High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models

Deng, Hongxing; Yao, Peng; Hai, Kuo; Yu, Shimeng; Huang, Chuanzhen; Zhu, Hongtao; Liu, Dun

doi:10.1007/s00170-023-11275-7

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Yuan et al [36] developed an empirical model in respect to several process parameters in order to predict the average depth of circular pockets machined by AWJ milling. Deng et al [37] used a neural network model in order to determine the optimum stepover and traverse speed values during pocket milling for the creation of an aspheric mirror. Kowsari et al [38] proposed a computational fluid dynamics (CFD)-based approach to predict the geometry of slots and pockets in order to take into account the contribution of fluid flow during the abrasive slurry jet machining of ceramics.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Efficient Modeling Approaches for Performing Controlled-Depth Abrasive Waterjet Pocket Milling

Karkalos,

Karmiris-Obratański

2024

Machines

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Non-conventional processes are considerably important for the machining of hard-to-cut alloys in various demanding applications. Given that the surface quality and integrity, dimensional accuracy, and productivity are important considerations in industrial practice, the prediction of the outcome of the material removal process should be able to be conducted with sufficient accuracy, taking into consideration the computational cost and difficulty of implementation of the relevant models. In the case of AWJ, various types of approaches have been already proposed, both relying on analytical or empirical models and developed by solving partial differential equations. As the creation of a model for AWJ pocket milling is rather demanding, given the number of parameters involved, in the present work, it is intended to compare the use of three different types of efficient modeling approaches for the prediction of the dimensions of pockets milled by AWJ technology. The models are developed and evaluated based on experimental results of AWJ pocket milling of a titanium workpiece by an eco-friendly walnut shell abrasive. The results indicate that a semi-empirical approach performs better than a two-step hybrid analytical/semi-empirical method regarding the selected cases, but both methods show promising results regarding the realistic representation of the pocket shape, which can be further improved by a probabilistic approach.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Efficient Modeling Approaches for Performing Controlled-Depth Abrasive Waterjet Pocket Milling

Karkalos,

Karmiris-Obratański

2024

Machines

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Design of a multidimensional data collection system based on DRS4 scheme

Ruan,

Li,

Hou

2024

Physical Communication

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Predicting the depth of rock cutting by abrasive water jet using support vector machine optimized with whale optimization algorithm

Li,

Ge,

Deng

et al. 2024

Physics of Fluids

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The utilization of abrasive water jet (AWJ) has garnered notable attention in subsurface engineering, as well as unconventional natural gas development, geothermal energy extraction, and tunnel excavation. The efficiency of construction operations is contingent upon rock fragmentation, which is controlled by AWJ parameters and rock properties. Currently, the parameter settings for rock fragmentation by AWJ predominantly rely on empirical approaches, and existing prediction models have large errors due to a limited number of training samples. In this study, we propose a combined support vector machine (SVM) and whale optimization algorithm (WOA) model. To test the model's predictive performance for rock-breaking depth, a database consisting of eight input parameters is constructed. These parameters include AWJ pressure, target distance, lateral velocity, abrasive types, mass flow rate, abrasive particle size, rock types, and rock uniaxial compressive strength. Additionally, to demonstrate the superiority of the WOA-SVM model, three other predictive models based on the back propagation (BP) network, SVM, and Random Forest (RF) are established, compared, and evaluated. The results show that the optimized WOA-SVM model is the most accurate in predicting rock cutting depth, achieving a precision rate of 0.972 25 compared to other models (BP: 0.9536; RF: 0.9681; SVM: 0.9687). Furthermore, sensitivity analysis highlights that lateral velocity exhibits the highest impact on the model, followed by jet pressure and the uniaxial compressive strength of rock. This underscores the critical importance of prioritizing the adjustment of lateral velocity, AWJ pressure, and rock properties when engaging in rock-cutting operations.

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High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models

Cited by 4 publications

References 21 publications

A Comparative Study of Efficient Modeling Approaches for Performing Controlled-Depth Abrasive Waterjet Pocket Milling

A Comparative Study of Efficient Modeling Approaches for Performing Controlled-Depth Abrasive Waterjet Pocket Milling

Design of a multidimensional data collection system based on DRS4 scheme

Predicting the depth of rock cutting by abrasive water jet using support vector machine optimized with whale optimization algorithm

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