Anthony Xavior M

Department of Manufacturing Engineering

School of Mechanical Engineering

Vellore Campus

Margret Anouncia S

Department of Software Systems

School of Computer Science and Engineering

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Services and Operations Management

In this research paper, hard machining of two materials viz. AISI 52100 (bearing steel) and AISI D2 (tool steel) at a hardness of 55 HRC is addressed. Taguchi's technique is used for the design of experiments. Eight different parameters are considered for the experimentation in order to perform comprehensive investigations on hard machining process. Case-based reasoning (CBR) model is developed for predicting the machining performance and its capability is evaluated by conducting validation experiments. The root mean squared error, mean absolute percentage error, and the correlation coefficient between the actual and the model-predicted values of surface roughness and tool life are evaluated to confirm the validity of the CBR model. Â© 2012 Springer-Verlag London Limited.

The International Journal of Advanced Manufacturing Technology

Case-based reasoning (CBR) model for hard machining process

The performance of the machining (turning) process is evaluated in terms of tool life, surface roughness, tool-shim interface temperature developed and metal removal rate during the process. It is very important for the manufacturing engineers to know the performance of the turning process for a set of cutting (input) parameters. In this paper, parametric models based on multiple regression analysis (MRA), neural networks (NNs) and case-based reasoning (CBR) are developed for predicting the machining performance, i.e. the output parameters. An experimental database containing 114 data sets are used for developing the three models. Each data set contains nine input and four output parameters. About 20 machining trials are exclusively conducted with various combinations of input parameters, and their corresponding output values are compared with the predicted values of the developed models. Descriptive statistics of the errors are calculated for the three models and it was found that the CBR model provided better prediction capability than MRA and NN models. Â© 2012 Inderscience Enterprises Ltd.

International Journal of Industrial and Systems Engineering

Evaluation of parametric models in predicting the machining performance

Artificial Intelligence for Engineering Design, Analysis and Manufacturing

Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

Cutting-fluid selection using complex proportional assessment method

Procedia Computer Science

Mining Sparse and Big Data by Case-based Reasoning

Artificial neural networks and case-based reasoning models for predicting tool life and tool-shim interface temperature

Journal	International Journal of Services and Operations Management
Publisher	Inderscience Publishers
ISSN	1744-2370
Open Access	0