Despite being the most promising third-generation semiconductor materials, the deformation and removal mechanisms of gallium nitride (GaN) single crystals involved in the ultra-precision machining process are not well revealed and few investigations on the grinding of GaN crystals have been reported, which hinders the development of high-efficiency and ultra-precision manufacturing of GaN components. Self-rotating grinding tests of GaN crystals were performed, and the results indicated that abrasive size had a significant influence on the surface morphology and roughness, in comparison with wheel rotational speed and feed speed. As the abrasive size decreased from 18 μm to 1.6 μm, the brittle fracture-dominated surface gradually changed to a full-plastic surface without brittle fractures and cracks. An ultra-smooth surface with a roughness of 1 nm in Sa was acquired using #8000 grinding wheels and a spark-out time of 10 min, which indicated that the machining technology of “grinding instead of polishing” of GaN crystals was achieved in this work. The plastic deformation mechanism of GaN crystals induced by ultra-precision machining was investigated using a cross-sectional TEM method and MD simulation, and both experimental and simulation results indicated that the plastic deformation involved in the scratching process was caused by the formation of polycrystalline nanocrystals, high-angle lattice misorientations, and close-to-atomic-scale defects, including stacking faults, dislocations and serious lattice distortions, along with a small amount of amorphous and phase transitions. There was an obvious delamination phenomenon in the plastic deformation zone. This research enhances the understanding of the deformation and damage mechanisms of GaN crystals involved in the ultra-precision machining process and is of significance for achieving the high-efficiency and high-accuracy manufacturing of GaN components. 

Phase transition and plastic deformation mechanisms induced by self-rotating grinding of GaN single crystals

Despite being the most promising third-generation semiconductor materials, the deformation and removal mechanisms of gallium nitride (GaN) single crystals involved in the ultra-precision machining process are not well revealed and few investigations on the grinding of GaN crystals have been reported, which hinders the development of high-efficiency and ultra-precision manufacturing of GaN components. Self-rotating grinding tests of GaN crystals were performed, and the results indicated that abrasive size had a significant influence on the surface morphology and roughness, in comparison with wheel rotational speed and feed speed. As the abrasive size decreased from 18 μm to 1.6 μm, the brittle fracture-dominated surface gradually changed to a full-plastic surface without brittle fractures and cracks. An ultra-smooth surface with a roughness of 1 nm in Sa was acquired using #8000 grinding wheels and a spark-out time of 10 min, which indicated that the machining technology of “grinding instead of polishing” of GaN crystals was achieved in this work. The plastic deformation mechanism of GaN crystals induced by ultra-precision machining was investigated using a cross-sectional TEM method and MD simulation, and both experimental and simulation results indicated that the plastic deformation involved in the scratching process was caused by the formation of polycrystalline nanocrystals, high-angle lattice misorientations, and close-to-atomic-scale defects, including stacking faults, dislocations and serious lattice distortions, along with a small amount of amorphous and phase transitions. There was an obvious delamination phenomenon in the plastic deformation zone. This research enhances the understanding of the deformation and damage mechanisms of GaN crystals involved in the ultra-precision machining process and is of significance for achieving the high-efficiency and high-accuracy manufacturing of GaN components.

Experimental evaluation of surface generation and force time-varying characteristics of curvilinear grooved micro end mills fabricated by EDM

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Ground surface roughness is regarded as one of the most crucial indicators of machining quality and is hard to be predicted due to the random distribution of abrasive grits and sophisticated grinding mechanism. In order to estimate surface roughness accurately in grinding process and provide feasible monitoring scheme for practical manufacturing application, a novel prediction system of surface roughness is presented in this article, including the processing of grinding signals, selection of feature combination, and development of prediction model. Grinding force, vibration, and acoustic emission signals are collected during the grinding of C-250 maraging steel. Numerous features in time domain and frequency domain are extracted from original and decomposed signals. A hybrid feature selection approach is proposed to select features based on their relevance to surface roughness as well as hardware and time costs. A sequential deep learning framework, long short-term memory (LSTM) network, is employed to predict ground surface roughness. The results have shown that the LSTM model achieves excellent prediction performance with a feature combination of grinding force and acoustic emission. After considering the hardware and time costs, features in acceleration signal replace those in grinding force and acoustic emission signals with slight loss of prediction performance and significant reduction of costs, which proves the practicability and feasibility of proposed prediction system.

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

Grinding burn is a typical quality defect that negatively affects microstructures and mechanical properties of parts. In this article, an intelligent grinding burn detection system that consists of processing of grinding signals, extraction of signal features, selection of optimal feature subset, and classification of grinding burn is presented. During the grinding of maraging steel 3J33, physical signals are collected by a dynamometer, accelerometer, and acoustic emission sensor. A large amount of preliminary features in time domain and frequency domain are extracted after wavelet packet decomposition and ensemble empirical mode decomposition. To reduce the dimensionality of feature space and increase relevancy of feature to grinding burn, a two-stage feature selection strategy combining filter and wrapper method is proposed. The filter ranks individual features by ReliefF, while the wrapper evaluates different feature subsets according to the model performance. A deep learning network, stacked sparse autoencoder (SSAE), is adopted to develop the classification model of grinding burn and is compared with other machine learning models. The results demonstrate that the two-stage feature selection method is able to provide the optimal feature subset for the model and SSAE model obtains the best training and testing classification rate of grinding burn with least features among all comparative models.

An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder

Grinding wheel condition is considered as the key factor affecting grinding performance, and therefore, accurate monitoring of wheel wear is necessary to prevent the deterioration of part quality. ...

An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network:

Thermal effect on oxidation layer evolution and phase transformation in grinding of Fe-Ni super alloy

In this study, a novel analytical model considering practical oil pressure and a cantilever plate bending calculation method is proposed to calculate the deformation of a vertical hydrostatic guide...

Qinzhi Zhou

Papers

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder

An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network:

Thermal effect on oxidation layer evolution and phase transformation in grinding of Fe-Ni super alloy

Deformation Analysis of Hydrostatic Guideways Based on the Cantilever Plate Bending Calculation Method