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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.

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.

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.

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.

Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system.

The fourth industrial revolution is the transformation of industrial manufacturing into smart manufacturing. The advancement of digital technologies that make the trend Industry 4.0 are considered as the transforming force that will enable this transformation. However, Industry 4.0 digital technologies need to be connected, integrated and used effectively to create value and to provide insightful information for data driven manufacturing. Smart manufacturing is a journey and requires a roadmap to guide manufacturing organizations for its adoption. The objective of this paper is to review different methodologies and strategies for smart manufacturing implementation to propose a simple and a holistic roadmap that will support the transition into smart factories and achieve resilience, flexibility and sustainability. A comprehensive review of academic and industrial literature was preformed based on multiple stage approach and chosen criteria to establish existing knowledge in the field and to evaluate latest trends and ideas of Industry 4.0 and smart manufacturing technologies, techniques and applications in the manufacturing industry. These criteria are sub-grouped to fit within various stages of the proposed roadmap and attempts to bridge the gap between academia and industry and contributes to a new knowledge in the literature. This paper presents a conceptual approach based on six stages. In each stage, key enabling technologies and strategies are introduced, the common challenges, implementation tips and case studies of industrial applications are discussed to potentially assist in a successful adoption. The significance of the proposed roadmap serve as a strategic practical tool for rapid adoption of Industry 4.0 technologies for smart manufacturing and to bridge the gap between the advanced technologies and their application in manufacturing industry, especially for SMEs.

This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.

The fourth industrial revolution is characterized by the introduction of the Internet of things (IoT) and Internet of Services (IoS) concepts into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The main driver is technology, as Industry 4.0 is a collective term for technologies and concepts of value chain organization. Digital manufacturing platforms play an increasing role in dealing with competitive pressures and incorporating new technologies, applications, and services. Motivated by the difficulties to understand and adopt Industry 4.0 and the momentum that the topic has currently, this paper reviews the concepts and approaches related to digital manufacturing platforms from different perspectives: IoT platforms, digital manufacturing platforms, digital platforms as ecosystems, digital platforms from research and development perspective, and digital platform from industrial equipment suppliers.