A generalized approach to the machining economics optimization.

(1988) A generalized approach to the machining economics optimization. Masters thesis, King Fahd University of Petroleum and Minerals.


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An extensive review of the machining optimization models has been carried out. Optimization methods pertinent to machining optimization have been compiled and evaluated by applying them to a number of selected machining models. Sequential Unconstrained Minimization Technique (SUMT) and a peresonal computer version of the Generalized Reduced Gradient (GRG2), known as the Generalized Integrated Optimizer (GINO), were found to be most suitable for application in machining economics optimization. Taking multipass turning as a representative case of multipass machining, five cutting strategies have been proposed for multipass machining optimization. Cutting strategies are passed on the optimal way of distributing the total depth of layer to be removed, over several passes required to do the job, while satisfying a given set of constraints. The performance of the proposed cutting strategies has been evaluated and their sensitivity has been demonstrated through a general machining optimization model to some of the process variables and constraints. A most economical machining strategy has been identified out of the proposed strategies. A general computer program package based on SUMT optimization method, which runs on both the mainframe and personal computer, has been developed to handle generalized multipass machining optimization problems. With some modifications, the developed program package is also capable of handling multi-tool and multi-operation optimization problems both in research and industrial environment.

Item Type: Thesis (Masters)
Subjects: Mechanical
Department: College of Engineering and Physics > Mechanical Engineering
Committee Advisor: Shuaib, AbdelRahman N.
Committee Members: Duffuaa, Salih O. and Sheikh, Anwar K.
Depositing User: Mr. Admin Admin
Date Deposited: 22 Jun 2008 13:47
Last Modified: 01 Nov 2019 13:50
URI: https://eprints.kfupm.edu.sa/id/eprint/9723