• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 추계학술대회 논문집
• /
• pp.888-891
• /
• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1997년도 춘계학술대회 논문집
• /
• pp.982-986
• /
• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• Journal of Korean Neurosurgical Society
• /
• 제57권2호
• /
• pp.131-134
• /
• 2015

Traumatic diaphragm hernia can occur in rare cases and generally accompanies thoracic or abdominal injuries. When suffering from ankylosing spondylitis, a small force can develop into vertebral fracture and an adjacent structural injury, and lead to diaphragm hernia without accompanying concomitant thoracoabdominal injury. A high level of suspicion may be a most reliable diagnostic tool in the detection of a diaphragm injury, and we need to keep in mind a possibility in a patient with ankylosing spondylitis and a thoracolumbar fracture, even in the case of minor trauma.

• Composites Research
• /
• 제27권4호
• /
• pp.152-157
• /
• 2014

Non-destructive testing methods of composite materials are very important for improving material reliability and safety. AE measurement is based on the detection of microscopic surface movements from stress waves in a material during the fracture process. The examination of AE is a useful tool for the sensitive detection and location of active damage in polymer and composite materials. FBG (Fiber Bragg Grating) sensors have attracted much interest owing to the important advantages of optical fiber sensing. Compared to conventional electronic sensors, fiber-optical sensors are known for their high resolution and high accuracy. Furthermore, they offer important advantages such as immunity to electromagnetic interference, and electrically passive operation. In this paper, the crack detection capability of AE (Acoustic Emission) measurement was compared with that of an FBG sensor under tensile testing and buckling test of composite materials. The AE signals of the PVDF sensor were measured and an AE signal analyzer, which had a low pass filter and a resonance filter, was designed and fabricated. Also, the wavelength variation of the FBG sensor was measured and its strain was calculated. Calculated strains were compared with those determined by finite element analysis.

• 지구물리와물리탐사
• /
• 제7권1호
• /
• pp.45-50
• /
• 2004

This paper describes a method of detecting formation boundaries, and permeable fractures, from frequency-domain Stoneley wave logs. Field data sets were collected between the depths of 330 and 360 m in well EE-4 in the Higashi-Hachimantai geothermal field, using a monopole acoustic logging tool with a source central frequency of 15 kHz. Stoneley wave amplitude spectra were calculated by performing a fast Fourier transform on the waveforms, and the spectra were then collected into a frequency-depth distribution of Stoneley wave amplitudes. The frequency-domain Stoneley wave log shows four main characteristic peaks at frequencies 6.5, 8.8, 12, and 13.3 kHz. The magnitudes of the Stoneley wave at these four frequencies are affected by formation properties. The Stoneley wave at higher frequencies (12 and 13.3 kHz) has higher amplitudes in hard formations than in soft formations, while the wave at lower frequencies (6.5 and 8.8 kHz) has higher amplitudes in soft formations than in hard formations. The correlation of the frequency-domain Stoneley wave log with the logs of lithology, degree of welding, and P-wave velocity is excellent, with all of them showing similar discontinuities at the depths of formation boundaries. It is obvious from these facts that the frequency-domain Stoneley wave log provides useful clues for detecting formation boundaries. The frequency-domain Stoneley wave logs are also applicable to the detection of a single permeable fracture. The procedure uses the Stoneley wave spectral amplitude logs at the four frequencies, and weighting functions. The optimally weighted sum of the four Stoneley wave spectral amplitudes becomes almost constant at all depths, except at the depth of a permeable fracture. The assumptions that underlie this procedure are that the energy of the Stoneley wave is conserved in continuous media, but that attenuation of the Stoneley wave may occur at a permeable fracture. This attenuation may take place at anyone of the four characteristic Stoneley wave frequencies. We think our multispectral approach is the only reliable method for the detection of permeable fractures.

• Imaging Science in Dentistry
• /
• 제44권2호
• /
• pp.89-94
• /
• 2014

Purpose: Vertical root fracture (VRF) is a common complication in endodontically treated teeth. Considering the poor prognosis of VRF, a reliable and valid detection method is necessary. Cone beam computed tomography (CBCT) has been reported to be a reliable tool for the detection of VRF; however, the presence of metallic intracanal posts can decrease the diagnostic values of CBCT systems. This study evaluated and compared the effects of intracanal stainless steel or titanium posts on the sensitivity, specificity, and accuracy of VRF detection using a NewTom VG CBCT system. Materials and Methods: Eighty extracted single-rooted teeth were selected and sectioned at the cemento-enamel junction. The roots were divided into two groups of 40. Root fracture was induced in the test group by using an Instron machine, while the control group was kept intact. Roots were randomly embedded in acrylic blocks and radiographed with the NewTom VG, both with titanium and stainless steel posts and also without posts. Sensitivity, specificity, and accuracy values were calculated as compared to the gold standard. Results: The sensitivity, specificity, and accuracy of VRF diagnosis were significantly lower in teeth with stainless steel and titanium posts than in those without posts. Interobserver agreement was the highest in teeth without posts, followed by stainless steel posts, and then titanium posts. Conclusion: Intracanal posts significantly decreased the VRF diagnostic values of CBCT. The stainless steel posts decreased the diagnostic values more than the titanium posts.

• 한국생산제조학회지
• /
• 제19권3호
• /
• pp.326-333
• /
• 2010

In this study, DAQ and TRA modules were applied to the CFRP single specimen testing method using AE. A method for crack identification in CFRP specimens based on k-mean clustering and wavelet transform analysis are presented. Mode I on DCB under vertical loading and mode II on 3-points ENF testing under share loading have been carried out, thereafter k-mean method for clustering AE data and wavelet transition method per amplitude have been applied to investigate characteristics of interfacial fracture in CFRP composite. It was found that the fracture mechanism of Carbon/Epoxy Composite to estimate of different type of fractures such as matrix(epoxy resin) cracking, delamination and fiber breakage same as AE amplitude distribution using a AE frequency analysis. In conclusion, the presented results provide a foundation for using wavelet analysis as efficient crack detection tool. The advantage of using wavelet analysis is that local features in a displacement response signal can be identified with a desired resolution, provided that the response signal to be analyzed picks up the perturbations caused by the presence of the crack.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1996년도 춘계학술대회 논문집
• /
• pp.119-123
• /
• 1996

The demands for robotic and automatic system are continually increasing in manufacturing fields. There were so many studies to monitor and predict system, but it were mainly relied upon measuring of cutting force, current of motor spindle and using acoustic sensor, etc. In this study digital image of time series sequence was acquired taking advantage of optical technique. Then, mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR model was selected for system model, fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, we found there was a system stability.

• Imaging Science in Dentistry
• /
• 제51권1호
• /
• pp.55-62
• /
• 2021

Purpose: This study evaluated the influence of a metal artifact reduction (MAR) tool in a cone-beam computed tomography (CBCT) device on the diagnosis of vertical root fractures (VRFs) in teeth with different root filling materials. Materials and Methods: Forty-five extracted human premolars were classified into three subgroups; 1) no filling; 2) gutta-percha; and 3) metallic post. CBCT images were acquired using an Orthopantomograph 300 unit with and without a MAR tool. Subsequently, the same teeth were fractured, and new CBCT scans were obtained with and without MAR. Two oral radiologists evaluated the images regarding the presence or absence of VRF. Receiver operating characteristic (ROC) curves and diagnostic tests were performed. Results: The overall area under the curve values were 0.695 for CBCT with MAR and 0.789 for CBCT without MAR. The MAR tool negatively influenced the overall diagnosis of VRFs in all tested subgroups, with lower accuracy (0.45-0.72), sensitivity (0.6-0.67), and specificity (0.23-0.8) than were found for the images without MAR. In the latter group, the accuracy, sensitivity, and specificity values were 0.68-0.77, 0.67-083, and 0.53-087, respectively. However, no significant difference was found between images with and without MAR for the no filling and gutta-percha subgroups (P>0.05). In the metallic post subgroup, CBCT showed a significant difference according to MAR use (P<0.05). Conclusion: The OP 300 MAR tool negatively influenced the detection of VRFs in teeth with no root canal filling, gutta-percha, or metallic posts. Teeth with metallic posts suffered the most from the negative impact of MAR.

• Journal of Korean Neurosurgical Society
• /
• 제66권1호
• /
• pp.53-62
• /
• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.