Stage identification and process optimization for fast drilling EDM of film cooling holes using KBSI method

Fast drilling electrical discharge machining(EDM)is widely used in the manufacture of film cooling holes of turbine blades.However,due to the various hole orientations and severe electrode wear,it is relatively intricate to accurately and timely identify the critical moments such as breakout,hole completion in the drilling process,and adjust the machining strategy properly.Existing breakout detection and hole completion determination methods are not suitable for the high-efficiency and fully automatic production of film cooling holes,for they almost all depend on preset thresholds or training data and become less appropriate when machining condition changes.As the breakout and hole completion detection problems can be abstracted to an online stage identification problem,in this paper,a kurtosis-based stage identification(KBSI)method,which uses a novel normalized kurtosis to denote the recent changing trends of gap voltage signals,is developed for online stage identification.The identification accuracy and generalization ability of the KBSI method have been verified in various machining conditions.To improve the overall machining efficiency,the influence of servo control parameters on machining efficiency of each machining stage was analyzed experimentally,and a new stage-wise adaptive control strategy was then proposed to dynamically adjust the servo control parameters according to the online identification results.The performance of the new strategy is evaluated by drilling film cooling holes at different hole orientations.Experimental results show that with the new control strategy,machining efficiency and the machining quality can be significantly improved.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.

An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN

Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSRS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(Faster R-CNN).The model incorporates the following optimization strategies: first, Inception_Res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(RPN);third, region of interest(Ro I) alignment replaces Ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original Faster R-CNN and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original Faster R-CNN model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.