Abstract
Knowledge of material properties, microstructure, underlying material composition, and manufacturing process parameters that the material has undergone is of significant interest to materials scientists and engineers. A large amount of information of this nature is available in publications in the form of experimental measurements, simulation results, etc. However, getting to the right information of this kind that is relevant for a given problem on hand is a non-trivial task. First, an engineer has to go through a large collection of documents to select the right ones. Then, the engineer has to scan through these selected documents to extract relevant pieces of information. Our goal is to help automate some of these steps. Traditional search engines are not of much help here, as they are keyword centric and weak on relation processing. In this paper, we present a domain-specific search engine that processes relations to significantly improve search accuracy. The engine preprocesses material publication repositories to extract entities such as material compositions, material properties, manufacturing processes, process parameters, and their values and builds an index using these entities and values. The engine then uses this index to process user queries to retrieve relevant publication fragments. It provides a domain-specific query language with relational and logical operators to compose complex queries. We have conducted an experiment on a small library of publications on steel on which searches such as “get the list of publications which have carbon composition between 0.2 and 0.3 and on which tempering is carried out for about 30 to 40 min” are performed. We compare the results of our search engine with the results of a keyword-based search engine.
Highlights
Materials, processes, and processstructure-property relationships and the ability to exploit this knowledge to systematically guide the design space exploration during product or material development are critical to the success of the Integrated Computational Materials Engineering (ICME) [1] approach
Our objective is to mine knowledge about material compositions, properties, processes, and process-structureproperty relations that are relevant to a given problem context
We present a system that is capable of supporting value-based queries of the kind discussed above to retrieve information from materials science publications
Summary
Materials, processes, and processstructure-property relationships and the ability to exploit this knowledge to systematically guide the design space exploration during product or material development are critical to the success of the Integrated Computational Materials Engineering (ICME) [1] approach. It will retrieve a publication even when the terms hardness and 50 Rc are unrelated but present in different parts of the document This results in retrieval of a lot of publications that do not match the problem context (precision error). Researchers have developed enhanced information retrieval (IR) techniques which, on top of keyword-based search, provide concept (such as protein, and genes)-based article categorization and search filtering, query refinement, and so on (for example, [2]). These techniques do not provide support for the kind of value-based retrieval required in materials engineering
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