Effect of process parameters on performance of grooved hot rolling of SAE 4340 steel bars

Singh, Gulvir; Singh, Pradeep K.

doi:10.1080/10426914.2022.2075894

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…The simulation output data, such as the defect volume fraction and average SDAS of the spoke surface, were gathered to establish the nonlinear input–output relationship with the process parameters. Following the model’s construction through regression analysis, its significance and predictive power were evaluated using analysis of variance (ANOVA) [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Multi-Objective Optimization of The Low-Pressure Casting of Large-Size Aluminum Alloy Wheels through a Systematic Optimization Idea

He,

Lu,

et al. 2023

Materials

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The process parameters in the low-pressure casting of large-size aluminum alloy wheels are systematically optimized in this work using numerical casting simulation, response surface methodology (RSM), and genetic algorithm (NSGA-II). A nonlinear input–output relationship was established based on the Box–Behnken experimental design (BBD) for the crucial casting parameters (pouring temperature, mold temperature, holding pressure, holding time), and response indicators (defect volume fraction, spokes large plane mean secondary dendrite spacing (SDAS)), and a mathematical model was developed by regression analysis. The Isight 2017 Design Gateway and NSGA-II algorithm were used to increase the population and look for the best overall solution for the casting parameters. The significance and predictive power of the model were assessed using ANOVA. Casting numerical simulation was used to confirm the best option. To accomplish systematic optimization in its low-pressure casting process, the mold cooling process parameters were adjusted following the local solidification rate. The results showed that the mathematical model was reliable. The optimal solutions were a pouring temperature of 703 °C, mold temperature of 409 °C, holding pressure of 1086 mb, and holding time of 249 s. The mold cooling process was further optimized, and the sequence solidification of the optimal solution was realized under the optimized cooling process. Finally, the wheel hub was manufactured on a trial basis. The X-ray detection, mechanical property analysis, and metallographic observation showed that the wheel hub had no X-ray defects and its mechanical properties were well strengthened. The effectiveness of the system optimization process scheme was verified.

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Section: Methodsmentioning

confidence: 99%

Multi-Objective Optimization of The Low-Pressure Casting of Large-Size Aluminum Alloy Wheels through a Systematic Optimization Idea

He,

Lu,

et al. 2023

Materials

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show abstract

“…Singh, G. and Singh, P. [12] used simulation with FORGE® NxT 1.1 software and the ANOVA technique to propose the best rolling parameters for SAE1020 Steel. Additionally, a connection was established for the rolling of SAE 4340 steel bars between the reaction and process parameters [13]. After doing a statistical analysis on around 100 data points, Ragab and Samy [4] discovered that the differences between the measured data for particular passes and the predictions ranged from −50% to +90%.…”

Section: /24mentioning

confidence: 99%

“…Flow stress in N/mm 2 , m=friction factor, 𝐴 𝑥 is a projected contact area as proposed by Sinel'nikove shown in the equation(13).where, n 𝑓 value to be used to calculate 𝐴 𝑥 is, n 𝑓 = 1.5 for D-D pass, 2.2 for O-R pass, 3.2 for O-DS pass, 1.25 for R-O and S-O pass, 2.6 for DS-O pass and 1 for Bx-Bx pass. Total Area = 𝛽 1 • 𝐴 𝑥 mm 2 𝛽 1 = 1.34 for D-D pass, 1.46 for O-R and O-Ds pass, 1.12 for R-O, S-O and Ds-O pass, 1.00 for Bx-Bx pass…”

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confidence: 99%

Analytical Approximation and Evaluation of Roll Separating Force in Bar Rolling Process.

Mistry,

Judal

2024

Preprint

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Designing the roller and determining the required passes to manufacture the bar depend on the estimation of the pressure value during the bar rolling operation. Numerous researchers have proposed a mean unit pressure formula based on the flat rolling process formula. However, it is challenging to predict the pressure accurately using these formulas. Hence, the principle of sticking and slipping friction as well as weak plane strain is applied to derive the analytical formula based on slab analysis. To apply the proposed formula for different types of passes, the given pass shape is converted into an equivalent rectangular bar and assumed that it is pressed between two flat plates. Prediction of metal flow stress value is calculated by using Shida’s formula with the proposed equation for the correction in input temperature of the hot metal. The constitutive formula of the roll separating force (RSF) is proposed based on these parameters. A comparison is made between the suggested formula and the experimental values which is available in the literature. The findings reveal that the RSF values obtained by applying the suggested method offer a reliable approximation for different kinds of passes.

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“…Their typical applications include airplanes, automobiles, machine tools, and industrial robots. Bearing steel is one of the most high-standard alloy steels, and its production quality can be improved through process parameter optimization [1][2][3]. In industrial scenarios, heavy loads and high-intensity operating conditions make bearings highly susceptible to varying degrees and unpredictable damage such as cracks and pitting corrosion.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

Qifeng,

Longsheng,

Naeem

2024

PLoS ONE

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Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

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Effect of process parameters on performance of grooved hot rolling of SAE 4340 steel bars

Cited by 10 publications

References 32 publications

Multi-Objective Optimization of The Low-Pressure Casting of Large-Size Aluminum Alloy Wheels through a Systematic Optimization Idea

Multi-Objective Optimization of The Low-Pressure Casting of Large-Size Aluminum Alloy Wheels through a Systematic Optimization Idea

Analytical Approximation and Evaluation of Roll Separating Force in Bar Rolling Process.

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings

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