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This study examines the pivotal role of the logistics sector in corporations, particularly those with diverse product portfolios. It focuses on the time-intensive “picking” process, driven by various products and material handling requirements for order fulfilment. The research presents a methodology that combines distribution centre modelling for simulation with an optimisation algorithm to enhance operational efficiency. The goal is to determine an optimal route for product retrieval, minimising employee travel distances, using the simulated annealing algorithm. The results showcase a significant 7 % reduction in employee travel distance during collection. This research is relevant to academia and practical applications as it presents an opportunity to reduce operational costs in distribution centres, enhance the efficiency of picking operations, and improve the competitiveness of companies. Furthermore, it contributes to operations research by addressing a complex problem with significant practical implications using a rather unused method to solve this problem of picking operations. The approach is empirically applied in a prominent Brazilian beverage company. This methodology proves valuable for optimising logistics operations in various industrial contexts, highlighting its practical applicability.

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.

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.

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.

Occupational ergonomics is responsible for the relationship between the worker and the machine in the work environment to achieve smooth، comfortable and safe performance، and therefore professional ergonomics for digital manufacturing is concerned with clarifying the relationship between digital production machines and the worker in the work environment to achieve the best quality and performance. The research problem can be expressed through the following questions: - What is the extent of ergonomic compatibility between digital production machines and technicians working on them in the field of metal furniture production? Is it possible to improve this ergonomic compatibility using professional ergonomics standards in order to achieve the best quality and safety for technicians working in this field? The research aims to determine the occupational ergonomics standards that govern the relationship between the machines of digital production systems for metal furniture and the technicians working on them. In order to improve the compatibility between digital production machines and technicians to achieve the best quality and safety in the work environment. The research uses the descriptive analytical method to monitor the relationship between the machines of digital production systems and the technicians working on them and analyze this relationship to come up with ergonomic criteria governing this mutual relationship to achieve the best quality and safety in the work environment. Through several axes:- First: The emergence and concept of digital manufacturing - Second: Professional ergonomics and its link to digital manufacturing machines - Third: Conclusion of professional ergonomics standards for digital manufacturing - The research reached a number of results and recommendations، including:- occupational ergonomics for digital manufacturing is one of the most important modern requirements due to the shift towards the digital age in manufacturing، which necessitated the presence of ergonomic factors that achieve ergonomic controls and standards that suit the good performance of jobs and their suitability with the worker or operator and digital machines.- Conducting more studies on the applications of ergonomics in the digital age and the consequent difference in ergonomic requirements and standards that suit new tasks.

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.

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.

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.

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.

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.

Manufacturing has provided growth and employment opportunities to many developed countries. Digital technologies have further enhanced these opportunities and diversified manufacturing activities. However, it has not been as successful in developing countries, such as Botswana, due to the low absorptive capacity, lagging digital infrastructure, and the slow development of people who need upskilling or an entirely new skill set. The increase in access to the Internet and the extensive adoption of information and communication technologies by manufacturing companies are driving competition and disrupting the present circumstances. This study aims to assess the digital skills students acquire when studying an industrial design programme and compare them with the skills needed by digital manufacturing small and medium enterprises. A case study was adopted for this study because it can capture the relationship among the phenomena, context, and people in the lived realities of the participants. The findings indicate an alignment of the skills students acquire during their studies with those needed by digital manufacturing small and medium enterprises. However, the level at which students are exposed to these digital manufacturing skills is skewed towards basic awareness, with very few students reporting competency in digital manufacturing skills, such as using a laser cutter, plasma cutter, 3D printing, and a router machine. The emphasis could be shifted to developing digital manufacturing skills, as this is the future of manufacturing in the fourth and fifth industrial revolutions.

The increasing demand for animal feed and limited availability of traditional feed raw materials have led to a growing interest in the use of alternative sources. Previous investigations have established that the Azolla plant can accelerate the growth of livestock by 10–20%. Therefore, this study aims to evaluate the use of Azolla as alternative feed raw material and assess the processing technology for its potential as a sustainable solution for the feed industry. The processing technology involved a combination of physical, chemical, drying, and grinding techniques. The results showed that the processing technology significantly improved the nutritional value of the Azolla as alternative feed raw material. The physical quality of the feed was also improved, leading to better handling and storage properties. The processing technology had different effects on various feed raw materials, as Azolla required drying at a controlled temperature of 40–60 °C to maintain the protein content. This analysis demonstrated the potential of Azolla as an alternative feed raw material for a sustainable solution in the feed industry. The processing technology significantly improved the nutritional value of the feed, leading to better animal performance and reduced environmental impact. However, the selection of processing technology should be carefully considered based on the specific characteristics of the feed raw material, which digital manufacturing technologies play a significant role for this purpose.

Within the framework of casting process design, the efficient retrieval of three-dimensional (3D) computer-aided design (CAD) models could result in significant time and cost savings. However, this technique still suffers from inefficiency and inaccuracy because of the wide variety of casting models’ poses. In this study, a method for normalizing the poses of casting models is proposed. This method constructs the transformation matrix through the eigendecomposition of the second-order central moment matrix calculated from the voxel casting model. Then the transformation matrix is applied to the casting model to get a normalized pose. An assessment approach for pose normalization is also suggested in the study, which measures the distance between poses normalized based on multiple poses of the same model. The study demonstrates that the pose-normalization approach reliably transforms distinct poses of the same model into a unified pose. The mean distance between normalized poses is 0.016 and the minimum distance is 0.010. The method’s effects improve when the voxel size reduces.

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

Purpose: The ever-evolving market dynamics and the trend toward increased product individualization demand a high degree of adaptability in production management. This calls for novel approaches, such as Industry 4.0 (I4.0), which integrates the Quality 4.0 (Q4.0) model as well. Methodology/Approach: I4.0 builds upon the foundation of digital manufacturing and entails three key dimensions: (i) The employment of advanced digital technologies to enable digital production, (ii) The development of smart products, which incorporate novel production methods and innovative features, and, (iii) The adoption of an intelligent supply chain, designed to facilitate the procurement of raw materials and the delivery of finished goods. In our research studies, we focus specifically on the first and third approaches. Findings: A bilateral flow of information in collaborative manufacturing is facilitated through digital platforms that span the entirety of the production process, from the inception of cutting-edge product designs to the final delivery to end-customers, integrating quality management (QM) as well. Research Limitation/Implication: The current study outlines a QM model developed for a digital factory in Serbia, utilizing the Q4.0 model, with particular emphasis on the workshop section, integrated within the Enterprise Resource Planning/Manufacturing Execution Systems module. Originality/Value of paper: The manuscript serves as a notable illustration of best practices for digital manufacturing, and the implementation of a digital QM model within the metalworking industry.

In this paper, we present a software tool called PocketFinderGNN for the recognition of a critical manufacturing feature named close pocket in 3D models. The close pocket is a pocket which is surrounded by material everywhere along its circumference. This feature makes a crucial role in the manufacturing industry, and its recognition is essential for the automation and optimization of machining processes. PocketFinderGNN converts the .stp file generated by CAD/CAM systems to a graph representation of the 3D model and utilizes a Graph Convolutional Network (GCN) to predict which nodes consist of the close pocket feature. The proposed tool was implemented using PyTorch Geometric and NetworkX frameworks. We trained our model on a dataset of 576 3D models obtained from the electromechanical industry and achieved an accuracy of 95% for the correct recognition of the faces forming the closed pocket feature. Our method outperforms state-of-the-art techniques and demonstrates its robustness to noise and perturbations in the input data. The results show that our approach is an effective tool for the recognition of complex manufacturing features in 3D models, which can significantly improve the efficiency and accuracy of manufacturing processes.

This article introduces a novel concept where advanced technologies have been leveraged to produce a modular walking orthosis (MOWA) within a completely digital process chain. All processes of this new supply chain are described step-by-step. The prescription and treatment of lower leg orthoses for individuals with paralysis or muscle weakness, particularly cerebral palsy (CP), are complex. A single case study indicates successful treatment with this new orthosis (MOWA). From the authors’ perspective, this innovative fitting concept is promising and will contribute to creating more efficient care within a multidisciplinary team.

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces.

There are many well-known systematic approaches to design the digital systems used in manufacturing. However, there are only a few approaches that specifically deal with low-cost components. Such components may not provide the same level of completeness as more expensive industrial alternatives and may need to be combined with other components to become comparable. Consequently, common design challenges for systems comprising such low-cost components revolve around extendability and interface standardisation. There is a need for analysing the capability of the existing approaches to design these systems. This study aims to evaluate the effectiveness of designs for low-cost digital manufacturing systems that have been derived from a particular design approach. The proposed evaluation methodology is used for the special case of designs that are directly based on reference architectures and for the development of specific metrics for that purpose. To quantify the effectiveness, these metrics are applied to a number of design scenarios. Although focusing on reference-architecture-based designs, the proposed methodology can also be used for other design approaches. The evaluation and structured implementation comparison helps practitioners in selecting an effective design approach to low-cost digital manufacturing systems and provides insights into how a particular design approach can become more effective.

Currently, many virtual simulation design studies of compression pants do not consider pressure distribution and human body characteristics. Therefore, this study aimed to optimize the simulation design accuracy of compression pants by investigating female body characteristics to improve the pressure distribution and enhance comfort. Firstly, we divided the body part features into flexible and rigid parts, performed compression relationship analysis between the material and the body, and collected qualitative and quantitative data related to the potential influencing factors. Subsequently, by conducting correlation analysis of the data, a pressure prediction model was established to address the pressure value errors in the simulation data. The research results showed that there was a significant difference between the real and virtual pressures in the flexible parts of the female body, and that the real pressure was closely related to the elasticity and thickness properties of the material. By optimizing virtual pressure values, the consistency between the virtual pressure and real test results can be significantly improved. The accurate prediction and optimization of pressure values can lead to the reduction of material waste and energy consumption during the manufacturing process.

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

The data available in this article include 3D mechanical designs used for the computer-aided fabrication of metal honeycombs produced by additive manufacturing and studied in [1]. In addition, the force-displacement data utilized to evaluate the mechanical performance of the metal used in this study are available via the digital image correlation technique. Further, the surface features obtained using 3D scanning microscopy of the fabricated parts are available as raw files and processed data. Finally, the impact test data are presented as high-frame-rate videos showing the time-displacement numerical values. This information has been provided in this data article to complement the related research, serve as a guide for future studies, and ensure the data's repeatability and reliability of the related research paper. The research article [1] investigates the mechanical performance and failure mechanism of additively manufactured metallic honeycombs under various scenarios, from quasi-static to dynamic loading. It also investigates the design optimization of these energy-absorbing hollow structures by comparing hollow structures made of three distinct novel cell designs (triangular, diamond-shaped, and diamond-shaped with curved walls) with traditional honeycombs made of hexagonal cells.

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

In fused deposition modeling (FDM), the cooling history impacts the bonding between filaments and layers. The existence of thermal gradients can cause non-homogeneous properties and localized stress points that may affect the individual filaments, resulting in distortion and detachment. Thermal analysis can aid in understanding the manufacturing flaw, providing necessary tools for the optimization of the printing trajectory. The present work is intended to deepen understanding of the thermal phenomena occurring during the extrusion of polymeric materials, aiming at more efficient three-dimensional (3D) printing methods. A one-dimensional (1D) finite differential method was implemented using MATLAB to simulate the temperature evolution of an extruded filament, and the results were compared with two-dimensional (2D) COMSOL Multiphysics simulations, and experimentally validated using infrared thermography. Acrylonitrile–butadiene–styrene (ABS) was used as a test material. The energy dissipation includes forced convection and radiation heat losses to the surrounding medium.

Digital twins are among the technologies that are considered to have high potential. At the same time, there is no uniform understanding of what this technology means. Definitions are used across disciplinary boundaries, resulting in a multitude of different interpretations. The concepts behind the terms should be clearly named to transfer knowledge and bundle developments in digitalization. In particular, the Reference Architectural Model for Industry (RAMI) 4.0, as the guiding concept of digitalization, should be in harmony with the terms to be able to establish a contradiction-free relationship. This paper therefore summarizes the most important definitions and descriptions from the scientific community. By evaluating the relevant literature, a concept is derived. The concept presented in this work concretizes the requirements and understanding of digital twins in the frame of RAMI 4.0 with a focus on manufacturing. It thus contributes to the understanding of the technology. In this way, the concept is intended to contribute to the implementation of digital twins in this context.

Robotics and automation have been a growing area within K–12 educational institutions for the past decade. Across secondary educational institutions, students are introduced to robotics in classes, after-school clubs, and competition leagues through various educational platforms, vendors, and kits. Robotics was initially implemented in schools to help drive more interest in STEM through hands-on application of mechanical, electrical, structural, and computer engineering concepts. Recently, the trend of K–12 robotics has become very niche, focusing more on mobile robotics or robotics competitions. Because of this trend, students have limited exposure to emerging technological advances, such as those found in Industry 4.0. Exciting technological areas, such as digital twins, are not covered in curricula, and this lack of exposure negatively influences the direction of student interest in the “T” and “E” of STEM, with many students never pursuing computer science, technology, or robotics in higher education. The primary goal of this research is to provide a methodology to expose secondary students to Industry 4.0 technologies by leveraging accessible technologies, such as Unity and the Robot Operating System (ROS), to develop a low-cost, high-fidelity digital twin of a pick-and-place robot in a smart warehouse operation. This digital twin prototype will help students to learn about Industry 4.0 trends, such as next-generation automation systems, digital twins, digital manufacturing, intelligent automation, and additive manufacturing, using ROS–Unity integration and hardware accessible to secondary schools to simulate a pick-and-place robotic application. By harnessing the accessibility of Unity and ROS to create a low-cost digital twin prototype for a secondary school, this research has a secondary goal of improving the pipeline of students interested in pursuing STEM-related learning in higher education, thereby ensuring a future STEM workforce that can research, design, develop, operate, and maintain the systems and technologies of Industry 4.0.

With the rapid development of the three-dimensional (3D) printing technology in recent decades, precise and personalized manufacturing has been achieved gradually, bringing benefit to biomedical application, especially stomatology clinical practice. So far, 3D printing has been widely applied to prosthodontics, orthodontics, and maxillofacial surgery procedures, realizing accurate, efficient operation processes and promising treatment outcomes. Although the printing accuracy has improved, further exploration is still needed. Herein, we summarized the various additive manufacturing techniques and their applications in dentistry while highlighting the importance of accuracy (precision and trueness).

Solid waste management is seen as a response to the increase in waste generation due to the rising number of industrial facilities. This includes digital manufacturing facilities such as Fabrication Laboratories (FAB LAB) which acts as innovation centers that generates prototypes using a common set of digital fabrication equipment. Previous studies have tackled with the environmental impacts of FAB LABs in a macro-level scale; however, there has been a lack of research specifically assessing the solid waste of laboratories, more so on Philippine FAB LABs. A baseline assessment study on FAB LABs of the Philippines could be applicable in future implementations of solid waste management systems through the crafting of institutional policies and guidelines for environmental sustainability. Using data gathered from 11 respondent FAB LABs, this study quantified percentage compositions of the waste according to waste type as well as the relative waste generated by each respondent FAB LAB. Machine availability was seen as a factor in waste generation resulting in the high generation of wood and plastic waste. Moreover, it was observed that earlier established laboratories generally had more active makers than recently established ones, hence the older FAB LABs statistically produced more waste. Approximately 53% of the overall waste produced was considered recyclable by Philippine standards but the actual recyclability of the waste was still undetermined due to the ambiguous criteria for recyclables and the lack of feedback data from recycling facilities. The initial findings suggest that an implementation of continuous waste monitoring, sufficient in-laboratory protocols, and coordination between FAB LABs and recycling facilities could improve actual waste recyclability and—by extension—the environmental sustainability of Philippine FAB LABs.

Most digital manufacturing projects follow the software-development process. However, the application of all the characteristics and processes specific to a digital manufacturing project cannot be generalized to all other engineering projects. Therefore, digital manufacturing project teams typically design and apply process principles that can be customized to individual use cases by project members. Tailoring these principles, including developing and referencing them, is a knowledge-intensive activity that requires the gradual improvement of development processes. This paper proposes a knowledge-oriented digital manufacturing ontology model and a semantic rule-based tailoring system that can derive tailoring strategies from knowledge ontologies via an inference-rule design and engine. The proposed model and system can assist in concrete project implementations based on software-development experience. A practical example is demonstrated through the 3D-modeling digital manufacturing of a hospital kitchen.

Abstract The transformation of industrial production is one of the big challenges on the pathway to sustainable development. Therefore, expectations regarding the contribution of Industry 4.0 are high. So far there is only little research focusing on the relation between the Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda and the digitalization of industrial processes. We argue that sustainability aspects must be an integral part of Industry 4.0 implementation to support a sustainable development. For that reason, the digital manufacturing concept itself must take essential characteristics of sustainability into account. Our analysis has investigated to what extent sustainability aspects are currently reflected in the most recognized articles about Industry 4.0. For that purpose, we have conducted a systematic literature review, in which we have analysed the top cohort of most frequently cited articles published after 2013 on GoogleScholar dealing with “Industry 4.0”. Our literature review reveals that the descriptions reflect many expectations and hopes while only few of them are evidence-based. According to our results Industry 4.0 mainly deals with the economic dimension of sustainability such as growth and productivity. Although there are expectations that Industry 4.0 creates a window of opportunity for a more sustainable production, we could not find evidence to support this idea. Instead of targeting a more sustainable production, many descriptions draw a picture in which Industry 4.0 processes run exactly as before, just in a digital way.

This paper is primarily concerned with determining and assessing the properties of a cement-based composite material containing large particles of aggregate in digital manufacturing. The motivation is that mixtures with larger aggregate sizes offer benefits such as increased resistance to cracking, savings in other material components (such as Portland cement), and ultimately cost savings. Consequently, in the context of 3D Construction/Concrete Print technology (3DCP), these materials are environmentally friendly, unlike the fine-grained mixtures previously utilized. Prior to printing, these limits must be established within the virtual environment’s process parameters in order to reduce the amount of waste produced. This study extends the existing research in the field of large-scale 3DCP by employing coarse aggregate (crushed coarse river stone) with a maximum particle size of 8 mm. The research focuses on inverse material characterization, with the primary goal of determining the optimal combination of three monitored process parameters—print speed, extrusion height, and extrusion width—that will maximize buildability. Design Of Experiment was used to cover all possible variations and reduce the number of required simulations. In particular, the Box—Behnken method was used for three factors and a central point. As a result, thirteen combinations of process parameters covering the area of interest were determined. Thirteen numerical simulations were conducted using the Abaqus software, and the outcomes were discussed.

In this work, the influence of extrusion infill angles on the mechanical properties of 3D printed (Fused Filament Fabrication, FFF) test specimens are investigated, considering the real geometry of the components. Therefore, various polylactide (PLA) specimens with different infill angles are manufactured, scanned by Computed Tomography (CT) and further investigated by mechanical testing using an optical measuring system. This allows the directional dependence and the elastoplastic behavior of the material to be demonstrated. It was found that the real geometry behavior differs significantly from the model. In addition to the tests Finite Element Method (FEM) simulations of the scanned components are carried out in order to provide a prediction of the mechanical properties of FFF-printed parts for component manufacturers. The conducted simulations have shown that the geometric deviation leads to an increase in stiffness, a higher ultimate tensile strength and strain at failure. The main objective of this work is to evaluate the stiffness and strength of FFF-printed components using FEM with an economically justifiable testing effort. This includes not only the evaluation of the directional dependence, considering the real geometry of the components, but also the evaluation of a suitable strength criterion. The criterion of maximum principal strain has proved to be suitable.

With the passage of time, the problems of preserving the archaeological heritage are increasing, so scientists have begun to search for new, unconventional methods of simulation, reproduction, and preservation of it, and furniture is one of the most important examples included in cultural heritage.In recent years, interest in digital manufacturing has increased, which depends mainly on the presence of computers, and many digital mechanisms have been deployed that serve design in general and interior design in particular, and from here the idea of research emerged that discusses the possibility of adopting design models that simulate heritage furniture through a computer, It is digitally produced and re-manufactured again. The research was conducted in three stages as follows:First theoretical study in which an extensive theoretical study was conducted for scientific research and messages in the period from 2009 to 2019 within the constituent environments of the research elements, then a theoretical study of digital modeling was conducted as one of the axes of the research topic, then how to simulate the work of existing furniture, and also discuss simulation methods and choose the method Optimal among them, and finally discussing digital manufacturing and knowing the digital technologies used in manufacturing, so we finally get a set of mechanisms and tools that can be used in preparing a standard model for use in analytical study.Second: An analytical study in which a number of samples were selected for pieces of historical furniture in which the method of digital modeling was applied to them taking into account the diversity of those samples in terms of the historical period between the ancient and classic Egyptian, and also in terms of function, then researching the possibility of digital manufacturing of those samples that have been Its model.Third: The applied study and the results in which the researcher presents the most important results related to the effect of using digital modeling on simulating classic furniture and digitally recycling it.key words :Simulation - Modeling - Historic Furniture - Digital Manufacturing - 3D Printing

ABSTRACT: This study explores Industry 4.0 in the Australian context, where manufacturing is dominated by smaller firms servicing the domestic market. We interviewed 20 advanced Australian manufacturers from diverse fields, capturing detailed descriptions of the Industry 4.0 implementation process. We compared the themes that emerged from their descriptions with the factors typically assessed in Industry 4.0 frameworks to draw out differences in emphasis. Consistent with these frameworks, Australian manufacturers were focused on using Industry 4.0 technologies to automate the capture, integration and analysis of data. To enable this activity, they were reorganising workforce roles and developing new skill sets. Knowledge sharing and collaborations within and across the organisation were seen to be especially important for small Australian manufacturers (with limited funding for technology investment and research and development) to maintain global competitiveness. However, while most Industry 4.0 frameworks describe supply chain applications, the development of smart products and services, and the need to adopt a strategy-led approach, relatively few participants spoke about these opportunities. Even fewer addressed the need for improved governance, standards and data security in the context of Industry 4.0. We argue that these gaps are best addressed through government policy and investment focusing beyond manufacturers, to support Industry 4.0 uplift across key domestic supply chains.

«Today, 600 million people in Africa do not have access to electricity and 900 million lack access to clean cooking facilities» (IEA, 2019). With this premise, the paper will explain the research agenda of the African Off-grid Housing project on how to design and build off-grid and affordable housing solutions for Sub-Saharan Africa. This ongoing project is being developed at the School of Architecture and Cities of the UoW with the support of the GCRF. The research agenda is based on the idea of producing innovative knowledge able to bridge traditional and advanced design strategies as well as construction technologies in response to the urgent need of sustainable and performative housing in Sub-Saharan Africa.

La creciente cultura maker, puede resultar un gran aliado para fomentar la alfabetización digital desde una perspectiva crítica, potenciar el desarrollo comunitario, así como generar oportunidades a colectivos en situación de vulnerabilidad, mediante la creación de sinergias entre los espacios de fabricación digital y la educación no reglada. En este contexto, el presente artículo tiene como propósito la validación de un conjunto de instrumentos (encuesta, grupo focal y entrevistas) elaborado en el marco de un estudio de tesis doctoral que busca hallar las posibilidades de las tecnologías creativas de entornos maker en la acción socioeducativa. Los resultados cualitativos y cuantitativos obtenidos en el panel conformado por 16 expertos confirman la confiabilidad y validez del conjunto evaluado. Este se evaluó por criterios de claridad, coherencia y relevancia donde, entre otros, sitúan el conjunto de instrumentos con una puntuación muy alta (promedio de 4,67 de 5, siendo n=282). Aun así, las discrepancias entre las observaciones y sugerencias obtenidas, junto a los valores del coeficiente de W de Kendall (0,14; siendo p < 0,05), sugieren una revisión más profunda en ciertos ítems. En conclusión, el proceso de validación sirve como marco contextual para evidenciar y profundizar el potencial de estos entornos creativos y colaborativos mediante la creación de metodologías sólidas y útiles para comprender lo que sucede en los mismos. /// The growth of the maker culture and the interactions between makerspaces and non- formal education, could be considered as a great support to encourage digital literacy from a critical perspective, increase social development or even to generate opportunities to vulnerable youth. In this context, the purpose of this paper is to validate a set of researching instruments (survey, focus group and interviews) design as part of a doctoral dissertation study and seeks to find the possibilities of making movement and creative technologies in the social and educational context. The results obtained on an experts’ panel of 16 people, verify the reliability and validity of the instruments evaluated by clarify, coherence and relevance criteria. The prominent outcomes show a very high score of the survey items (average of 4.67 of 5, while n = 282. Even so, the discrepancies between the observations and suggestions obtained, together with the values of Kendall's W coefficient (0.14, being p

The objective of this paper is to present the development of 3D digital manufacturing through synchronous 5-axes printing for greatly enhancing the strength of the printed parts. In traditional fused deposition manufacturing (FDM), which is one of the digital manufacturing technologies, the melted material is required to be extruded from the nozzle on the workspace platform with a fixed direction. The strength is restricted in the direction of perpendicular to the layers since the printing way is layer by layer. The poor adhesion between the layers becomes a weakness to resist external force, especially when the force exerted from different directions. In this paper, algorithms for synchronous five axes printing based on the surface printing trajectory has been proposed to overcome the lack of strength issue. A five axes synchronous 3D printing machine developed in our NTU Intelligent Robotics and Automation Lab enhances the strength of the printed parts by adding additional materials to the surface of the parts. Five axes printing can achieve the goal of printing in different orientations so that the strength of the printed parts is greatly enhanced in comparison with the printing in a fixed direction only. The five axes synchronous 3D printing has been successfully demonstrated in physical printing. The strength analysis of printed parts is also performed under the three-point bending test and the tensile test. It shows that the strength of the five axes printed sample is increased by nearly three times in the bending test and nearly two times stronger in the tensile test.

Mass Personalisation (MP) is becoming more significant to answer diversifying customer needs, as a result of the advancements in digital manufacturing. In contrary to the modular design in mass customisation, Design for MP (DfMP) proposes more profound changes in product and active user involvement in the design process, while maintaining mass efficiency. Traditional product development methodologies fall short in guiding MP, as it has the distinct differences with product variability and the customer involvement with specific needs. In this study, a dedicated design methodology for MP is presented, focussing on these key dimensions. The proposed methodology guides the designer through the development process of a user modifiable design and demonstrates how to facilitate the user involvement in reaching a personalised design. It proposes a flexible and adaptable seed design architecture, and an interactive customer co-creation process. The development of a seed design, construction of its design space, and management of the solution space with a design solution algorithm are elaborated. The application of the methodology was illustrated on the personalisation of knitted footwear, and 3D printed saxophone mouthpiece. The examples show the potential of the methodology to deal with coupled MP cases. A systematic approach to DfMP will allow expanding MP to more products, and acts as a foundation for the customer co-creation oriented design in the context of this emerging paradigm.

In the last decades, digital manufacturing has constituted the headline of what is starting to be known as the ‘fourth industrial revolution’, where the fabrication processes comprise a hybrid of technologies that blur the lines between fundamental sciences, engineering, and even medicine as never seen before. One of the reasons why this mixture is inevitable has to do with the fact that we live in an era that incorporates technology in every single aspect of our daily lives. In the industry, this has translated into fabrication versatility, as follows: design changes on a final product are just one click away, fabrication chains have evolved towards continuous roll-to roll processes, and, most importantly, the overall costs and fabrication speeds are matching and overcoming most of the traditional fabrication methods. Laser-induced forward transfer (LIFT) stands out as a versatile set of fabrication techniques, being the closest approach to an all-in-one additive manufacturing method compatible with virtually any material. In this technique, laser radiation is used to propel the material of interest and deposit it at user-defined locations with high spatial resolution. By selecting the proper laser parameters and considering the interaction of the laser light with the material, it is possible to transfer this technique from robust inorganic materials to fragile biological samples. In this work, we first present a brief introduction on the current developments of the LIFT technique by surveying recent scientific review publications. Then, we provide a general research overview by making an account of the publication and citation numbers of scientific papers on the LIFT technique considering the last three decades. At the same time, we highlight the geographical distribution and main research institutions that contribute to this scientific output. Finally, we present the patent status and commercial forecasts to outline future trends for LIFT in different scientific fields.

The adoption and adaptation of digital tools in architecture have configured an intense process of assimilation of their own logic. The digital turn in architecture could be understood as the one that replaces the architecture of industrialization with the architecture of information, but, and, above all, the one that goes from thinking in mechanical terms to doing it in algorithmic and digital terms. The effects of digitization on architecture, as a historical disciplinary process, are characterized in two previous stages, as two turns of the digital in architecture in the process that goes from mechanics to Digital Materiality (M.D.). The present article intends to advance in conceptual precisions from the selection and analysis of texts focused on the understanding and explanation of a complex process whose gestation has been going on for several decades, M.D. as the current stage of assimilation of the digital logics in the processes of architectural ideation.

Meat products are popular foods and there is a need for cost-effective technologies for rapid quality assessment. In this study, RGB color imaging coupled with machine learning algorithms were investigated to detect plant and animal adulterants with ratios of from 1 to 50% in minced meat. First, samples were classified as either pure or adulterated, then adulterated samples were classified based on the adulterant's type. Finally, regression models were developed to predict the adulteration quantity. Linear discriminant classifier enhanced by bagging ensembling performed the best with overall classification accuracies for detecting pure or adulterated samples up to 99.1% using all features, and 100% using selected features. Classification accuracies for adulteration origin were 48.9–76.1% using all features and 63.8% for selected features. Regression trees were used for adulterant level quantification and the r (RPD) values were up to 98.0%(5.0) based on all features, and 94.5%(3.2) for selected features. Gray-level and co-occurrence features were more effective than other color channels in building classification and regression models. This study presents a non-invasive, and low-cost system for adulteration detection in minced meats.

The additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

Digital twin and physical internet adoption based on advanced digital technologies is gaining significance in the recent times. Digital twin is one of the enabling technologies that link actual physical system with its virtual depiction using advanced digital technologies. In order to leverage its potential, firms have instigated to adopt digital twin and physical internet for real-world applications. Although researches on impact of information technology in supply chains exists, a study on the modelling aspects by analysing critical success factors for adoption of digital twin and physical internet is limited. We have addressed this gap by developing a structural model for identifying the inter-relationships and cause effect parameters of critical success factors for adoption of digital twin and physical internet. Thirteen critical success factors were shortlisted based on literature review and in consultation with experts. An integrated Grey- based, decision making trial and evaluation laboratory (DEMATEL)is used to visualize the causal relationships among critical success factors and verified by applying the proposed model in an electronics manufacturing firm. Sensitivity analysis has been done to check accuracy of results and robustness of the study. The results reveal that top management commitment and support and long, medium and short term planning are the most influential factors impacting the adoption of digital twin and physical internet in electronics industry. Findings of this research provide insights for supply chain managers and academicians by identifying the cause and effect group critical success factors for effective adoption of digital twin and physical internet.

This work describes an approach for the Digital Transformation (DT) of a manufacturing SME in the mold production industry. The phases for changing from manual and non-adding value labor-intensive practices to digital and smart manufacturing configurations are described. Initially, the needs of the SME are presented in terms of improving the planning and monitoring capabilities. Following the requirements analysis, a set of digital functionalities are proposed and mapped to the specific needs. The digital solution presented includes technologies such as the Internet of Things, data management, intelligent production planning and web technologies. The industrial pilot case has showed how the use of open-source off-the-shelf technology, integrated with the facility’s legacy systems can reduce daily production management tasks and ease the burden of planning and monitoring. The engineers and management team of the SME have acquired first-hand experience on the benefits digitalization of process can bring to the organization that is valuable for proceeding with the DT of the organization.