Search Results (244)

The significant challenges of machining hard-to-cut materials pose an important problem for the manufacturing industries, as it can lead to increased tool wear, higher machining costs, and reduced productivity. Apart from experimental investigations, which are rather expensive and cannot always provide a comprehensive view of the process outcome due to limitations in measurement techniques, it is possible to use validated models to predict the temperature and stress state of the workpieces or test the effect of different process conditions. Although many Finite Element (FE) models have been developed for the turning process, usually accurate representation of the machining setup with a realistic 3D geometry for both cutting tool and workpiece is not taken into account. Thus, in this work, two different representations of the machining setup, including curved workpiece geometry, which is more rarely studied, are compared for the case of Ti-6Al-4V ELI turning under various conditions, and their effect on the accuracy of the prediction of the cutting force and chip morphology is investigated. It was found that the model with the straight workpiece overpredicts the cutting force to a higher extent compared to the model with the curved workpiece and also predicts a much higher workpiece temperature, whereas chip morphology was mainly affected by feed rate. No noticeable differences were observed between the two models. These results indicate that in most cases, the use of geometry with curved workpiece is more suitable for better prediction of the cutting forces. Full article

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm. Full article

The reuse of WC–Co cutting inserts is a relevant strategy to reduce tooling costs and the consumption of critical raw materials, such as W and Co. Still, the effect of stripping and re-coating cycles on tool performance remains largely unexplored. This work investigates the wear behavior of carbide inserts coated with four PVD nitride systems—CrN, TiAlN, TiAlCrN, and TiAlCrSiN—during CNC turning of AISI 4340 steel. A single cutting edge was subjected to two complete reuse cycles consisting of machining six workpieces, electrolytic stripping of the worn coating, and PVD re-deposition. Tool wear and surface integrity were evaluated by 3D optical profilometry, roughness measurements, and SEM/EDS analysis. CrN exhibited progressive crater and flank wear with large material-loss volumes and increasing roughness. TiAlN exhibited pronounced built-up edge/layer formation, resulting in mixed adhesion–spallation behavior and degradation of roughness in the second cycle. TiAlCrN developed stable adhesive layers with limited coating loss, and after re-coating, its roughness decreased from ~2.7 µm to ~1.0 µm. The most complex coating, TiAlCrSiN, provided the lowest roughness (~1.3–1.6 µm) and the smallest wear volumes in both cycles, associated with a fine Al–Si-induced nanostructure and improved oxidation resistance. The results demonstrate that multicomponent nanostructured coatings, particularly TiAlCrN and TiAlCrSiN, can withstand at least one stripping and re-coating cycle without performance loss, supporting the feasibility of controlled insert reuse in turning AISI 4340 steel. Full article

Accurate tool wear identification is essential for ensuring the continuity of intelligent machining and workpiece quality. To address the challenges of multi-source fusion inefficiency and inadequate feature extraction, this study proposes a novel identification architecture combining physics-guided multi-channel Gramian angular field (PG-MGAF) with a minimalist 14-layer CSA-Kansformer network. Multi-source signals are preprocessed via PG-MGAF to convert 1D time-series into 2D RGB images, effectively characterizing spatial coupling and interactive energy across three channels. Subsequently, the minimalist network maps these composite features to tool states, significantly reducing computational overhead. Experimental results demonstrate that the proposed model achieves an average accuracy of 93.6% with a single-step inference latency of only 5.90 ms, significantly outperforming mainstream methods such as MobileNet-V2 and ConvNeXt. This architecture provides a high-efficiency, low-latency solution for real-time tool condition monitoring under complex industrial conditions. Full article

Trajectory planning algorithms are essential in human–robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a virtual model of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant’s left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in HRC environments. Full article

To address the multi-sensor collaborative calibration challenges in laser 3D projection systems, a pose calibration method integrating binocular vision and laser ranging is proposed. A multi-coordinate system fusion framework encompassing the camera coordinate system, galvanometer coordinate system, and workpiece coordinate system is established. Through the calculation of reference pose matrices and real-time transformations, adaptive calibration under arbitrary workpiece placements is achieved. Experimental results demonstrate that within a working range of 1.5–2.5 m, the calibration error is 45.5 $\mathsf{\mu }$m, meeting the high-precision requirements of aerospace precision machining and assembly. Full article

The surface quality of granite cut by diamond wire saw significantly impacts the cost of subsequent processes such as grinding and polishing. Traditional evaluation parameters like surface roughness (Ra) or peak-to-valley value (PV) face challenges in characterizing the surface morphology. This study introduces fractal dimension (FD) as a potential auxiliary parameter for evaluating the surface quality of sawn granite. Cutting experiments were conducted on Shanxi Black granite using varying wire speeds, feed speeds, and workpiece sizes. The box-counting method was employed to extract the three-dimensional fractal dimension (3D FD) of the granite surface, which characterizes the overall surface complexity, as well as the distribution of two-dimensional fractal dimensions (2D FD) for granite surface cross-sectional profiles at different angles. The results indicate that the granite-sawn surface exhibits complex micro-morphology featuring brittle micro-pits and wavelike saw marks along the feed direction. A strong negative correlation exists between the 3D FD and both surface roughness Ra and PV value, suggesting that 3D FD can serve as an indicator of granite surface quality, with higher FD values corresponding to better surface quality. Moreover, compared to the PV value constrained by material heterogeneity, 3D FD more effectively represents the true surface quality of the granite. Additionally, the distribution characteristics of 2D FD at different angles effectively reveal surface anisotropy and damage. The results suggest that a more symmetrical 2D FD distribution is associated with consistent surface integrity in the evaluated samples. This suggests that FD has the potential to serve as a meaningful auxiliary parameter for characterizing granite surface quality. The findings hold significant importance for the accurate evaluation of diamond wire-saw-cut granite surfaces and provide a basis for the formulation of subsequent grinding process. Full article

A galvanometer-based laser 3D projection system requires accurate mapping between galvanometer control signals and workpiece coordinates to ensure reliable on-part marking. This study presents a calibration and verification pipeline that uses a color camera and a depth sensor to reconstruct 3D target points and estimate the extrinsic parameters between the projector and the workpiece. Laser spot centers are localized in color images, and corresponding depth values are acquired after color–depth alignment. The resulting 3D points are back-projected and transformed into the workpiece coordinate frame. A hybrid solver is employed: the Whale Optimization Algorithm (WOA) provides a global initial estimate, followed by Levenberg–Marquardt (LM) refinement to enhance convergence stability under noisy and small-sample conditions. Experimental validation on an independent 13-point set demonstrates sub-millimeter accuracy, with a mean error of approximately 0.37 mm and a maximum error of 0.87 mm. A further rectangular contour projection test confirms consistent performance, yielding a mean error of 0.434 mm and a maximum error of 0.879 mm, with all errors remaining below 1 mm. Full article

The multi-jet polishing process (MJP) demonstrates high shape accuracy and surface quality in the machining of nonlinear and complex surfaces, and it achieves precise and adjustable material removal rates through computer control. However, there are still challenges in terms of machining efficiency, system complexity, and stability. In particular, maintaining the polishing quality presents a greater challenge when working conditions change. To overcome these issues, this paper conceptually proposes a digital twin (DT)-driven, human-centric design framework that integrates key factors of MJP, such as jet kinetic energy, nozzle structure, abrasive type, and machining path. Within this framework, a feature-encoded transfer learning-based model is introduced to enhance surface roughness prediction accuracy and robustness under varying working conditions. The effectiveness of the proposed model was verified by conducting experiments on 3D printed workpieces under two different MJP working conditions. The results show that our proposed method yields better predictive performance and cross-condition adaptability. Overall, this work provides a predictive modeling component that supports DT-driven process design, offering a practical and extensible perspective for optimizing complex ultra-precision manufacturing processes under data-scarce and uncertainty-dominated conditions. Full article

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces. Full article

Semantic segmentation of workpieces and die cavities is critical for intelligent process monitoring and quality control in hammer die-forging. However, the field of 3D point cloud segmentation currently faces prominent limitations in forging scenario adaptation: existing state-of-the-art (SOTA) methods are predominantly optimized for road driving or indoor scenes, where targets have stable poses and regular surfaces. They lack dedicated designs for capturing the fine-grained deformation characteristics of forging workpieces and alleviating multi-scale feature misalignment caused by large pose variations—key pain points in forging segmentation. Consequently, these methods fail to balance segmentation accuracy and real-time efficiency required for practical forging applications. To address this gap, this paper proposes a novel semantic segmentation framework fusing 3D point cloud and bird’s-eye-view (BEV) representations for complex die-forging scenes. Specifically, a Star-based encoding module is designed in the BEV encoding stage to enhance capture of fine-grained workpiece deformation characteristics. A hierarchical feature-offset alignment mechanism is developed in decoding to alleviate multi-scale spatial and semantic misalignment, facilitating efficient cross-layer fusion. Additionally, a weighted adaptive fusion module enables complementary information interaction between point cloud and BEV modalities to improve precision.We evaluate the proposed method on our self-constructed simulated and real die-forging point cloud datasets. The results show that when trained solely on simulated data and tested directly in real-world scenarios, our method achieves an mIoU that surpasses RPVNet by $1.1\%$. After fine-tuning with a small amount of real data, the mIoU further improves by $5\%$, reaching optimal performance. Full article

Although RGB images contain rich details, they lack 3D depth information. Depth data, while providing spatial positioning, is often affected by noise and suffers from sparsity or missing data at key feature points, leading to low accuracy and high computational complexity in traditional visual localization. To address this, this paper proposes a high-precision, sub-pixel-level localization method for workpiece feature points based on RGB-D data fusion. The method specifically targets two types of localization objects: planar corner keypoints and sharp-corner keypoints. It employs the YOLOv10 model combined with a Background Misdetection Filtering Module (BMFM) to classify and identify feature points in RGB images. An improved Prewitt operator (using 5 × 5 convolution kernels in 8 directions) and sub-pixel refinement techniques are utilized to enhance 2D localization accuracy. The 2D feature boundaries are then mapped into 3D point cloud space based on camera extrinsic parameters. After coarse error detection in the point cloud and local quadric surface fitting, 3D localization is achieved by intersecting spatial rays with the fitted surfaces. Experimental results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.17 mm for localizing flat, free-form, and grooved components, with a maximum error of less than 0.22 mm, meeting the requirements of high-precision industrial applications such as precision manufacturing and quality inspection. Full article

This article investigates the potential of using polymer-based Fused Filament Fabrication (FFF) 3D printing to manufacture structural components. This study aimed to explore the application of this technology within manufacturing processes. This research focused on material analysis, using a gear wheel from a lawn mower’s drive system as a case study. To achieve the purpose of this study, a reverse engineering process was carried out to create 2D and 3D documentation based on a physical piece. The geometric model created in the CAD environment was optimized for volume. It was intended to keep the material expenditure unchanged, which is intensified during the 3D printing process due to the need to apply an adhesive layer and supports. The final design process of the geometric model of the prototype was subjected to numerical analyses in terms of total deformation and reduced stresses for materials commonly used as filaments in the FDM/FFF 3D printing technology. The basic filaments PLA, ABS, and PA6 and PA12 were analyzed in this study. The results of the analyses showed that two of the four selected filaments had to be rejected due to significant deterioration in strength properties. Finally, the prototype workpieces were printed using materials approved for the manufacturing process. Full article

Ammonia, widely regarded as the “hydrogen carrier of the future,” offers high hydrogen content, ease of production, and a well-established infrastructure for handling and transportation globally. Meanwhile, ammonia cracking requires a heat supply at high temperatures, and induction heating provides efficient, precise, and rapid heating to conductive materials of different shapes and sizes. Therefore, this work presents a proof of concept for ammonia cracking using induction heating with three different reactor configurations: (1) a 3D metal workpiece; (2) a 3D metal workpiece and Ni/Al₂O₃ catalyst; and (3) only Ni/Al₂O₃ catalyst. The performance of the inductively heated reactor is also compared to an electric furnace. The results showed that the reactor with the workpiece and the catalyst required 97 W to reach 650 °C, being the most efficient in terms of power usage when compared to the workpiece alone and the electric tube furnace, which required 39% and 132% more, respectively; the least efficient configuration is with just the catalyst, needing 138 W to reach just 116 °C. Overall, the introduction of the 3D workpiece allowed for fast and uniform conversion and heating within the reactor, enabling efficient and dynamic process control when applying induction heating to chemical reactors. Full article

Surface finishing processes are required to produce the final shape of components made of the silicon-infiltrated silicon carbide composite Cesic^® from ECM (Engineered Ceramic Materials GmbH, 85452 Moosinning, Germany). Electrical discharge machining (EDM) is still the most effective method for manufacturing pockets and mounts in 3D-shaped ceramic satellite components for space applications. NC-grinding is not used, because it results in high grinding loads and rapid tool wear when applied to Cesic^®. In contrast to planar machining, tool wear during NC-grinding with small tools is particularly critical, as it alters the tool geometry and consequently causes deviations in the workpiece geometry. Ultrasonic-assisted grinding offers a promising alternative to overcome the low material removal rates and long processing times associated with EDM while simultaneously enhancing tool life, thus enabling more economical and reliable production. In this experimental study, both conventional grinding (CG) and ultrasonic-assisted grinding (UAG) processes are compared and used to machine Cesic^®. In order to verify the effect of the ultrasonic vibration, analyses of amplitude and frequency are performed. During machining experiments, the grinding loads are measured. The influence of different machining conditions on surface quality is evaluated concerning the roughness of the machined specimens. Compared to CG, UAG shows lower tool wear, owing to the self-cleaning effects caused by the ultrasonic oscillation of the tool. Consequently, the stability of the NC-grinding process is significantly improved. Full article