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
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         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  (IF    AND