Prediction of workpiece dynamic motion using an optimized artificial neural network

Vishnupriyan, S.; Muruganandam, A.; Govindarajan, Lakshmi Narasimhan

doi:10.1177/0954405412457121

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Another popular soft computing method used extensively for solving the regression problems is ANN. 45–51 A set of nodes and link between the nodes is used to form the ANN model. The nodes are computational units; they receive information and process it.…”

Section: Soft Computing Methodsmentioning

confidence: 99%

A novel evolutionary approach in modeling wear depth of laser engineering titanium coatings

Garg

Vijayaraghavan

Tai

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this study, a new evolutionary approach, Akaike information criterion-based multi-gene genetic programming, is proposed for formulating the functional relationship of wear depth of the laser engineering titanium coatings. The carbon nanotube-reinforced titanium coatings were fabricated with 0, 10, 15, and 20 wt% carbon nanotubes. Six main input process variables such as specimen temperature, friction coefficient, contact potential, gap, carbon nanotube reinforcement composition (%), and sliding distance were considered. The laser cladding process was performed, and a total of 21,600 samples are collected, randomly divided into 17,280 and 4320 sets, and then trained and tested in the proposed algorithm. The performance of the proposed model is compared to that of the artificial neural network model. Statistical evaluation of the models concludes that the proposed model outperformed the artificial neural network. To validate the robustness of the proposed model, sensitivity and parametric analyses are conducted, and the impact of each input variable on the wear depth is studied. Analysis reveals that carbon nanotube reinforcement composition (%) plays a significant role in reducing the wear depth of the laser engineering titanium coatings.

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Section: Soft Computing Methodsmentioning

confidence: 99%

A novel evolutionary approach in modeling wear depth of laser engineering titanium coatings

Garg

Vijayaraghavan

Tai

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…It has been widely applied in the fields of the pattern recognition, function approximation, automatic control, signal processing, and so forth. Vishnupriyan et al 16 measured the acceleration signals of workpiece processing references to obtain the workpiece dynamic motion. If these experimental results were used as training samples, the workpiece dynamic motion can be predicted from the position of fixels.…”

Section: Introductionmentioning

confidence: 99%

A unified approach to multi-fixturing layout planning for thin-walled workpiece

Qin

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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It is crucial to properly design the fixturing layout described by fixturing parameters, such as the fixturing sequence, the placement of clamping force, the locator position, and so on. This is because the clamping deformation of the thin-walled workpiece can influence extremely the machining accuracy and surface quality. Generally speaking, the finite element method can be used to easily obtain the deformation rule of the workpiece caused by one single fixturing parameter. But it is difficult to reveal the relationship between the multiple fixturing parameters and the clamping deformation of workpiece. Therefore, the workable finite element model of multi-fixturing layout is above all established for the thin-walled workpiece. Thus, clamping deformations can be calculated to be the training samples of the neural network. Next, according to the training samples, the prediction model is suggested for obtaining the clamping deformation from multiple fixturing parameters. When the prediction errors are defined as fitness function, the genetic algorithm is developed to search the optimal initial weights and thresholds for the neural network. The optimized neural network has better generalization and prediction ability than the non-optimized one. Ultimately, the embedded optimal model with the objective of minimizing the clamping deformation is presented for a multi-fixturing layout. When the individual fitness of each generation is constructed as a function of the clamping deformations, the genetic algorithm can be skillfully used to solve the embedded optimal model. Moreover, the experiment is conducted to validate the prediction method with good agreement between the predicted results and the experimental data. The above presented “analysis—prediction—control” method of clamping deformation not only improves the calculation efficiency of clamping deformation but also provides a basic theory of fixturing layout design for the thin-walled workpiece.

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“…The setup/fixture planning methods can be classified into three categories: multiconstraints, fixture driven, and tolerance analysis planning methods. Vishnupriyan et al [19] proposed a method of using an artificial neural network (ANN) for the prediction of workpiece dynamic motion. Different layouts are obtained using a modular fixture and actual machining is performed on the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Fixture Layout Design Based on a Single-Surface Clamping with Local Deformation

Tadić¹,

Todorovič²,

Novkinic³

2015

Int. j. simul. model.

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Proposed in this paper is a novel approach to fixture layout design. The contact interface between workpiece and fixture is reduced to a single workpiece surface. This allows reliable machining of the remaining five workpiece surfaces in a single setup. Workpiece clamping is performed by inserting special elements into prepared auxiliary openings on the workpiece. Clamping is thus reduced to indenting of sharp clamping element tips into the workpiece surface. Shallow indenting is performed on surfaces which have no practical function. Special device was designed to allow experimental investigation. Experimental results point towards the efficiency of the proposed approach. Workpiece displacements were small during machining and allowed the required machining accuracy. Furthermore, the results obtained during machining indicate high surface quality.

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Prediction of workpiece dynamic motion using an optimized artificial neural network

Cited by 6 publications

References 19 publications

A novel evolutionary approach in modeling wear depth of laser engineering titanium coatings

A novel evolutionary approach in modeling wear depth of laser engineering titanium coatings

A unified approach to multi-fixturing layout planning for thin-walled workpiece

Fixture Layout Design Based on a Single-Surface Clamping with Local Deformation

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