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Clemson University TigerPrints All Theses Theses 12-2013 Knowledge Extraction from Work Instructions through Text Processing and Analysis Abhiram Koneru Clemson University, abhirak@g.clemson.edu Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Part of the Mechanical Engineering Commons Recommended Citation Koneru, Abhiram, "Knowledge Extraction from Work Instructions through Text Processing and Analysis" (2013).All Theses. 1769. https://tigerprints.clemson.edu/all_theses/1769 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact kokeefe@clemson.edu. KNOWLEDGE EXTRACTION FROM WORK INSTRUCTIONS THROUGH TEXT PROCESSING AND ANALYSIS A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Mechanical Engineering by Abhiram Koneru December 2013 Accepted by: Dr. Gregory M. Mocko, Committee Chair Dr. Lonny L. Thompson Dr. Mary E. Kurz ABSTRACT The objective of this thesis is to design, develop and implement an automated approach to support processing of historical assembly data to extract useful knowledge about assembly instructions and time studies to facilitate the development of decision support systems, for a large automotive original equipment manufacturer (OEM). At a conceptual level, this research establishes a framework for sustainable and scalable approach to extract knowledge from big data using techniques from Natural Language Processing (NLP) and Machine Learning (ML). Process sheets are text documents that contain detailed instructions to assemble a portion of the vehicle, specification of parts and tools to be used, and time study. To maintain consistency in the authorship process, assembly process sheets are required to be written in a standardized structure using controlled language. To realize this goal, 567 work instructions from 236 process sheets are parsed using Stanford parser using Natural Language Toolkit (NLTK) as a platform and a standard vocabulary consisting of 31 verbs is formed. Time study is the process of estimating assembly times from a predetermined motion time system, known as MTM, based on factors such as the activity performed by the associate, difficulty in assembling, parts and tools used, distance covered. The MTM compromises of a set of tables, constructed through statistical analysis and best-suited for batch production. These MTM tables are suggested based on the activity described in the work instruction text. The process of performing time studies for the process sheets is ii time consuming, labor intensive and error-prone. A set of (IFAND
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