Semi-empirical model for depth of cut in abrasive waterjet machining of metal matrix composites

Mohankumar, V.; Kanthababu, M.

doi:10.1007/s40430-020-02581-2

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…Grey relations analysis (GRA), which is a multi-response decision-making method [2,26], was used to investigate the complex relationship between the process parameters of the AWJ milling of Hastelloy C-276, namely MRR and R a , and how they impact the performance of the process. The GRA process involved three stages, as illustrated in Fig.…”

Section: Multi-response Optimisation and Grey Relational Analysismentioning

confidence: 99%

Multi-response parametric optimisation of abrasive waterjet milling of Hastelloy C-276

Gopichand¹,

Sreenivasarao

2020

SN Appl. Sci.

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Abrasive waterjet (AWJ) milling is a rapidly evolving research topic in the development of unconventional methods for machining high-strength materials without affecting their physical and thermal properties. It is considered as a promising technique for machining milling pockets in Hastelloy C-276. In the present study, the AWJ milling behaviour of Hastelloy C-276 was examined with the purpose of evaluating the performance parameters, namely the material removal rate and surface roughness (R a ). The considered input process parameters included the waterjet pressure, step over, traverse rate, and abrasive flow rate. The response surface methodology with a Box-Behnken design was used to perform the experiments, which involved 29 machining runs. Analysis of variance was used to identify the significant parameters of the machining process, and the optimal process parameter combination for achieving a high material removal rate and low surface roughness was established with the aid of response surface graphs. Multi-response optimisation based on grey relational analysis was also performed taking into consideration the overall output response. The waterjet pressure and traverse rate were identified as the primary determinants of the material removal rate, whereas the step over was the predominant factor of the surface roughness. The selected AWJ milling conditions based on grey relational analysis approach were examined using surface morphology, surface topography of milled pockets on Hastelloy C-276.

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Section: Multi-response Optimisation and Grey Relational Analysismentioning

confidence: 99%

Multi-response parametric optimisation of abrasive waterjet milling of Hastelloy C-276

Gopichand¹,

Sreenivasarao

2020

SN Appl. Sci.

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“…The output response variables MRR, RA, and Ka, are predicted using MINITAB 18 software utilizing a multilinear stepwise regression analysis. The regression model is written as follows with a sample size of n observations for the dependent variable Y (output response) [16,29]:…”

Section: Rsm Modelingmentioning

confidence: 99%

“…Pahuja and Ramulu [26] created a semi-empirical model to determine the depth of cut in the AWJM of Ti6Al4V, CFRP, and layered Ti/CFR. Mohankumar et al [27][28][29] developed using AWJM semi-empirical dimensional analysis-based methods to determine the cutting depth, the angle of the kerf, and the roughness of the surface on MMC.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Abrasive Waterjet Machining Parameters of Military-Grade Armor Steel by Semi-Empirical and Regression Models

et al. 2022

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Rolled homogeneous armor steel (RHA) with a high tensile strength, toughness, and hardness is often used in military combat vehicles. RHA is a high-strength low alloy steel suitable for all battlefield usage in military vehicles. The present work examines the prediction output responses in the material removal rate (MRR), surface roughness (Ra), and kerf angle (Ka) for the AWJM of armor steel using regression and semi-empirical models. The AWJM trials were performed using an L27 factorial design with each process variable set to three levels, namely, the standoff distance (SOD), jet traversing speed (JT), and jet water pressure (P). A regression model was constructed using the response surface method (RSM) and data from the trials. Through dimensional analysis and with Buckingham’s π-theorem, a semi-empirical model was built using both the experimental data and material property data. Predictions made by the models were proportionate with the results of the experiments under the same conditions. Microscopic investigations on MRR and Ra were performed using a scanning electron microscope (SEM). The optimal values of the output responses of the machined armor steel plate were obtained with higher MRR = 298.92 mm3/min, lower Ka = 0.651°, and lower Ra = 2.23 µm. The present work established that semi-empirical models accurately predict the output responses in the AWJM of armor steel.

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“…As the AWJM is characterized by several control parameters, selecting the best set of these to achieve the maximum depth of cut is an arduous and time-consuming process. Thus, Mohankumar et al [ 18 ] chose process variables through suitable modeling techniques. They used a semi-empirical model using Buckingham’s pi theorem to predict the depth of cut during the AWJM of reinforced aluminum metal matrix composites.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Studies into Cutting of a Novel Two Component 3D-Printed Stainless Steel–Polymer Composite Material by Abrasive Water Jet

et al. 2023

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Composites are materials with a heterogeneous structure, composed of two or more components with different properties. The properties of composites are never the sum or average of the properties of their components. There is a lot of research and many models on the different property assessments of composite materials. Composites are used as construction materials in key areas of technology, including in civil and mechanical engineering, aviation and space technology, and others. This work presents a modern composite material created with 3D-printing technology using the SLM method, and the possibility of its processing with one of the advanced manufacturing technologies, i.e., the Abrasive Water Jet (AWJ). Tests planned using DoE methods were carried out by changing control parameters such as the pressure, abrasive flow, and traverse speed. As a dependent parameter, the surface roughness parameter Sq (squared mean height) was selected and measured in different places of the cut composite. Based on the S/N ratio, the most favorable control parameters of the cutting process were also determined to achieve the lowest roughness of the cut surface. A clear effect of the controlled cutting process on the surface roughness was observed, as well as roughness variation for the metal and polymer component. In addition, the contact surface of the polymer with the metal in the cut zone was analyzed. Analysis of the contact surfaces on the microscope showed that the gap between the polymer–metal contact surfaces does not exceed 2.5 μm.

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Semi-empirical model for depth of cut in abrasive waterjet machining of metal matrix composites

Cited by 16 publications

References 24 publications

Multi-response parametric optimisation of abrasive waterjet milling of Hastelloy C-276

Multi-response parametric optimisation of abrasive waterjet milling of Hastelloy C-276

Prediction of Abrasive Waterjet Machining Parameters of Military-Grade Armor Steel by Semi-Empirical and Regression Models

Preliminary Studies into Cutting of a Novel Two Component 3D-Printed Stainless Steel–Polymer Composite Material by Abrasive Water Jet

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