Abstract
Experimental data in many domains serves as a basis for predicting useful trends. If the data and analysis are available over the Web this promotes E-Business by connecting clientele worldwide. This paper describes such a predictive tool "QuenchMiner™" in the domain "Materials Science". Data mining, more specifically the "Apriori Algorithm", is used to derive association rules that represent relationships between input conditions and results of domain experiments. This enables the tool to answer questions such as "Given cooling medium and agitation during material heat treatment, predict cooling rate". This allows users to perform case studies on the Web and use their results to optimize the involved processes, thus increasing customer satisfaction. Another interesting aspect is predicting material microstructure during heat treatment. Microstructure controls material properties such as hardness. Hence its prediction helps in making decisions about materials selection. Microstructure prediction has similarities to an artificial intelligence process called "Game-of-Life". Some challenges in our work are incorporating domain expert judgement while mining association rules, simulating microstructure evolution under different conditions, and dealing with uncertainty. These challenges and associated research issues are outlined here. To the best of our knowledge, this is the first tool performing Web-based predictive analysis in Materials Science.