Convolutional Neural Network for geometric deviation prediction in Additive Manufacturing

Zhu, Ziyi; Ferreira, Kevin; Anwer, Nabil; Mathieu, Luc; Guo, Kai; Qiao, Lihong

doi:10.1016/j.procir.2020.03.108

Cited by 38 publications

(6 citation statements)

References 10 publications

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“…In laser powder-directed energy deposition (LP-DED), three classic geometries, namely a plane wall, L-shaped wall, and rectangular box, are included in finite element analysis with thermal-mechanical modeling to generate training datasets; then, an ANN is applied to build up a model to predict residual stresses [116], as shown in Figure 13. In addition to the ANN model, a convolution neural network is also applied to predict geometric deviation before deposition to avoid defects [117]. The gray-box model approach is also included in the shot-peening process.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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“…In the study of additive manufacturing (AM) product quality prediction, reducing the time and cost of process design is key to achieving rapid prototyping. To avoid conducting large numbers of simulation experiments, small sample simulation results are used to train ML models for quickly predicting the inherent strain [40] or geometric deviation [41] of AM products. Additionally, ML can be used in the manufacturing phase, for instance, to predict the quality of surface roughness [42], tensile strength [43], porosity [44,45], and microstructural defects [46] based on digital images or machine sensors.…”

Section: Quality Prediction Based On Machine Learning Techniquesmentioning

confidence: 99%

Machine learning-enabled early prediction of dimensional accuracy for complex products of investment casting

Dong

Wang²,

Wang³

et al. 2023

Preprint

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For the foundry industry, predicting the dimensional accuracy of investment precision castings is vital yet challenging. In order to reduce cost loss caused by out-of-tolerance phenomena, this work develops a data-driven framework for estimating and screening early products based on machine learning techniques. The hollow turbine blade is analyzed as a typical case for the proposed framework. Initially, a database was compiled from the same production line of wax patterns and corresponding castings. Feature engineering techniques were employed to choose the most important characteristics and simplify inputs, employing reject rate analysis and decision tree analysis. Random forest regressors (RFRs) were chosen as the fundamental models after a sensible selection of machine learning algorithms. To enhance the performance of RFRs, the results suggested that the thickness distribution of the source material is another crucial element in determining the dimensional correctness of castings. Ultimately, a hybrid metaheuristic strategy incorporating RFR and dung beetle optimization was developed. The results showed that the proposed prediction model could minimize the error by 22.4% compared to conventional models, making it an valuable tool for early dimensional quality evaluation and guiding wall thickness control for hollow turbine blades.

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“…Neurons in a fully connected layer have full connections to all activations in the previous layer. Their activations can hence be computed with a matrix multiplication followed by a bias offset [68].…”

Section: A Fcdnn Structurementioning

confidence: 99%

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

et al. 2021

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A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.

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Convolutional Neural Network for geometric deviation prediction in Additive Manufacturing

Cited by 38 publications

References 10 publications

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Machine learning-enabled early prediction of dimensional accuracy for complex products of investment casting

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

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