{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:43:06Z","timestamp":1767706986797},"reference-count":31,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2009,2,27]],"date-time":"2009-02-27T00:00:00Z","timestamp":1235692800000},"content-version":"unspecified","delay-in-days":5536,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[1994]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.<\/jats:p><jats:p>The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.<\/jats:p>","DOI":"10.1017\/s0890060400000469","type":"journal-article","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T13:47:41Z","timestamp":1270043261000},"page":"63-75","source":"Crossref","is-referenced-by-count":5,"title":["Use of decision-tree induction for process optimization and knowledge refinement of an industrial process"],"prefix":"10.1017","volume":"8","author":[{"given":"A.","family":"Famili","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2009,2,27]]},"reference":[{"key":"S0890060400000469_ref030","unstructured":"Wilkins D.C. (1990). Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory, Report No. UIUCDS-R-90\u20131585, University of Illinois, Urbana, Illinois."},{"key":"S0890060400000469_ref029","first-page":"646","volume-title":"Seventh Nat. Conf. Artificial Intelligence, Vol. I","author":"Wilkins","year":"1988"},{"key":"S0890060400000469_ref027","unstructured":"Turney P. , & Famili A. (1992). Analysis of induced decision trees for industrial process optimization. In Proc. 6th Int. Conf. Systems Research, Informatics and Cybernetics, pp. 19\u201324."},{"key":"S0890060400000469_ref025","doi-asserted-by":"publisher","DOI":"10.1016\/0921-8890(91)90016-E"},{"key":"S0890060400000469_ref023","unstructured":"Risko D.G. (1989). Electrochemical Machining, SME Technical Paper EE-89\u2013820, Society of Manufacturing Engineers, Dearborn, MI."},{"key":"S0890060400000469_ref019","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"S0890060400000469_ref017","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(84)90024-9"},{"key":"S0890060400000469_ref020","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(87)80053-6"},{"key":"S0890060400000469_ref011","unstructured":"Garrett P. , William Lee C. , & LeClair S.R. (1987). Qualitative process automation vs. quantitative process control. In Proc. Ame. Control Conf, 1368\u20131373."},{"key":"S0890060400000469_ref010","doi-asserted-by":"publisher","DOI":"10.1007\/BF00117745"},{"key":"S0890060400000469_ref009","unstructured":"Holder L.B. (1990). Application of machine learning to the maintenance of knowledge base performance. In Proc. Third Int. Conf. Industrial and Engineering Applications of AI and Expert Systems, 1005\u20131012."},{"key":"S0890060400000469_ref008","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(88)90012-4"},{"key":"S0890060400000469_ref007","volume-title":"Fundamentals of Electrochemical Machining","author":"Faust","year":"1971"},{"key":"S0890060400000469_ref006","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060400002602"},{"key":"S0890060400000469_ref004","volume-title":"Electrochemical Milling","author":"De Barr","year":"1968"},{"key":"S0890060400000469_ref002","unstructured":"Cheng J. , (1990). Expert system and process optimization techniques for real-time monitoring and control of plasma processes. In Proc. SPIE, Vol. 1392, 373\u2013384."},{"key":"S0890060400000469_ref012","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.1988.194373"},{"key":"S0890060400000469_ref028","unstructured":"Widmer G. , Horn W. , & Nagele B. (1992). Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology, Report No. TR-92\u201316, Austrian Research Institute for Artificial Intelligence."},{"key":"S0890060400000469_ref003","unstructured":"Craw S. , & Sleeman D. (1990). Automating the refinement of knowledge based systems. In Proc. Ninth Europ. AI Conf., 167\u2013172."},{"key":"S0890060400000469_ref016","volume-title":"Current Developments in Knowledge Acquisition-EKAW","author":"Nedelec","year":"1992"},{"key":"S0890060400000469_ref013","first-page":"3","volume-title":"In Machine Learning: An Artificial Intelligence Approach, Volume II","author":"Michalski","year":"1986"},{"key":"S0890060400000469_ref001","doi-asserted-by":"publisher","DOI":"10.1145\/122344.122348"},{"key":"S0890060400000469_ref024","first-page":"38","article-title":"Where\u2019s the AI?","volume":"12","author":"Schank","year":"1991","journal-title":"AI Magazine"},{"key":"S0890060400000469_ref014","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(82)90040-6"},{"key":"S0890060400000469_ref026","volume-title":"The Knowledge Frontier (Volume 1: Fundamentals), Course notes distributed by the author","author":"Sriram","year":"1990"},{"key":"S0890060400000469_ref031","doi-asserted-by":"publisher","DOI":"10.1016\/0957-4174(92)90064-Y"},{"key":"S0890060400000469_ref015","first-page":"1395","volume-title":"Proc. Eleventh Int. Joint Conf. Artificial Intelligence","author":"Mittal","year":"1989"},{"key":"S0890060400000469_ref005","volume-title":"Applied Regression Analysis","author":"Draper","year":"1966"},{"key":"S0890060400000469_ref018","first-page":"463","volume-title":"Machine Learning: An Artificial Intelligence Approach","author":"Quinlan","year":"1983"},{"key":"S0890060400000469_ref022","volume-title":"C4.5: Programs for Machine Learning","author":"Quinlan","year":"1993"},{"key":"S0890060400000469_ref021","first-page":"304","volume-title":"Proc. Tenth Int. Joint Conf. Artificial Intelligence","author":"Quinlan","year":"1987"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060400000469","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T19:41:29Z","timestamp":1557949289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060400000469\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1994]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[1994]]}},"alternative-id":["S0890060400000469"],"URL":"https:\/\/doi.org\/10.1017\/s0890060400000469","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[1994]]}}}