Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4386
Title: Studies on tool wear classification and surface roughness prediction using machine learning appro
Researcher: Elangovan, M
Guide(s): Ramachandran, K I
Soman, K P
Keywords: Wear area
surface roughness prediction
Artificial Neural Network
Upload Date: 22-Aug-2012
University: Amrita Vishwa Vidyapeetham (University)
Completed Date: June, 2012
Abstract: Metal cutting plays an important role in the present day manufacturing. Over the years, the manufacturing industry has matured by introducing new materials and processes. Superior manufacturing facilities, with the state of the art technology processes are now available, catering to the stringent product requirements, like form, fit and function. They generate surface finishes that produce the right texture enhancing the product s aesthetic appeal or satisfying the designer s functional requirements. The product quality has been built into the product, with every aspect of the process studied, monitored and excelled. With the advent of computer technology and its allied growth in the software industry, newer computing techniques and algorithms push the technology to its limits and application engineers are curious to study the impact of these in various situations that may interest them. As manufacturing brings to life the various abstract designs, there exists a huge potential to create newer and newer products by various processes. This moots the study of the implication of such algorithms and techniques on these processes with a goal to manufacture better products in a shorter time, keeping the cost aspects low and complying with the quality requirements. This also opens up another related domain called condition monitoring . Condition monitoring studies are carried out on processes, machines, tools and the like. It is the periodic or continuous measurement of various parameters that indicate the condition of the tool, stability of the process or condition of the machine. The focus is to avoid producing parts that are out of tolerance or those which are in nonconformance with the specified finish and to avoid surprise breakdown of the machine itself. Some of the methods by which diagnosis is carried out include studying and analyzing the wear debris, sound and acoustic emission, and vibration signals. Signals are acquired and processed in time domain, frequency domain and time-frequency domain.
Pagination: 225p.
URI: http://hdl.handle.net/10603/4386
Appears in Departments:Department of Mechanical Engineering (Amrita School of Engineering)

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01_title.pdfAttached File130.05 kBAdobe PDFView/Open
02_certificate.pdf132.38 kBAdobe PDFView/Open
03_declaration.pdf133.11 kBAdobe PDFView/Open
04_dedication.pdf129.6 kBAdobe PDFView/Open
05_contents.pdf110.78 kBAdobe PDFView/Open
06_list of figures.pdf126.55 kBAdobe PDFView/Open
07_list of tables.pdf122.65 kBAdobe PDFView/Open
08_list of symbols and abbreviations.pdf124.89 kBAdobe PDFView/Open
09_abstract.pdf131.51 kBAdobe PDFView/Open
10_acknowledgements.pdf124.22 kBAdobe PDFView/Open
11_chapter 1.pdf2.27 MBAdobe PDFView/Open
12_chapter 2.pdf2.5 MBAdobe PDFView/Open
13_chapter 3.pdf2.4 MBAdobe PDFView/Open
14_chapter 4.pdf2.35 MBAdobe PDFView/Open
15_chapter 5.pdf2.67 MBAdobe PDFView/Open
16_chapter 6.pdf2.57 MBAdobe PDFView/Open
17_chapter 7.pdf2.39 MBAdobe PDFView/Open
18_chapter 8.pdf4.1 MBAdobe PDFView/Open
19_chapter 9.pdf2.27 MBAdobe PDFView/Open
20_references.pdf2.3 MBAdobe PDFView/Open
21_appendix.pdf1.45 MBAdobe PDFView/Open


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