Optimization of Surface Roughness, Material Removal Rate and cutting Tool Flank Wear in Turning Using Extended Taguchi Approach

Khandey, Umesh (2009) Optimization of Surface Roughness, Material Removal Rate and cutting Tool Flank Wear in Turning Using Extended Taguchi Approach. MTech thesis.



Quality and productivity play significant role in today’s manufacturing market.From customers’ viewpoint quality is very important because the extent of quality of the procured item (or product) influences the degree of satisfaction of the consumers during usage of the procured goods. Therefore, every manufacturing or production unit should concern about the quality of the product. Apart from
quality, there exists another criterion, called productivity which is directly related to the profit level and also goodwill of the organization. Every manufacturing
industry aims at producing a large number of products within relatively lesser time. But it is felt that reduction in manufacturing time may cause severe quality
loss. In order to embrace these two conflicting criteria it is necessary to check quality level of the item either on-line or off-line. The purpose is to check whether
quality lies within desired tolerance level which can be accepted by the customers. Quality of a product can be described by various quality attributes. The attributes may be quantitative or qualitative. In on-line quality control controller and related equipments are provided with the job under operation and continuously the quality is being monitored. If quality falls down the expected
level the controller supplies a feedback in order to reset the process environment. In off-line quality control the method is either to check the quality of few products from a batch or lot (acceptance sampling) or to evaluate the best process environment capable of producing desired quality product. This invites optimization problem which seeks identification of the best process condition or
parametric combination for the said manufacturing process. If the problem is related to a single quality attribute then it is called single objective (or response)
optimization. If more than one attribute comes into consideration it is very difficult to select the optimal setting which can achieve all quality requirements
simultaneously. Otherwise optimizing one quality feature may lead severe quality loss to other quality characteristics which may not be accepted by the customers.
In order to tackle such a multi-objective optimization problem, the present study applied extended Taguchi method through a case study in straight turning of mild
viii steel bar using HSS tool. The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality and
as well as productivity with special emphasis on reduction of cutting tool flank wear. Because reduction in flank wear ensures increase in tool life. The predicted
optimal setting ensured minimization of surface roughness, height of flank wear of the cutting tool and maximization of MRR (Material Removal Rate). In view of the fact, that traditional Taguchi method cannot solve a multi-objective
optimization problem; to overcome this limitation grey relational theory has been coupled with Taguchi method. Furthermore to follow the basic assumption of Taguchi method i.e. quality attributes should be uncorrelated or independent. But is practical case it may not be so. To overcome this shortcoming the study applied Principal Component analysis (PCA) to eliminate response correlation
that exists between the responses and to evaluate independent or uncorrelated quality indices called Principal Components. Finally the study combined PCA,
grey analysis, utility concept and Taguchi method for predicting the optimal setting. Optimal result was verified through confirmatory test. This indicates
application feasibility of the aforesaid techniques for correlated multi-response optimization and off-line quality control in turning operation.

Item Type:Thesis (MTech)
Uncontrolled Keywords:quality, productivity, straight turning, surface roughness, MRR (Material Removal Rate), flank wear, grey-Taguchi method, Principal Component analysis (PCA)
Subjects:Engineering and Technology > Mechanical Engineering > Production Engineering
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:1378
Deposited By:Umesh Khandey
Deposited On:28 May 2009 11:35
Last Modified:28 May 2009 11:35
Supervisor(s):Datta, S

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