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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.

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. [ABSTRACT FROM AUTHOR]

A more rapid development of smart manufacturing system (SMS) within the automotive industry will significantly promote its competitiveness, which is essential to the economic sustainable development of South Africa. From recent research studies, the automotive sector contributes >7% to the annual gross domestic product of South Africa, and its sustainability is important to the segment through SMS. Thus, SMS deserves greater focus from the government and the industry. Smart manufacturing technology uses machines and tools that are interconnected for the improvement of the manufacturing system and energy optimization. The benefits of SMS include cost reduction, increased productivity, product customization, resources efficiency and minimization of negative environmental impacts. Globally, the automotive industry puts significant emphasis on SMS, considering the ongoing substantial initiatives. To achieve SMS, ultramodern technologies in different areas, varying from cyber-physical systems, smart sensors, complex and large data analytics, Internet of Things, cloud manufacturing, additive manufacturing, energy reduction and hologram are being established and used in manufacturing locations. Therefore, this study is aimed at technologies and toolkits development that will increase the implementation and responsiveness of SMS among automotive manufacturers and support decision makers in planning better SMS capabilities. [ABSTRACT FROM AUTHOR]

Metallic powder alloys for additive manufacturing (AM) are required to have a certain particle size and shape distribution in different processes such as laser powder bed fusion for metallic alloys (LPBF-M). For reasons of sustainability and cost-effectiveness, it is common to recycle powder that was not melted in the build construction process. As usual in a LPBF-M process, the metal powder undergoes a heuristical designed sieving and blending procedure to maintain a certain particle distribution. However, keeping track of the powder's mean age during each building cycle can be obscure or laborious. To overcome this challenge, the authors of this research propose a theoretical model and an experimental methodology to track the powder morphology evolution with respect to a specific number of building cycles and the sieved and solidified amount of powder Such morphological investigation was carried out through a scanning electron microscope, analyzing the morphological evolution of nickel-chromium alloy metal (Inconel 718) powder. The powder analysis gives a close fit of its mean age to the span of the retained particle solidity measurements and the evolution of the mean major diameter. Recycling and thereby modifying the ageing rate of the powder, under monitoring the morphologic evolution of the particles, could be considered as a key criterion for a more effective powder usage lifetime. The findings from this methodology give insight on a predictive model for aging rate, capable of retrieving information about the effective powder's lifetime, impacting positively in the organization and storage of metal powders for AM. [ABSTRACT FROM AUTHOR]

Manufacturing industries continuously face the challenge of delivering high-quality products under high production rates while minimizing non-value adding activities. The recent COVID-19 pandemic has caused manufacturers to rethink and reassess their global supply chains and the flexibility of their production sites. Resilience means the ability to withstand difficult situations without incurring significant extra costs. The deficiencies can be attributed largely to the lack of efficient ways for trusted data sharing among the value chain stakeholders without interoperability barriers. There is a need to be able to determine when such changes lead to deterministic-chaotic behavior with far reaching consequences. This work presents a framework to support production networks' reconfiguration for resilient manufacturing value chains. The proposed concept utilizes platform-based manufacturing that builds on the concept of dataspace and its reference implementations of Gaia-X and IDS for data-sharing in the horizontal supply chain and the Asset Administration Shell (AAS) for implementing intra-factory reconfiguration practices. Moreover, the proposed framework considers the Digital Twin of the value-adding network as a key enabling technology to achieve reconfiguration of production systems and networks. The concept has been projected upon an industrial case in order to validate its potential. [ABSTRACT FROM AUTHOR]

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An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers' cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. [ABSTRACT FROM AUTHOR]

Purpose: Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes. Design/methodology/approach: Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case. Findings: Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process. Practical implications: Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance. Originality/value: This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain. [ABSTRACT FROM AUTHOR]

With access to a set of previously unpublished data focusing on implementation of digital manufacturing capabilities (Industry 4.0, Smart Manufacturing, and digital manufacturing), we attempt to identify recurring themes inhibiting their adoption, particularly focusing on small- and medium-sized manufacturers (SMMs). The data from webinar surveys and industry interviews revealed specific insights into a lack of digital readiness of the US industrial base. While larger manufacturers can be well-positioned to take advantage of new digital capabilities due to their size and resource availability, their small- and medium-sized suppliers often lag. This phenomenon can be a significant roadblock to the adoption of Industry 4.0 capabilities and its promised benefits. Our research indicates that most SMMs are not in a position to adopt these advanced manufacturing technologies and lack awareness and understanding of what "digital manufacturing" means. The investigation echoes similar issues documented and described by others in Europe, Australia, and Asia in addition to other research findings within the USA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The world economy is witnessing the rapid digitization of global production, trade, and consumption of goods and services, which is significantly impacting economic activities in both developing and least developed countries. In this article, we examine and review the evolving framework of the national e-commerce policy in India and map digital policies in the broader contours of the industrial policy framework. Findings of this article show that interventionist policies such as localization of data and restrictions on cross-border data flows are vital for building domestic digital infrastructure, firms, platforms, and capabilities. [ABSTRACT FROM AUTHOR]

The present work provides an overview on an additive manufacturing (AM) design case of a novel battery cell lid structure (patent pending) for electrical vehicle applications. The benefits of AM have not yet been explored on metal case structures of prismatic battery cells. The method allows the manufacturing of complex hollow structures and integration of multiple functions in one part. The main challenge is to address thermal management in an optimal location in the battery cell. More efficient charging and discharging by maintaining the batteries at optimum operating conditions allows a longer battery lifetime. Recent research shows that elevating the charging temperature enables significantly shorter charging times. The aim of this study is to develop a lid structure to support higher peak current, faster charging, and reduced production steps and enable mass customization. The optimum performance simulated with computational fluid dynamics calculations is realized to determine the optimum design. The design case study is verified via laser powder bed fusion prototypes. This study shows that it is possible to produce integrated thermal management liquid channels to the battery lid. Significant improvement is achieved with localized battery cell temperature management. The novel design integrates six critical functionalities of the lid in one part. The design of the features is optimized to avoid support structures in AM and to maximize the number of parts in the printing chamber volume. The better thermal management extends the driving range of the vehicle and improves vehicle safety. Reducing the parts significantly simplifies cell production. [ABSTRACT FROM AUTHOR]

• Lead-in results of a multi-center, Phase-II trial on 3D-printed oral stents for oropharyngeal cancer patients. • Leveraging intraoral scanning, CAD, and 3D printing for efficient, cost-effective stent production. • Quality of intraoral scanning and streamlined centralized manufacturing and shipping ensures timely stent delivery. • Patients tolerate 3D-printed oral stents well during chemoradiation. We describe the development of 3D-printed stents using our digital workflow and their effects on patients enrolled in the lead-in phase of a multi-center, randomized Phase-II trial. Digital dental models were created for patients using intraoral scanning. Digital processes were implemented to develop the mouth-opening, tongue-depressing, and tongue-lateralizing stents using stereolithography. Time spent and material 3D-printing costs were measured. Physicians assessed mucositis using the Oral Mucositis Assessment Scale (OMAS) and collected MD Anderson Symptom Inventory (MDASI) reports and adverse events (AEs) from patients at various time points (TPs). OMAS and MDASI results were evaluated using paired t -test analysis. 18 patients enrolled into the lead-in phase across 6 independent clinical sites in the USA. 15 patients received stents (average design and fabrication time, 8 h; average material 3D-printing cost, 11 USD). 10 eligible patients with complete OMAS and MDASI reports across all TPs were assessed. OMAS increased significantly from baseline to week 3 of treatment (mean difference = 0.34; 95 % CI, 0.09–0.60; p = 0.01). MDASI increased significantly from baseline to week 3 of treatment (mean difference = 1.02; 95 % CI, 0.40–1.70; p = 0.005), and week 3 of treatment to end of treatment (mean difference = 1.90; 95 % CI, 0.90–2.92; p = 0.002). AEs (grades 1–3) were reported by patients across TPs. Mucositis and radiation dermatitis were primarily attributed to chemoradiation. 3D-printed stents were successfully fabricated and well tolerated by patients. As patients enroll in the randomized phase of this trial, data herein will establish a baseline for comparative analysis. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]