Advanced Manufacturing Technologies: Development and Prospect

Abstract

The main aim of this Special Issue was to present the current state of the research on the subjects of theory, modeling, monitoring, and control of the operation of technology systems and processes, along with research and diagnostics of manufacturing systems and processes operation. The contributions have focused on manufacturing research, operation reliability, and diagnostics of machines; inspection, measurements, evaluation, and diagnostics of production quality in technologies of standard and progressive machining; reversible engineering; 3D printing; pressure die casting; injection molding; EDM; AWJ cutting; etc., which are used for advanced processing of materials and various kinds of technological applications.

1. Introduction

The manufacturing industry is currently experiencing a paradigm shift, driven by the integration of advanced technologies. These innovations have been shown to enhance efficiency, precision, and sustainability, thereby transforming production processes and supply chains. Emerging trends in the field, including automation, artificial intelligence (AI), additive manufacturing [1,2], and smart manufacturing systems, are collectively driving a paradigm shift in traditional manufacturing practices. Concomitantly, the mounting emphasis on sustainability has precipitated the emergence of eco-friendly materials and energy-efficient production techniques [3,4].

The evolution of manufacturing technologies has paved the way for increased efficiency, customization, and sustainability [5,6]. The integration of automation [7], additive manufacturing [8], and digital technologies [9,10] has transformed traditional manufacturing into a more intelligent [11] and adaptive industry. In order to maintain competitiveness in the global market, it is imperative that industries continue to embrace innovation as these technologies advance further.

The theme of this Special Issue in the field of advanced manufacturing technologies has been met with a positive response from the scientific community. The significant engagement of the scientific community is evidenced by the numerous views on the articles, but more importantly by the rich citation response. To date, the articles in this Special Issue have been cited a total of 107 times, with an average of 5.1 citations per article, and this figure continues to increase. The success of the response can be attributed to the universality of the challenges and perspectives in this field, which are being addressed based on familiar approaches in various settings. The positive response to the initiative to collate and disseminate the latest findings in the field resonated and was confirmed, somewhat surprisingly, across a broad base of research areas. Conversely, the objectives of all the contributions are closely related.

2. Overview of Published Articles

In their study, Ramirez et al. (1) investigated the implementation of a 5G digital twin (DT) in warehouse logistics, emphasizing dynamic network monitoring and cybersecurity. Their study highlighted the role of network function virtualization (NFV) in securing IoT devices through advanced encryption standards (AES). The utilization of simulation tools such as Altair Feko and GNS3 demonstrated the potential of digital twins to enhance communication capabilities within warehouse environments. Matos et al. (2) propose a model that integrates electricity cost consumption and power demand into aggregate production planning, addressing market uncertainties and the rising costs of electricity. Applied in the food industry, the model utilized the Holt–Winters forecasting method and successfully optimized power demand, reducing workers labor and achieving cost savings while minimizing overall production expenses.

A methodology for determining the optimal number of grinding operations in cold rolling, with the objective of reducing labor intensity and improving processing efficiency, was developed by Bratan et al. (3). The findings of the present study contribute to the refinement of industrial grinding techniques. Mascenik and Coranic’s (4) investigation into the coefficient of friction in screw joints revealed that lubrication significantly reduces friction. The study found that copper-based grease yielded the most significant reduction in friction, with a recorded 47.64% decrease. This outcome demonstrates the efficacy of copper-based grease in enhancing the efficiency and stability of the joints.

Ružbarský (5) conducted an investigation into the difficulties of using laser profilometry for roughness measurements on glossy surfaces. The study concluded that while non-contact laser methods provide useful data, they are less reliable for high-gloss materials like stainless steel and aluminum alloys. Iakovets et al. (6) explored the role of mobile applications in supporting SMEs’ transition to Industry 4.0. Their study highlighted how digital tools facilitate workforce adaptation, process optimization, and sustainable business development in manufacturing enterprises. Kuznetsov et al. (7) explore the optimization of combined wind–solar power plants for sustainable smart city development. To enhance energy efficiency and stability, the study proposes using autonomous renewable energy sources instead of traditional power grids. The research highlights the benefits of integrating a vertical Darier rotor with photovoltaic converters, improving heat dissipation and maximizing energy output. Through digital aerodynamic modeling, the study identifies optimal design parameters for wind turbines and a photovoltaic module tilt for a particular location, ensuring efficient energy utilization in urban environments.

Surface quality is a pivotal factor in determining the functionality and lifespan of machined components. Palová et al. (8) proposed an efficient method for evaluating surface roughness using a secondary surface roughness standard. Their study identified a uniform distribution law for measurement uncertainty assessment and optimized measurement parameters such as evaluation standards, measurement speed, and cut-off filters. The findings of this study are instrumental in reducing measurement uncertainty while minimizing financial costs. In their study, Chang et al. (9) proposed an incremental learning model using the YOLO framework to improve tire bubble defect detection via digital shearography. The efficacy of this approach was evidenced by the attainment of 98% detection accuracy, thereby addressing the limitations of conventional quality control methodologies and providing a foundation for automation in the context of tire manufacturing.

Mitaľ et al. (10) analyze the impact of technological parameters on the tribological properties of 3D-printed polyetherimide (PEI) models using the fused filament fabrication (FFF) technique. By varying build orientation and fiber deposition strategy, the study examines abrasive wear resistance through tribometer testing per ASTM standards. Results indicate that wear and friction force continuity depend on model orientation. Notably, samples with fiber orientation in the Z direction exhibited greater weight loss than those in the X direction, highlighting the influence of deposition strategy on wear performance.

Rimar et al. (11) proposed a methodology of successive approximation for the modeling of heat transfer and gas dynamics in combustion equipment. Their approach has been demonstrated to simplify complex mathematical relationships, offering practical solutions with controlled error rates of 5–7%, which remain acceptable for engineering applications. A hybrid PFMEA–DEMATEL–ERPN model with the objective of enhancing defect management in post-communist economies was introduced by Bujna et al. (12), and the efficacy of this approach was demonstrated by its superior performance in reducing defect occurrence and associated costs when compared with conventional PFMEA methods. Gulyaev et al. (13) present a method for monitoring nanoparticle and agglomerate sizes using a scanning probe microscope. The study outlines an image analysis process involving segmentation, particle allocation, and overlap correction to ensure accurate size measurements. Existing commercial microscopes and software were found to have limitations in complex image processing. To address this, the authors propose a curvature-based method for particle segmentation and size determination, along with the use of sample displacement sensors for improved image stitching, enhancing measurement precision. Ságová et al. (14) conducted an examination of friction anisotropy in gear transmissions, presenting a tensor-based model with the objective of enhancing the accuracy of friction loss calculations. The findings of the present study are of crucial importance for improving efficiency in mechatronic systems by refining mechanical models of frictional anisotropy.

Efficient data exchange in computer-aided design (CAD) and computer-aided manufacturing (CAM) systems is essential for maintaining accuracy in digital modeling. Kuryło et al. (15) examined system compatibility issues and neutral format effectiveness, focusing on 2D and 3D object transmission. Their research highlighted the need for ongoing development of data exchange standards to minimize errors in geometry transversions. The integration of Industry 4.0 technologies has had a profound impact on the realm of quality control in manufacturing. Saif et al. (16) introduced an Internet of Things (IoT)-based computer vision system for roundness measurement, employing image processing techniques to enhance accuracy. The system they developed offers a non-contact method capable of inspecting multiple rounded components, thereby significantly improving the efficiency of automated quality control processes. Deburring is of paramount importance in the domain of aerospace manufacturing, particularly in the context of jet engine component production. In their study, Falandys et al. (17) conducted an analysis of the automation of edge deburring and its impact on production parameters. Their findings indicate that automated systems provide enhanced repeatability, improved working conditions, and reduced labor fatigue in comparison to manual deburring, thereby significantly enhancing process efficiency.

Liu et al. (18) developed a bi-level optimization model for industrial park layouts, with the objective of minimizing association risks while maximizing rental profit. The model incorporated risk assessment calculations and an enhanced genetic algorithm for layout design, thereby demonstrating its efficacy through a real-world case study. Holubek and Vagaš (19) proposed a vision system for estimating center of gravity coordinates using an overall brightness average from a 3D vision system. Designed for advanced manufacturing and robotic applications, the system utilizes two CCD cameras and an RS 232C communication interface for real-time data exchange. By integrating 1D photogrammetric calibration and advanced image processing techniques, the system achieves a correlation factor of 84–100% for object recognition. Successful data transfer to a robotic system demonstrates its feasibility as a cost-effective alternative for industries seeking high-performance vision solutions without significant investment in cutting-edge technology.

The semiconductor industry is contingent on the transportation of wafers that are both highly precise and free from contamination. In this context, Ha et al. (20) developed a pioneering magnetic levitation-based wafer handling system that eliminates friction and particle generation. The study demonstrated the effectiveness of this system in achieving dust-free, high-speed transport and precise positioning, which is critical for ultra-fine semiconductor production. Gearing accuracy exerts a direct influence on the performance of mechanical systems. Mascenik et al. (21) investigated the influence of angular settings in hobbing mills on gear tooth profile modification. Their research demonstrated that varying the angular settings affects final gear geometry and identified optimization strategies for improving precision in gear manufacturing.

3. Conclusions

The evolution of manufacturing technologies has paved the way for increased efficiency, customization, and sustainability. The integration of automation, additive manufacturing, and digital technologies has transformed traditional manufacturing into a more intelligent and adaptive industry. In order to maintain competitiveness in the global market, it is imperative that industries continue to embrace innovation as these technologies advance further. Future research and investments in smart manufacturing will further enhance the capabilities of production systems, rendering them more resilient and environmentally responsible.

Consequently, the primary objective of the Special Issue has been accomplished. The papers presented have the potential to contribute to the reduction of the cost of manufacturing processes, to the promotion of simulation tools, and to the design of key elements and components to achieve exceptional quality and reliability in the manufacturing field.

The latest advancements in manufacturing technologies, drawing insights from recent research and its impact on industrial applications, provide visions for the future research and investments in smart manufacturing that will further enhance the capabilities of production systems, rendering them more resilient and environmentally responsible.