Search Results (27)

To address high-precision robotic abrasive belt grinding of 42CrMo steel, this study adopted the orthogonal central composite design with grinding force, feed rate, and rotational speed as key parameters, establishing and verifying regression models for material removal depth (MRD) and surface roughness (Ra). Results showed that the models’ relative errors are within 6% (MRD) and 10% (Ra). Grinding force and feed rate exert a strong coupling effect on MRD, while feed rate–rotational speed and grinding force–feed rate interactions significantly influence Ra, with “saddle-shaped” response surfaces. Grey relational analysis determined the optimal parameters: 75 N grinding force, 22.4 mm·s⁻¹ feed rate, and 3261 rpm rotational speed, achieving a maximum MRD of 1.975 mm and a minimum Ra of 3.506 μm. The tolerance range of the optimal parameters is F = 70–80 N, v_f = 20–24 mm·s⁻¹, and ω = 3000–3500 rpm. This research provides robust support for process parameter prediction and optimization in high-precision robotic abrasive belt grinding of 42CrMo steel. Full article

Abrasive belt wear has an important impact on the dimensional accuracy and surface quality of parts. Accurate quantitative measurement of abrasive belt wear is an important basis for optimizing grinding process parameters, but also a very challenging task for abrasive belts with randomly distributed abrasive particles. In this paper, a quantitative method of determining wear state based on the life cycle surface images of the abrasive belt is proposed to evaluate its material removal ability in the grinding process. For blunted abrasive particles with extremely irregular shapes, TransUNet with a hybrid encoding of a CNN and transformer is adopted to obtain strong representation of complex features and high-precision segmentation boundaries. Three other U-net-based semantic segmentation networks are compared to prove the effectiveness of the trained TransUNet model. The number and area of blunted abrasive particles were calculated by connected domain and statistical methods. The proportion of worn abrasive particles and the wear area ratio when the service life of the abrasive belt is exhausted are about 74.29% and 3.06%, respectively. Full article

Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a multi-view spark image detection method based on YOLOv8 transfer learning is proposed. A multi-pose spark image dataset including front, side, and 45° angle views is constructed, and the cross-view detection task is achieved for the first time. The generalization ability of the model is enhanced through the following innovative strategies: (1) a cross-view transfer learning framework based on dynamic anchor box optimization is designed, and the parameters of the front spark detection model YOLOv8 are transferred to the side and 45°-angle detection tasks; (2) an attention-guided feature alignment module is introduced to alleviate the feature distribution shift caused by view differences; and (3) a curriculum learning strategy is adopted, where the datasets of different views are trained separately first and then sampled to reconstruct the dataset for further training, gradually increasing the weight of samples from complex views. The experimental results show that on the self-built multi-view dataset (containing 3000 annotated images), this method achieves an average detection accuracy of 98.7%, which is 14.2% higher than that of the original YOLOv8 model. The inference speed reaches 55 FPS on an NVIDIA RTX 4090, meeting the requirements of industrial online monitoring. The research results provide key technical support for the intelligent prediction of the material removal rate in the precision machining of blades and have the potential for rapid deployment in industrial scenarios. Full article

Robotic grinding serves as a pivotal embodiment and key technological support of Industry 4.0. Elucidating the influence of robotic grinding parameters on the material removal depth (MRD) of 42CrMo steel and optimizing these parameters are critical to enhancing grinding efficiency and quality. In this study, the influences of revolution speed, feed speed, grinding force, and grit designation on MRD and surface Vickers hardness of 42CrMo steel were investigated by using an adaptive electro-hydraulic-actuated triangular abrasive belt in robot grinding. A predictive model for MRD of 42CrMo steel has been established using the orthogonal central composite design method. The results indicated that as the revolution speed or grinding increases, both MRD and surface hardness increase. However, as the revolution speed surpasses 4000 RPM or the grinding force exceeds 60 N, the increase of MRD becomes slower due to the increase in surface hardness. Both the MRD and surface hardness decrease continuously as the feed speed increases, and once it exceeds 15 mm·s⁻¹, the decrease of the MRD becomes slow. The rise in grit designation of the abrasive belt makes the MRD reduce gradually while the surface hardness rises slightly. The correlation coefficient of the predictive model is 0.9387, and the relative error between the predicted and experimental MRD is within 10%, indicating a relatively high accuracy. At the optimal grinding parameters (grinding force of 81 N, revolution speed of 4739 RPM, and feed speed of 7.6 mm·s⁻¹), the maximum MRD of 42CrMo steel achieved by an abrasive belt of 60 grit designation is 0.934 mm. This work provides a basis for high-precision robot abrasive belt grinding of 42CrMo steel. Full article

Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R² value of 0.919. Full article

For a comprehensive understanding of the surface roughness of wood substrates after grinding and its influence on the construction of surface structural color layers (SCLs) and the effect of color production, four typical diffuse-porous wood species were investigated by grinding with abrasive belts of different grits. The results indicated that an abrasive belt of suitable grit was required to form the flattest surface for different wood species. Notably, 400-grit abrasive belts can be used for quaking aspen (QA) and yellow poplar (YP) wood, while 320-grit abrasive belts can be used for kang duan (KD) and hard maple (HM) wood for the grinding process. When the grit of the belt was 80–240, the surface roughness of the wood was high, and the gully contour was mainly determined by the machining marks created by the grits during the cutting process. When the SCLs were constructed on these wood samples, the grooves formed by grit grinding caused the emulsion to overflow on the surface of the wood, thus preventing the formation of well-ordered SCLs with excellent color production. In contrast, when the grit of the abrasive belts was increased to the range of 320–800, the main factors affecting the roughness of the wood surface led to the anatomical structural features. Vessels, in particular, not only affected the surface roughness of the wood but also served as a major path for emulsion flow. The number, diameter, and patency of vessels per unit area were the main factors affecting the SCL’s construction and decoration effect on wood surfaces. This study clarifies that the roughness of the wood surface after the grinding process is jointly influenced by the grit of the abrasive belt and the wood’s anatomical structure. Roughness is an essential factor that affects the modification effect of the SCLs on the surface of wood. Full article

In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness were conducted. Firstly, an empirical formula for the surface roughness of titanium alloys after abrasive belt grinding was constructed based on the balanced weight analysis of the process parameters for titanium alloy surface roughness. Sensitivity analysis was carried out to identify the process parameters with the greatest effect on surface roughness, and the stable and unstable domains of the process parameters were determined. Combined with range analysis in orthogonal experiments, the influence curves of the process parameters on surface roughness were obtained, and the optimal parameter ranges were selected. The research results showed that surface roughness is the most sensitive to changes in abrasive grain size and the least sensitive to changes in abrasive belt linear speed. The optimal ranges of abrasive grain size, abrasive belt linear speed, and grinding pressure were determined to be 120# to 150#, 15 m/s to 20 m/s, and 10 N to 15 N, respectively. This study provides a theoretical method and experimental basis for the control of surface roughness in abrasive belt grinding of titanium alloys. Full article

As a flexible grinding method with high efficiency, abrasive belt grinding has been widely used in the machining of mechanical parts. However, abrasive belt grinding has not been well applied in the field of ultra-precision optical processing, due to the lack of a stable and controllable removal function. In this paper, based on the idea of deterministic machining, the time-controlled grinding (TCG) method based on the abrasive belt as a machining tool was applied to the deterministic machining of optical components. Firstly, based on the Preston equation, a theoretical model of the TCG removal function was established. Secondly, removal function experiments were carried out to verify the validity and robustness of the theoretical removal model. Further, theoretical and actual shaping experiments were carried out on 200 mm × 200 mm flat glass-ceramic. The results show that the surface shape error converged from 6.497 μm PV and 1.318 μm RMS to 5.397 μm PV and 1.115 μm RMS. The theoretical and experimental results are consistent. In addition, the surface roughness improved from 271 to 143 nm Ra. The results validate the concept that the removal function model established in this paper can guide the actual shaping experiments of TCG, which is expected to be applied to the deterministic machining of large-diameter optical components. Full article

As a high-performance and difficult-to-machine material for the manufacture of blades, titanium alloys are increasingly being used in high-end manufacturing industries such as aerospace and aircraft. As engineering applications become more demanding, so do the requirements for precision. However, to date, the choice of blade grinding parameters is still mainly dependent on the traditional “trial cut” and “experience” method, making the processing efficiency low and the quality of processing difficult to be guaranteed. In order to achieve the requirements of high precision and low surface roughness of the workpiece, to get rid of the status quo of relying on manual decision-making, and to achieve reasonable prediction and control of surface quality, this paper proposes to establish a theoretical prediction model for surface roughness of titanium alloy by abrasive belt grinding, and to analyze the influence of the main process parameters on surface roughness during the grinding process through experiments. A theoretical prediction model for surface roughness was developed. The experimental results show that the model has certain accuracy and reliability, and can provide guidance for the high-precision prediction of the surface roughness of ground titanium alloy blades, which has strong practical significance in engineering. Full article

The edge sharpness of a propeller blade plays a vital role in improving energy transmission efficiency and reducing the power required to propel the vehicle. However, producing finely sharpened edges through casting is challenging due to the risk of breakage. Additionally, the blade profile of the wax model can deform during drying, making it difficult to achieve the required edge thickness. To automate the sharpening process, we propose an intelligent system consisting of a six-DoF industrial robot and a laser-vision sensor. The system improves machining accuracy through an iterative grinding compensation strategy that eliminates material residuals based on profile data from the vision sensor. An indigenously designed compliance mechanism is employed to enhance the performance of robotic grinding which is actively controlled by an electronic proportional pressure regulator to adjust the contact force and position between the workpiece and abrasive belt. The system’s reliability and functionality are validated using three different workpiece models of four-blade propellers, achieving accurate and efficient machining within the required thickness tolerances. The proposed system provides a promising solution for finely sharpened propeller blade edges, addressing challenges associated with the earlier robotic-based grinding studies. Full article

High-performance grinding has been converted from traditional manual grinding to robotic grinding over recent years. Accurate material removal is challenging for workpieces with complex profiles. Over recent years, digital processing of grinding has shown its great potential in the optimization of manufacturing processes and operational efficiency. Thus, quantification of the material removal process is an inevitable trend. This research establishes a three-dimensional model of the grinding workstation and designs the blade back arc grinding trajectory. A prediction model of the blade material removal depth (MRD) is established, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Experiments were carried out using the Taguchi method to investigate how certain elements might affect the outcomes. An Analysis of Variance (ANOVA) was used to study the effect of abrasive belt grinding characteristics on blade material removal. The mean absolute percent error ($MAPE$) of the established ANFIS model, after training and testing, was 3.976%, demonstrating superior performance to the reported findings, which range from 4.373% to 7.960%. ANFIS exhibited superior outcomes, when compared to other prediction models, such as random forest (RF), artificial neural network (ANN), and support vector regression (SVR). This work can provide some sound guidance for high-precision prediction of material removal amounts from surface grinding of steam turbine blades. Full article

During the manual grinding of blades, the workers can estimate the material removal rate based on their experiences from observing the characteristics of the grinding sparks, leading to low grinding accuracy and low efficiency and affecting the processing quality of the blades. As an alternative to the recognition of spark images by the human eye, we used the deep learning algorithm YOLO5 to perform target detection on spark images and obtain spark image regions. First the spark images generated during one turbine blade-grinding process were collected, and some of the images were selected as training samples, with the remaining images used as test samples, which were labelled with LabelImg. Afterwards, the selected images were trained with YOLO5 to obtain an optimisation model. In the end, the trained optimisation model was used to predict the images of the test set. The proposed method was able to detect spark image regions quickly and accurately, with an average accuracy of 0.995. YOLO4 was also used to train and predict spark images, and the two methods were compared. Our findings show that YOLO5 is faster and more accurate than the YOLO4 target detection algorithm and can replace manual observation, laying a specific foundation for the automatic segmentation of spark images and the study of the relationship between the material removal rate and spark images at a later stage, which has some practical value. Full article

Titanium alloy materials are used in a variety of engineering applications in the aerospace, aircraft, electronics, and shipbuilding industries, and due to the continuous improvement of the contemporary age, surface integrity needs to be improved for engineering applications. Belt grinding parameters and levels directly affect the surface integrity of titanium alloys (TC4), which further affects the fatigue life of the titanium alloys during service. In order to investigate the surface integrity of titanium alloys at different roughness levels, the surfaces were repeatedly ground with the same type and different models of abrasive belts. The results showed that at roughness Ra levels of 0.4 μm to 0.2 μm, the compressive residual stresses decreased with increasing linear velocity and there were problems with large surface morphological defects. At the roughness Ra of 0.2 μm or less, grinding improves the surface morphology, the compressive residual stress increases with increasing feed rate, and the surface hardness decreases with increasing linear velocity. In addition, the research facilitates the engineering of grinding parameters and levels that affect surface integrity under different roughness conditions, providing a theoretical basis and practical reference. Full article

One of the most important properties of the surface of wood-based panels is their roughness. This property determines the way of working with the material in the processes of gluing and surface varnishing. The aim of this study was to determine the effect of various sanding belt configurations and the feeding speed of the conveyor belt during grinding on the surface roughness of high-density fiberboards (HDF). The research material was prepared under industrial conditions. Three types of boards were selected for the tests. After grinding, the roughness parameters were measured both transversely and longitudinally relative to the grinding direction, using a Mitutoyo SJ-210 profilometer and the optical method. Based on ANOVA analysis of the data, it was found that the type of HDF boards used and the configuration of the abrasive belts had a statistically significant impact on the roughness. The samples for which the grinding process was performed with sanding belts of the highest grain size had the lowest roughness. For the amplitude roughness parameters, the direction of roughness measurement had a significant influence. These results may provide valuable guidance for the furniture industry in the preparation of HDF for furniture production. Full article

Nowadays, the grinding process is mostly automatic, yet post-grinding quality inspection is mostly carried out manually. Although the conventional inspection technique may have cumbersome setup and tuning processes, the data-driven model, with its vision-based dataset, provides an opportunity to automate the inspection process. In this study, a convolutional neural network technique with transfer learning is proposed for three kinds of inspections based on 750–1000 surface raw images of the ground workpieces in each task: classifying the grit number of the abrasive belt that grinds the workpiece, estimating the surface roughness of the ground workpiece, and classifying the degree of wear of the abrasive belts. The results show that a deep convolutional neural network can recognize the texture on the abrasive surface images and that the classification model can achieve an accuracy of 0.9 or higher. In addition, the external coaxial white light was the most suitable light source among the three tested light sources: the external coaxial white light, the high-angle ring light, and the external coaxial red light. Finally, the model that classifies the degree of wear of the abrasive belts can also be utilized as the abrasive belt life estimator. Full article