东方红105系列缸体裂纹分析与对策

分析了缸体铸件检视面及下缸筒裂纹产生的原因。通过消除铸件局部尖角,增加局部壁厚,消除砂芯毛刺,改善落砂、热处理、焊补条件,控制合金成分等措施解决了缸体裂纹问题。

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Research on automatic inspection system for defects on precise optical surface based on machine vision

In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

Posterior lingual lidocaine: A novel method to improve tolerance in upper gastrointestinal endoscopy

AIM: To evaluate the effect of posterior lingual lidocaine swab on patient tolerance to esophagogastro-duodenoscopy, the ease of performance of the procedure, and to determine if such use will reduce the need for intravenous sedation. METHODS: Eighty patients undergoing diagnostic esophagogastroduodenoscopy in a tertiary care medical center were randomized to either lidocaine swab or spray. Intravenous meperidine and midazolam were given as needed during the procedure. RESULTS: Patients in the lidocaine swab group (SWG) tolerated the procedure better than those in the spray group (SPG) with a median tolerability score of 2 (1, 4) compared to 4 (2, 5) (P < 0.01). The endoscopists encountered less diffi culty performing the proceduresin the SWG with lower median difficulty scores of 1 (1, 5) compared to 4 (1, 5) in the SPG (P < 0.01). In addition, the need for intravenous sedation was also lower in the SWG compared to the SPG with fewer patients requiring intravenous sedation (13/40 patients vs 38/40 patients, respectively, P < 0.01). The patients in the SWG were more satisfi ed with the mode of local anesthesia they received as compared to the SPG. In addition, the endoscopists were happier with the use of lidocaine swab. CONCLUSION: The use of a posterior lingual lidocaine swab in esophagogastroduodenoscopy improves patient comfort and tolerance and endoscopist satisfaction and decreases the need for intravenous sedation.

Realtime Vision-Based Surface Defect Inspection of Steel Balls

In the proposed system for online inspection of steel balls, a diffuse illumination is developed to enhance defect appearances and produce high quality images. To fully view the entire sphere, a novel unfolding method is put forward based on geometrical analysis, which only requires one-dimensional movement of the balls and a pair of cameras to capture images from different directions. Moreover, a realtime inspection algorithm is customized to improve both accuracy and efficiency. The precision and recall of the sample set were 87.7% and 98%, respectively. The average time cost on image processing and analysis for a steel ball was 47 ms, and the total time cost was less than 200 ms plus the cost of image acquisition and balls' movement. The system can sort 18 000 balls per hour with a spatial resolution higher than 0.01 mm.

Effect of Sample Unit Size on Visually Examining Pavement Condition for Asphalt-Surfaced Roads

Road surface condition evaluation involves the collection of data over pavement surface for different types of distresses. The exercise consumes a lot of resources if the whole road section length is surveyed and may be prone to errors as a result of surveyors＇ fatigue. It is therefore important to develop a representative sample to be used when evaluating road condition manually. This study aimed at determining an adequate sample size for section level as well as a way forward for network level condition evaluation of highways in Nepal. Again the study was conducted to quantify the effects of altering the sample unit size for performing a distress survey according to the PCI （pavement condition index） and SDI （surface distress index） method separately for asphalt surfaced roads. The effect of reducing/increasing sample unit size was investigated adopting visual examination through field survey by eight teams in July, 2015, along the section of Banepa-Bardibas highway. The PCI was then calculated for each sample unit using standard deduct curves and PCI calculation methodology as per SHRP （Strategic Highway Research Program） recommendations and the computation of SDI was done as per DoR （Department of Roads） guidelines. The results show that 13% sample unit are needed for SDI and 21% for PCI computation, however, the results are out of the significant level. This is higher than DoR and SHRP guidelines. Again no strong relationship is observed between SDI and PCI values.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.