Weld bead geometry inspection by non-destructive testing techniques is the major challenge of today’s welding industries. As compared to similar materials, application of dissimilar materials demanded all over. Generally, feature extraction by radiographic images sensed geometric features and categories for classification of weld imperfections. Whereas diverse understanding for human intelligence grasp beneficial evidence from non-geometric features of images. To overcome this difficulty by exploring features and recognize imperfections of two-dimensional digital image wherever geometric features state presence of weld. Mostly, classification accuracy significantly influences by weld imperfection region segmentation and imperfection texture feature extraction. The proposed techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features. At last, support vector machine and k-nearest neighbours compared to discover finest classifier and uppermost accuracy of 96% attained through grouping of local binary pattern features and support vector machine.

Weld Imperfection Classification by Texture Features Extraction and Local Binary Pattern

Computation of optimum parameters to achieve the intrusion of mandibular central incisor and mandibular canine for deep bite treatment

Estimation of the maximum permissible intrusive force for intrusion of a canine tooth: one-dimensional finite element study

Online weld examination by non-destructive testing significantly demanded specially for aerospace, petrochemical, shipbuilding and nuclear power industries. Mostly, X-ray testing accepted by accuracy and consistency in weld bead examinations and approving part quality. In radiography, the texture feature extraction by grey level co-occurrence matrix plays key role for surface texture examination. This works projected technique for detection and cataloguing of imperfections in weld joint. This technique identify detects and differentiates weld images that look like to improper signs or deficiencies such as crack, slag, incomplete fusion, incomplete penetration, porosity, gas cavity and undercut. A group of four descriptors matching to texture measurements extracted segmented entity and specified input to classifiers. Then, classifier trained to classify entity from one of the defects classes. At last, support vector machine and artificial neural network classifiers confirmed accuracy performance of 92 and 87% by confusion matrix.

Multi-class Weld Defect Detection and Classification by Support Vector Machine and Artificial Neural Network

In the current research study, a computer application (desktop software) was developed to determine orthodontic force application points to achieve en-masse retraction of six maxillary anterior teeth. In this type of tooth movement, retraction forces are applied with retraction spring by fixing it anteriorly and posteriorly. Based on other orthodontic force, moment and distance parameters, proper location of retraction spring attachment on anterior and posterior side is needed to ensure accurate application of orthodontic appliances. In this research study, the computer application (desktop software) based on simple trigonometric calculation was developed for the ease of estimation of location of required retraction force. This computer application can be used both in labial orthodontics (LaO) and lingual orthodontics (LiO) if other force, moment, and distance parameters are known. Due to simple user interface of computer application, the task of determination of force application points will be simplified for orthodontists which is desirable.

Abhishek M. Thote

Papers

Multi-class Weld Defect Detection and Classification by Support Vector Machine and Artificial Neural Network

Estimation of Orthodontic Force Parameters with Developed Computer Application for En-Masse Retraction of Six Maxillary Anterior Teeth

Computation of optimum parameters to achieve the intrusion of mandibular central incisor and mandibular canine for deep bite treatment

Estimation of the maximum permissible intrusive force for intrusion of a canine tooth: one-dimensional finite element study