Dimensional accuracy of workpart under machining is strongly influenced by the layout of the fixturing elements like locators and clamps. Setup or geometrical errors in locators result in overall machining error of the feature under consideration. Therefore it is necessary to ensure that the layout is optimized for the desired machining tolerance for a given deviation in the set up or geometry of the locator. Also, the locator layout should be capable of holding the workpart in a unique position during machining thus providing deterministic location. This paper proposes a Genetic Algorithm (GA) based optimization method to arrive at a layout of error containing locators for minimum machining error satisfying the tolerance requirements and providing deterministic location. A three dimensional workpiece under the 3-2-1 locating scheme is studied. Results indicate that by optimally placing the error containing locators the geometric error component of the machining error can be substantially reduced thus enabling compliance to overall dimensional requirements.
PurposeSource errors in a workpiece fixture system include the compliance of the workpiece fixture system and workpiece dynamics. The purpose of this paper is to study the relative significance of these two. The findings would help to achieve computational economy in optimization of fixture layout and/or clamping forces.Design/methodology/approachDifferent layouts are generated with the help of a reconfigurable fixture set up and a slot is end milled on the workpiece. Using these data and the finite element software ANSYS, the machining error due to system compliance is computed. The machining error due to workpiece dynamics is obtained using a data acquisition system with the LabView software. These steps are repeated for different clamping forces and the relative contribution of these two sources to the overall machining error is studied.FindingsResults show that the system compliance is much smaller in magnitude compared to workpiece dynamics and hence does not contribute appreciably to the overall machining error. This leads to the conclusion that, for bulky and stiff parts, evaluation of the machining error due to compliance can be done away with.Originality/valueThe paper's originality lies in comparing the two sources of machining error using experimental work and finite element models. To the author's knowledge such a comparison has not been reported in the literature.
A machining fixture is an element used to hold the workpiece in the desired position and orientation during machining. The overall machining error in a workpiece is a result of different sources of errors in a workpiece–fixture system. One among them is the motion of the workpiece under the action of cutting forces. Evaluation of this dynamic motion is essential for the determination of the overall machining error. Most commonly, the finite element method is employed to compute the workpiece dynamic motion. During optimization of fixture layout, a large number of layouts are generated and the workpiece dynamic motion must be computed for each of the layouts. In such cases, use of the finite element method is prohibitive because of the long computation time required. Also, the results of the finite element analysis are susceptible to different parameters used in the analysis. Hence, an alternate and efficient methodology is necessary to determine the workpiece displacement for a given fixture layout. This article proposes a method of using an artificial neural network for the prediction of workpiece dynamic motion. Different layouts are obtained using a modular fixture and actual machining is performed on the workpiece. For each layout, the workpiece dynamic motion is computed at select datum points and an artificial neural network is trained with these data. To achieve better prediction capability of the artificial neural network and minimize different forms of errors in training and generalization, critical parameters of the artificial neural network are optimized using a genetic algorithm. Then, this optimized network is employed to predict the workpiece dynamic motion for any arbitrary layout. Results show that the optimized artificial neural network is capable of predicting the workpiece dynamic motion with acceptable accuracy (maximum absolute relative error 9.71%). This method, hence, can serve as an economical means of computing the overall machining error during optimization of fixture layouts.
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