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Compared with the parameters of surface grinding, those of the curved surface abrasive belt grinding are more diverse, and the material removal mechanism is more complicated. This makes the selection of the parameters of the curved surface grinding process extremely difficult. This study investigates the effects of different parameters on the grinding performance of convex surface workpieces. The material removal (material removal efficiency and microchips), grinding heat (overall grinding temperature and single abrasive grain temperature), and grinding surface quality (surface roughness and removal profile) were analyzed in detail at the macro- and microscales. The effects of the grinding parameters on the above three performance indices were analyzed and discussed. The results showed that an increase in the theoretical grinding depth results in a higher material removal efficiency and grinding temperature as well as a superior ground surface quality. The effects of belt speed on the material removal efficiency and grinding temperature are less significant than those of the theoretical grinding depth. Comprehensive consideration of three indicators of grinding performance, when the theoretical grinding depth is 0.16 mm and the belt speed is 26–28 m/s, the abrasive belt grinding performance is relatively superior. Therefore, by comprehensively analyzing the grinding performance, more suitable grinding parameters can be selected to improve the grinding quality of curved workpieces. [ABSTRACT FROM AUTHOR]

Double-plane grinding plays an important role in the processing of high-precision flat parts. A double-side autonomous grinding (DSAG) method was proposed and investigated to improve the uniformity of the abrasive grains of the grinding disc on the surface of the workpiece by releasing the constraints on the workpiece. In DSAG, the main driving force of workpiece movement comes from the friction force generated by the relative movement of the upper and lower grinding pads. Experiment of dynamic friction coefficient measurement between workpiece and grinding plates is carried out to take into account the differences of dynamic friction factor under different speed and force conditions. A mathematical model of the relative motion trajectory between the particles on the upper and lower plate and the workpiece is established based on kinematics and dynamics theories. Then, the effects of the linkage mode of the lower plate, grinding pressure, and speed ratio of the upper and lower plates on the frequency and direction uniformity of the grinding trajectory of the abrasive particles on the workpiece surface are simulated and analyzed. Aiming at the trajectory uniformity, the grinding parameters are optimized through orthogonal experiment and verified on the independently developed experimental platform. The flatness and roughness of the workpiece are detected. The results show that the DSAG performs well in obtaining the better roughness and flatness of the workpiece surface than that of planetary grinding. [ABSTRACT FROM AUTHOR]

The surface temperature of a workpiece during abrasive belt grinding directly affects its surface quality and determines whether the workpiece surface burns. However, the surface temperature of the workpiece during the grinding process is determined by the total heat and the heat carried away by the chips. Under certain grinding parameters, accurately obtaining the heat removed by the chips is one of the key factors to accurately obtain the workpiece surface quality. Therefore, a new method for chip heat monitoring based on audio-visual information decision fusion is proposed in this study. By obtaining visual sparks and sound signals directly related to the chip, a real-time chip quality (i.e., material removal rate, MRR) monitoring model based on convolutional neural network (CNN) and multilayer perceptron (MLP) was established. The heat taken away by the chips in real time was calculated through theoretical formula. The results show that the RMSE of the proposed method for the material removal rate does not exceed 0.006, and the coefficient of determination R2 is not less than 99.4%. The maximum error between the predicted maximum temperature of the grinding surface and the experimental measurement value is 10 °C. The proposed method provides the theoretical basis for the prediction and control of workpiece grinding temperature. [ABSTRACT FROM AUTHOR]

The wear state of an abrasive belt is one of the important factors affecting the grinding precision of belt grinding processes. At present, there are two problems associated with the monitoring method of the wear condition of abrasive belts: (1) there are no uniform wear criteria to classify the wear condition of abrasive belts, and the segmentation threshold of the wear condition is affected by the change in the grinding parameters; and (2) an abrasive belt wear model based on indirect sensor monitoring of signals is affected by the change in the grinding parameters of abrasive belts; therefore, it is only suitable for abrasive belt wear monitoring under specific grinding parameters. This paper introduces a method of belt wear state monitoring based on machine vision and image processing. Surface images of an abrasive belt during its entire life are captured using a noncontact electron microscope. Three image features related to the wear state are selected: first-order distance of color component R, entropy of the horizontal subgraph, and vertical subgraph of the texture feature. Moreover, the wear state is classified into three categories based on the selected features. Using the selected features and the random forest classification algorithm, an abrasive belt wear state classifier is established. The performance of the classifier is verified and evaluated using a data subset of different images. The results show that the proposed method has high recognition accuracy for belt wear state, which can reach 99% in the accelerated wear stage. The proposed method solves the problem of the dependence and sensitivity of the monitoring model on the variation in the grinding parameters in the process of abrasive belt wear monitoring, and it improves the adaptability and versatility of the monitoring method. [ABSTRACT FROM AUTHOR]

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson's coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online. [ABSTRACT FROM AUTHOR]

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization. [ABSTRACT FROM AUTHOR]

Wear is an inevitable problem in abrasive belt grinding, and the material removal rate decreases with continuous wear of the abrasive belt. This indicates that the grinding control force is affected by two dynamic factors, namely the actual material removal and abrasive belt wear state. To obtain an accurate force-control model to achieve uniform material removal, a new method for online monitoring of abrasive belt material removal rates and their corresponding wear statuses is proposed herein using only the grinding sound signals. By performing material removal rate and abrasive belt wear experiments, the grinding sound signals during processing are obtained. The wear states of the abrasive belt are quantified using the newly defined gray-mean values of the topographical images of the belt into different levels. The grinding sound signals are quantitatively described via the statistical features of their sound wavelet signals. The statistical features related to material removal rates or belt wear states are selected on the basis of the Pearson correlation coefficients. The prediction models for material removal rate and wear levels of the abrasive based on the selected features are then established using the LightGBM learning algorithm. Experimental datasets are used to train and validate the established model. The test results show that the evaluation parameters of the prediction model of the material removal rate are all within 5%. Further, the accuracy of the wear levels of the abrasive belt can exceed 91%. Compared with other prediction models, the new LightGBM models exhibit superiority in terms of time factor without loss of accuracy of the model. It is thus proved that the proposed method can provide a good basis for monitoring the material removal rate and belt wear in the abrasive belt grinding process. [ABSTRACT FROM AUTHOR]

Sphere-pipe joints welding is widely used in industrial applications. This paper presents a robot welding approach for the sphere-pipe joints with swing and multi-layer planning. Firstly, various coordinate systems are used to describe the geometric relationship between weld seam and robot welding torch. The sphere-pipe intersecting curve welding process is basically uphill and downhill welding. Therefore, this paper establishes a description model of the welding torch attitude, which parameterizes the attitude description and automatically adjusts the torch attitude during the welding process according to the change of weld inclination angle. To overcome the negative effects of gravity, such as deepening of the molten pool and reduction of the weld width, this paper integrates the swing welding technology into trajectory planning and gives a solving algorithm for the welding torch swing curve. The swing welding also can reduce the number of weld pass. Therefore, multi-layer single-pass swing welding is an economical and efficient way for thicker weldments. In this paper, a multi-layer single-pass swing welding planning algorithm is proposed, which can automatically determine the height and swing amplitude of each welding layer. Finally, the industrial robot Puma560 is used to carry out experimental simulation, and the simulation results are used to verify the feasibility and accuracy of this approach. [ABSTRACT FROM AUTHOR]

Belt grinding is a material processing operation which is capable of producing parts to high dimensional accuracy, excellent finish surface, and surface integrity. Unlike turning or other metal cutting operations that use geometrically well-defined tools, belt grinding involves tool geometry and cutting actions that are not well defined. Therefore, it is quite difficult to obtain a comprehensive theoretical model to predict the grinding depth. As well known, the strain rate is very high in grinding with abrasive tools, which results in substantial heat produced in the shear zone. Sparks are produced when the hot chips thrown out during the process, get oxidized, and burn in the atmosphere. Spark is an inherent feature for most dry grinding process. An approach on material removal rate monitoring in belt grinding by spark field measurement is proposed. The size of the spark field is a visualized reflection of the number of chips instantaneous produced in grinding. Features of spark field related to the material removal rate are analyzed and quantified. With this method, the coupling between the grinding parameters is no need to be considered. Experimental results indicate that the changing of grinding parameters has a different impact on the feature values of the spark field. Feature values of the spark field, such as area, boundary, and density, show a tight correlation with the material removal rate. The resolution accuracy of spark features on the grinding depth is studied. The correct rate of the grinding depth identification can reach more than 95% for the area, and density features of the spark field with the resolution range greater than 10 μm. The analysis indicates that the proposed method is effective and easy-to-accomplish for material removal rate monitoring in belt grinding. [ABSTRACT FROM AUTHOR]

2Line 3-4 in paragraph 2 in page 2722:"The material removal rates are 0.036 and 0.077 g/(mm/s), respectively""The material removal rates are 0.036 and 0.077 g/(mm-min), respectively"3Line 2-4 in paragraph 3 in page 2722: "And their material removal rates are 0.079, 0.11, and 0.15 g/(mm/s), respectively.""And their material removal rates are 0.079, 0.11, and 0.15 g/(mm-min), respectively." 4Line 5-7 in paragraph 2 in page 2723: "The material removal rates are 0.05, 0.082, 0.16, and 0.22 g/(mm/s), respectively""The material removal rates are 0.05, 0.082, 0.16, and 0.22 g/(mm-min), respectively"5Line 8-9 in paragraph 3 in page 2723: "Their material removal rates are 0.33, 0.21, and 0.05 g/(mm/s), respectively.""Their material removal rates are 0.33, 0.21, and 0.05 g/(mm-min), respectively.". [Extracted from the article]