Development Of A Computed Radiography-Based Weld Defect Detection And Classification System [RC78.7.D35 K75 2008 f rb].

Dalam penyelidikan ini, satu sistem bersepadu yang terdiri daripada satu peta kecacatan dan satu pengelas pelbagai rangkaian neural bagi peruasan, pengesanan dan pengesanan kecacatan kimpalan telah direkabentuk dan dibangun. In this research, an integrated system consisting of a flaw map and a multiple neural network classifier for weld defect segmentation, detection, and classification is designed and developed

Pengambilan Putusan Hukuman Pidana Pembunuhan dengan Case Based Reasoning

Penelitian ini bertujuan untuk pengambilan putusan hukuman pidana pembunuhan menggunakan metode Case Based Reasoning adalah sebagai aplikasi bantuan, yang dikhususkan bagi seorang Jaksa/Hakim dalam memilah-milah kasus tindak pidana pembunuhan, serta sebagai bahan referensi dalam memutuskan vonis pidana, yang tentunya lebih efektif dan efisien, dikarenakan data basis kasus dalam perangkat lunak bersumber dari kualifikasi delik pidana pembunuhan menurut KUHP. Reuse based digunakan sebagai model proses pengembangan aplikasi ini dengan siklus Case Based Reasoning. Hasil yang dibuat merupakan aplikasi yang menggabungkan Case Based Reasoning dengan jaringan syaraf tiruan perceptron. Perangkat lunak pengambilan putusan hukuman pidana pembunuhan menggunakan metode Case Based Reasoning dapat membantu praktisi hukum (Hakim/Jaksa) dalam menelusuri kasus tindak pidana pembunuhan, serta menjadikan kasus dalam aplikasi sebagai referensi dalam pemutusan vonis dalam dunia hukum yang real, dikarenakan solusi yang diberikan berdasarkan sumber yang mengatur hukum pidana di Indonesia (KUHP)

Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Numerical and Experimental Analysis of Magnetic Pulse Welding for Joining Similar and Dissimilar Materials

Magnetic pulse welding, a high speed joining process using electromagnetic forces, because of clean and multi-material operation has a wide range of possibilities for further development and application. Unlike conventional joining processes, the weld interface does not melt keeping the material properties intact without generation of hazardous emissions in form of heat, fume, and spatters. The present investigation deals with the feasibility study of the magnetic pulse welding technology for joining of similar and dissimilar materials through numerical modelling and simulation work followed by experimental validation of the obtained results. A finite element model was developed and validated with results available in literature. The model developed in this study helped predict accurate values of weld validation criteria for a wide range of process parameters and for different combinations of similar and dissimilar materials with varying geometry