Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network

Liu, Huaming; Qin, Xunpeng; Huang, Song; Jin, Lei; Yong-liang, Wang; Lei, Kaiyun

doi:10.1007/s12541-018-0126-8

Cited by 47 publications

(20 citation statements)

References 23 publications

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“…In contrast, practical challenges attract less attention from the CSAM community although providing a solution to them is a key aspect to facilitating the development of a commercial CSAM technology. One such practical challenge is the geometric control of as-fabricated components often associated with the nature of high production rate additive manufacturing technologies: namely, CSAM [8,9,24], Wire and Arc Additive Manufacturing (WAAM) [13,25] and Laser Cladding (LC) [26,27]. Low geometric control is attributed to a range of key issues that limit the application of additive manufacturing technologies such as the necessity of post-machining, difficulty in fabricating complex shapes, geometry-induced property variations and inconsistent quality of fabricated parts [8,9,28].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the great capability of ANN modelling as seen in other additive manufacturing processes, it has drawn only a small amount of interest as a track modelling approach from the CSAM community. Furthermore, the application of the ANN modelling in prediction was greatly limited to key geometric characteristics only, e.g., height and width, in additive manufacturing [27,33]; such observations formed an underlying motivation to study in mathematical modelling that could describe more detailed geometric track profiles. This trend can be seen in previous CSAM studies focusing on the mathematical approach only (i.e., Gaussian model) to predict a single-track profile at both normal and off-normal spray angles [24,34].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2019

Materials

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Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2019

Materials

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“…The laser power was 1600 W, and the defocus amount was 16 mm. The crack density was evaluated to measure the impact of the process parameters on the crack sensitivity, and is given by Equation (1). The laser cladding orthogonal experiment results are presented in Table 4.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…where G is the number of input neurons; Q is the number of output neurons; C is a constant between [1,10]; and M is the number of neurons in the hidden layer.…”

Section: Establishment Of Network Topology Model For Crack Predictionmentioning

confidence: 99%

“…Laser cladding technology is an advanced manufacturing method that uses a high-energy laser beam to irradiate cladding powder and a matrix to rapidly melt and solidify it [1]. It can produce high-performance alloy surfaces on inexpensive metal substrates without affecting the properties of the matrix, thus conserving valuable rare metal materials [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

Huang

Wang

et al. 2019

Coatings

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This paper aimed to establish a nonlinear relationship between laser cladding process parameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to control the cracking of the cladding layer via an intelligent algorithm. By using three main process parameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was designed, and the experimental results were used as training and testing datasets for a neural network. A neural network prediction model between the laser cladding process parameters and coating crack density was established, and a genetic algorithm was used to optimize the prediction results. To improve their prediction accuracy, genetic algorithms were used to optimize the weights and thresholds of the neural networks. In addition, the performance of the neural network was tested. The results show that the order of influence on the coating crack sensitivity was as follows: overlap rate > powder feed rate > scanning speed. The relative error between the predicted value and the experimental value of the three-group test genetic algorithm-optimized neural network model was less than 9.8%. The genetic algorithm optimized the predicted results, and the technological parameters that resulted in the smallest crack density were as follows: powder feed rate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of 0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization of the neural network and genetic algorithm process.

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Process Modeling of Laser-Based Metal Additive Manufacturing of Metal Alloy

Nath

2022

Advances in Processing of Lightweight Metal Alloys and Composites

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Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network

Cited by 47 publications

References 23 publications

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network

Process Modeling of Laser-Based Metal Additive Manufacturing of Metal Alloy

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