In this paper, the estimation of a screw's physical properties using a neural network (NN) technique is presented. The aim of this research is to study the effects of various control parameters of heat treatment and spheroidization on the physical properties of an alloy steel wire in its manufacturing process. The NN model is used to analyze the data collected by the image sensor and temperature sensor for heating treatments of alloy steel wire. It is expected that an advanced screw manufacturing system with intelligent analysis ability can be developed. Then, this smart system will be able to provide the optimal control parameters in real time to produce an alloy steel wire with ideal physical properties so that high-quality screws can be produced in the later manufacturing process. The results of this study show that the NN model can indeed achieve a fairly accurate estimation of the physical properties of a steel wire after the spheroidization, quenching, and tempering heat treatments. This shows that the development of an artificial-intelligence-based screw process optimization mechanism is very feasible.

This paper proposed a novel method to extract bilingual translation pairs from the web. Based on the observation that translation pairs tend to appear collectively on the web, a recursive process is used to extract high quality translation pairs from the web. First query the search engine with some seed data and crawl the returned pages. Then identify the Collective Translation Pair Block (CTPB) which contains the collective translation pairs using a heuristic evaluation method. After the CTPB has been identified, a PAT tree is employed to generate the extraction patterns automatically. Then a ranking SVM model is used to re-rank these patterns based on the F measure. The top 10 patterns are adopted to extract the translation pairs with the help of surface pattern. At last in order to get the high quality extraction translation, the extracted translation pairs are verified by a SVM classifier based on the translation relevant between the source and the target language.

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