Machine Learning Based Instantaneous Cutting Force Models for End Milling Operation (Record no. 14751)

MARC details
000 -LEADER
fixed length control field 02536nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621
Item number V197M
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Vaishnav, Shubham
245 ## - TITLE STATEMENT
Title Machine Learning Based Instantaneous Cutting Force Models for End Milling Operation
Statement of responsibility, etc by Shubham Vaishnav
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Mechanical Engineering
Year of publication 2019
300 ## - PHYSICAL DESCRIPTION
Number of Pages xvi,36p.
Other physical details HB
520 ## - SUMMARY, ETC.
Summary, etc Cutting force is a fundamental parameter determining productivity and quality ofthe end milling operation. The development of a generic cutting force model forend milling operation requires a large number of machining experimentsfollowed by determining empirical relationships between process parameterswith cutting force. This thesis presents a novel approach combining Mechanisticmodel and supervised ANN model to predict instantaneous cutting force variationduring the end milling operation. The approach proposes training of an ANNmodel using datasets generated from the Mechanistic force model instead ofusing experimental data. The proposed approach can generate a large number ofdatasets for the training of an ANN model without conducting rigorousexperimentation. The thesis work explores different ANN architecturesanddetermines an appropriate network by comparing a set of performanceparameters. A series of end milling experiments are conducted to examine theefficacy of the proposed approach in predicting cutting forces over a wide rangeof cutting conditions. Subsequently, the application of modern machine learningmodels such as Long Short Term Memory (LSTM) is explored to detect processparameters from the experimentally measured cutting force data. The studyuses a multi-layered LSTM to map measured cutting force data (input sequence)to a vector of end milling process parameters (fixed dimensionality labelledoutput vector). The dataset for the training of an LSTM model is generated usingthe Mechanistic force model. The testing of trained LSTM model is carried out toidentify process parameters from experimentally measured cutting force data. Ithas been observed that the proposed model can detect cutting conditions quiteeffectively for CNC end milling.KeywordsMachining; End milling; Cutting forces; Mechanistic Model; Machine Learning<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Instantaneous Cutting Force Models
Topical Term MTech Theses
Topical Term Department of Mechanical Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Desai, Kaushal A.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Full call number Accession Number Price effective from Koha item type
      Not For Loan Reference S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Course Reserve 2024-01-30 621 V197M TM00168 2024-01-30 Thesis