABSTRACT FROM AUTHOR]