Design of a defined grain distribution brazed diamond grinding wheel for ultrasonic assisted grinding and experimental verification.

Ultrasonic assisted grinding (UAG) is a promising manufacturing technology in processing hard and brittle materials, mostly due to its excellent machining performance. In UAG, improvements in grain trajectory interactions and overlapping stemming from kinematic characteristics were identified as the main reasons for reduced grinding forces and improved processing quality. However, in existing studies, the grinding wheels with disordered abrasive grain distributions and irregular grain protrusion heights were generally used. Consequently, it is difficult to control both the interactions between grain trajectories and overlapping. Aiming to solve this problem, a brazed diamond grinding wheel with defined grain distribution was proposed in this study. The grinding wheel matrix was designed using finite element analysis, while abrasive grain distribution was obtained by kinematic analysis of UAG. Finally, the grinding wheel was prepared using brazing technology and to verify its grinding performance, an electroplated grinding wheel with the identic matrix and abrasive grain size was prepared. Morphology analysis of both grinding wheels has shown that compared to the electroplated grinding wheel, the brazed one has both higher and more uniform grain protrusion height. In the next step, UAG and CG experiments were carried out using the brazed and electroplated grinding wheel. The effects of grain distribution and grinding parameters on the grinding force, force ratio, surface profile wave, and surface roughness were studied. The results have shown that in similar operating conditions the brazed grinding wheel produced smaller grinding force, force ratio, smoother ground surface, and lower surface roughness compared to the electroplated grinding wheel. Additionally, for both grinding wheels, the UAG reduced grinding force, force ratio, surface roughness, and profile wave height. However, it also caused a more extensive ultrasonic vibration effect on grinding compared to the electroplated grinding wheel; its reduction percentage in grinding force was larger, while surface roughness and average height difference for UAG were higher compared to CG.

Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.

BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE: To establish models for early prediction and intervention of HDP. METHODS: This study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks. RESULTS: The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and ⩾ 35 weeks, it reached more than 92%. CONCLUSION: Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.

A study of a fetal heart rate calculation system based on R-R interval.

BACKGROUND: Fetal electrocardiogram (FECG) can be obtained in a non-invasive manner to monitor fetal growth status. OBJECTIVE: In this study, a fetal heart rate (FHR) calculation system was proposed, which consists of the FECG recorder (MF-HOLTER) and the FECG monitoring software (FECG-MS). The abdomen electrocardiogram (AECG) of pregnant woman is acquired through the MF-HOLTER. The FECG-MS packs the AECG data and calls the FECG separation algorithm to obtain the separated FECG and the fetal QRS (FQRS) position. The FHR is further obtained by calculating the R-R interval value. At the same time, this study proposed a FQRS position correction algorithm to calculate the correct FHR values. METHOD: In order to verify the accuracy of the FHR calculation, the ECG signal of FLUKE's PS320 FETAL SIMULATOR and clinical data were simultaneously tested. RESULTS: The accuracy rate is over 98% in processing the simulator's data. In processing clinical data, the FHR values obtained by both the system proposed in this study and Monica AN24 are very close, and the difference is less than 1 bpm. CONCLUSION: The results show that the FHR calculation system is accurate and stable, and has a positive application value and prospect.

Influence of gestational age and time of day in baseline and heart rate variation of fetuses.

BACKGROUND: Fetal electrocardiography (FECG) places electrodes on the maternal abdomen to convert the fetal electrocardiosignals into fetal heart rate (FHR), improving the accuracy and comfort of pregnant woman. At the same time, FECG simplifies the procedure of long term monitoring in the perinatal period. OBJECTIVE: Investigating the influence of gestational age and time of day on FHR features to distinguish between non-stress test (NST) normal fetuses and NST suspicious fetuses. METHODS: A novel method of FHR baseline estimation was presented; then baseline value and fetal heart rate variation (FHRV) were analyzed in the time domain using FHR signals recorded from 52 fetuses. RESULTS: Baseline values in 1:00, 2:00, 4:00, 5:00 and heart rate variation (HRV) distribution showed a significant difference (p< 0.05) between NST normal fetuses and NST suspicious fetuses. CONCLUSIONS: The results suggest that NST normal and suspicious fetuses had same outcome and different FHR features. Accurately distinguishing normal fetuses and suspicious fetuses is important for lowering the false positive rate and reducing unnecessary clinical intervention.