Search Results (58)

In order to enhance the management and application of fault knowledge within intelligent production lines, thereby increasing the efficiency of fault diagnosis and ensuring the stable and reliable operation of these systems, we propose a fault diagnosis methodology that leverages knowledge graphs. First, we designed an ontology model for fault knowledge by integrating textual features from various components of the production line with expert insights. Second, we employed the ALBERT–BiLSTM–Attention–CRF model to achieve named entity and relationship recognition for faults in intelligent production lines. The introduction of the ALBERT model resulted in a 7.3% improvement in the F₁ score compared to the BiLSTM–CRF model. Additionally, incorporating the attention mechanism in relationship extraction led to a 7.37% increase in the F₁ score. Finally, we utilized the Neo4j graph database to facilitate the storage and visualization of fault knowledge, validating the effectiveness of our proposed method through a case study on fault diagnosis in CNC machining centers. The research findings indicate that this method excels in recognizing textual entities and relationships related to faults in intelligent production lines, effectively leveraging prior knowledge of faults across various components and elucidating their causes. This approach provides maintenance personnel with an intuitive tool for fault diagnosis and decision support, thereby enhancing diagnostic accuracy and efficiency. Full article

This work aimed to develop both a real implementation and a digital twin for a small CNC machining center. The X-, Y-, and Z-axes feed systems were realized as closed-loop motion loops with DC servo motors and encoders. Motion control was provided by Arduino boards and Pololu motor drivers. A simulation study of the step response parameters was carried out, and then the positioning regime was studied, followed by the two-axis simultaneous motion regime (circular interpolation). This study, based on a hybrid simulation diagram realized in Simulink–Simscape, allowed a preliminary tuning of the PID (proportional integral derivative) controllers. Next, the CAE (computer-aided engineering) simulation diagram was complemented with the CAM (computer-aided manufacturing) simulation interface, the two together forming an integrated digital twin system. To validate the contouring performance of the proposed CNC system, a circular groove with an outer diameter of 31 mm and an inner diameter of 29 mm was machined using a 1 mm cylindrical end mill. The trajectory followed the simulated 30 mm circular path. Two sets of controller parameters were applied. Dimensional accuracy was verified using a GOM Atos Core 200 optical scanner and evaluated in GOM Inspect Suite 2020. The results demonstrated good agreement between simulation and physical execution, validating the PID tuning and system accuracy. Full article

Hybrid additive manufacturing (AM) provides a unique way of fabricating complex geometries with onboard machining capabilities, combining both additive and traditional subtractive techniques and resulting in reduced material waste and efficient high-tolerance components. In this work, a hybrid AM technology was used to create 316L stainless steel (316L SS) components using laser-wire-directed energy deposition (LW-DED) coupled with a CNC machining center on a single platform. Fully reversed fatigue tests were completed to investigate the as-manufactured life span of the additively manufactured structures for three different deposition rates of 6.33 g/min, 7.12 g/min, and 7.91 g/min. High-cycle fatigue test results showed that the fatigue performance of the tested specimens is not dependent on the deposition rates for the investigated parameters, with specimens with a 7.12 g/min deposition rate showing comparatively superior behavior to that of the other deposition rates at higher stress amplitudes. Fractography analysis was used to investigate the fractured surfaces, showing that the crack initiation sites were predominantly near the edges and not affected by the volumetric defects generated during manufacturing. X-ray-computed tomography (X-ray CT) analysis quantified the effect of the as-manufactured porosity on fatigue behavior, showing that the amount of porosity for the build rates used was insufficient to have a substantial impact on the fatigue behavior, even as it increased with the deposition rate. Full article

Titanium alloys are widely used in medicine due to their unique properties, including inertness with respect to living tissues, light weight, high strength, and impact toughness. For successful implementation, titanium alloy implants should possess high wear resistance and hydrophilicity. This article investigates the surface modification process of VT-1 and VT-6 titanium alloys by electron-beam processing (EBP). The EBP effect on the modified surface′s wear resistance, roughness, and hydrophilicity was analyzed. The specimens were made by machining them at a CNC turning center. The specimen surfaces were modified at the SOLO facility by a submillisecond modulated electron beam with a controlled power density of thermal impact, allowing it to reach and stabilize 1400 °C in 400 µs and then maintain it on the surface for 600 µs. A friction machine with a counterbody was used to study the wear resistance of the specimen surface. The study revealed that EBP reduces the roughness parameters of the surface. EBP also decreases the contact angle of wetting, indicating an increase in hydrophilicity compared to the original surface. Experimentally, it was shown that the formation of a nanostructure consisting of needle-like α-strips induced by EBP improves the wear resistance of the surface layer. Full article

The quality of the processed products in CNC machining centers is a critical factor in manufacturing equipment. The anomaly detection and predictive maintenance functions are essential for improving efficiency and reducing time and costs. This study aims to strengthen service competitiveness by reducing quality assurance costs and implementing AI-based predictive maintenance services, as well as establishing a predictive maintenance system for CNC manufacturing equipment. The proposed system integrates preventive maintenance, time-based maintenance, and condition-based maintenance strategies. Using continuous learning based on long short-term memory (LSTM), the system enables anomaly detection, failure prediction, cause analysis, root cause identification, remaining useful life (RUL) prediction, and optimal maintenance timing decisions. In addition, this study focuses on roller-cutting devices that are essential in packaging processes, such as food, pharmaceutical, and cosmetic production. When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments. Full article

As the structural complexity of machined components increases and the pace of product updates accelerates, the demands for machining precision in CNC machine tools are becoming increasingly rigorous. Consequently, the continuous enhancement of machining accuracy in machine tools presents a significant challenge that must be addressed within the realms of machine tool innovation and the development of manufacturing equipment. This paper conducts a comprehensive investigation into the tolerance optimization allocation method for geometric accuracy in precision horizontal machining centers utilizing interval theory. Initially, a mapping model is developed to represent each source of geometric error and the overall spatial error, drawing upon multi-body system theory. Subsequently, the global maximum interval sensitivity of each geometric error source in relation to the overall spatial model is analyzed. Finally, an interval optimization model for geometric accuracy is formulated based on interval optimization theory, employing a genetic algorithm to address the accuracy allocation problem associated with various error sources in machine tools. Full article

Extended reality (XR) as an immersive technology has gained significant interest in the industry for training and maintenance tasks. It offers an interactive, three-dimensional environment that can boost users’ efficiency and safety in various sectors. The present systematic review provides information based on a Scopus database search for research articles from 2011 to 2024 to expose 19 selected studies related to XR developments and approaches. The purpose is to grasp the state of the art, focusing on user training in goals or tasks that involve computer numerical control (CNC) machines. The study revealed approaches that broadly employed XR devices to execute diverse operations for virtual CNC machines, offering enhanced safety and skills acquisition, lessening the use of physical machines that impact energy consumption or the time invested by an expert worker to teach an operation task. The articles highlight the advantages of XR training versus traditional training in CNC machines, revealing an opportunity to enhance learning aligned to the industry 4.0 (I4.0) paradigm. Virtual reality (VR) and augmented reality (AR) applications are the most used and are mainly centered on a single-user environment. In addition, a VR approach is built as a proof of concept for learning CNC machine operations, considering the key features identified. Full article

Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their pivotal role lies in augmenting tool lifespan, minimizing machine downtime, and elevating productivity, thereby contributing to industry growth. However, the efficacy of CNC machine TCM hinges upon multiple factors, encompassing system type, data precision, reliability, and adeptness in data analysis. Globally, extensive research is underway to enhance real-time TCM system efficiency. This review focuses on the significance and attributes of proficient real-time TCM systems of CNC turning centers. It underscores TCM’s paramount role in manufacturing and outlines the challenges linked to TCM data processing and analysis. Moreover, the review elucidates various TCM system variants, including cutting force, acoustic emission, vibration, and temperature monitoring systems. Furthermore, the integration of industrial Internet of things (IIoT) and machine learning (ML) into CNC machine TCM systems are also explored. This article concludes by underscoring the ongoing necessity for research and development in TCM technology to empower modern intelligent industries to operate at peak efficiency. Full article

In this study, we performed milling machining on carbon-epoxy polymer composites and jute-epoxy composites using a CNC vertical machining center. We focused on spindle speed, feed rate, depth of cut, and flute number and analyzed the cutting force and surface roughness. The optimal parameter combination to reduce cutting force in both composites was as follows: S = 600 rpm, FR = 100 mm/min, DOC = 0.25 mm, and FN = 6. The jute-epoxy composites required less cutting force (11.85 N/m²) compared to the carbon-epoxy composites (18.77 N/m²). The average surface roughness of the carbon-epoxy composites (6.685 µm) is higher than that of the jute-epoxy composites (3.08 µm). The type of reinforced material used greatly affects the cutting force and surface roughness during milling. Full article

This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector machines (SVMs), and boosting ensemble decision trees, utilizing sensor data from a CNC machining center. These methods focus on the challenges and importance of feature selection, data preprocessing, and the application of tailored machine learning models to specific industrial tasks. Results show that SVM, with an accuracy of 96%, excels in handling high-dimensional data and robust feature extraction. In contrast, LSTM, which is appropriate for sequential data, is constrained by limited training data and the absence of pre-trained networks. Boosting ensemble decision trees also demonstrate efficacy in reducing model bias and variance. Conclusively, selecting an optimal machine learning strategy is crucial, depending on task complexity and data characteristics, highlighting the need for further research into domain-specific models to improve performance in industrial settings. Full article

The drive at the center of gravity (DCG) concept-based twin-ball-screw drive mechanism (TBSDM) is vital in automated factories for its robustness and reliability. However, changes in the worktable mass or position result in changes in the center of gravity (CG), significantly affecting the system’s dynamic properties. In this regard, this paper introduces a novel analytical model using improved receptance coupling to analyze vibrations in four modes. A mathematical framework for the twin TBSDM is generated, and the effect of changing the worktable position–mass on each mode is examined. The applicability of the proposed method is verified based on dynamic experiments that were carried out on a TBSDM of a CNC grinding wheel machine tool. After thoroughly analyzing the experimental and theoretical results, it is revealed that changing the worktable position primarily influences the rotational and axial vibrations of the twin ball screw (TBS). Furthermore, changes in the worktable mass significantly affect the coupling vibration among the TBSs and rotors or bearings. Moreover, in terms of performance, the variances between the theoretical and experimental natural frequencies are consistently below 5%. Thus, the proposed method is promising for the improvement of the modeling and analysis of the TBSDM. Full article

This paper explores the interaction between cutting parameters and the geometric accuracy of machined holes in a variety of engineering plastics, with the aim of improving manufacturing processes in the plastic processing industry. In the context of fast and precise manufacturing technology, the accuracy of drilled holes in polymers is of paramount importance, given their essential role in the assembly and functionality of finished parts. The objective of this research was to determine the influence of cutting speed and feed rate on the diameter and cylindricity of machined holes in six diverse types of plastics using a multilevel factorial design for analysis. The key message conveyed to the reader highlights that careful selection of cutting parameters is crucial to achieving high standards of accuracy and repeatability in plastic processing. The methodology involved structured experiments, looking at the effect of changing cutting parameters on a set of six polymer materials. A CNC machining center for drills and high-precision measuring machines were used to evaluate the diameter and cylindricity of the holes. The results of ANOVA statistical analysis showed a significant correlation between cutting parameters and hole sizes for some materials, while for others the relationship was less evident. The conclusions drawn highlight the importance of optimizing cutting speed and feed rate according to polymer type to maximize accuracy and minimize deviations from cylindricity. It was also observed that, under selected processing conditions, high- and medium-density polyurethane showed the best results in terms of accuracy and cylindricity, suggesting potential optimized directions for specific industrial applications. Full article

The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and vibrodiagnostics were carried out before disassembling the milling head in the idle state. The bearings, lubricant, and friction regime were analyzed in the next step. The vibrodiagnostic methods used included VEL, ACC, EN2, EN3, and EN4, with recommended limits conforming to STN ISO 10816-3. The vibration values obtained indicated a problem with the bearings, exceeding the limit values. After disassembly of the bearings, abrasive wear, corrosion, and improper lubricant conditions were detected. Lubricant analysis showed the presence of abrasive and corrosive particles, indicating an unsatisfactory friction regime. Determining the optimum lubricant temperature and the effect on friction torque constituted other aspects of the study. Inspection of the bearing microgeometry confirmed unsatisfactory roundness. Furthermore, the assembly of tapered roller bearings with axial preload was analyzed with a focus on bearing stiffness, accuracy, and life. The results showed that preload improves shaft guidance accuracy and load distribution, promoting reliable operation and extending bearing life. Full article

Advances in machining technology and materials science impose the identification of optimal settings for process-related parameters to maintain high quality and process efficiency. Given the available resources, manufacturers should determine an advantageous process parameter range for their settings. In this work, the machinability of a special tool steel (UNIMAX^® by Uddeholm, Sweden) under dry CNC turning is investigated. The working material is examined under two states; annealed and hardened. As major machinability indicators, main cutting force Fz (N) and mean surface roughness Ra (μm) were selected and studied under different values for the cutting conditions of cutting speed, feed rate, and depth of cut. A systematic experimental design was established as per the response surface methodology (RSM). The experimental design involved twenty base runs with eight cube points, four center points in the cube, six axial points, and two center points in the axial direction. Corresponding statistical analysis was based on analysis of variance and normal probability plots for residuals. Two regression models referring to main cutting force and surface roughness for both the annealed and hardened states of the material were developed and used as objective functions for subsequent evaluations by three modern meta-heuristics under the goal of machinability optimization, namely multi-objective grey wolf algorithm, multi-objective multi-verse algorithm and multi-objective ant lion algorithm. All algorithms were found capable of providing beneficial Pareto-optimal solutions for both main cutting force and surface roughness simultaneously whilst regression models achieved high correlation among input variables and optimization responses. Full article

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model’s ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology. Full article