Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

Tanvir, Mahamudul Hasan; Hussain, Afzal; Rahman, Md. Maksudur; Ishraq, Sakib; Zishan, Khandoker; Rahul, SK Tashowar Tanzim; Habib, M. A.

doi:10.3390/jmmp4030064

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…The WOA is an algorithm that simulates the hunting behavior of whales rounding up prey using random or optimal search agents and spirals to simulate the bubble net attack mechanism of humpback whales. The WOA is characterized by its simple mechanism, few parameters, and strong optimization capability [29]. The distance and position vectors between individuals in the WOA are as follows:…”

Section: Methodology Of T-sne-woa-lstmmentioning

confidence: 99%

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Liu,

Qu,

Xie

et al. 2024

Processes

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The performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production.

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Section: Methodology Of T-sne-woa-lstmmentioning

confidence: 99%

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Liu,

Qu,

Xie

et al. 2024

Processes

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“…Another example of optimization is the use of a hybrid WOA (Whale Optimization Algorithm) algorithm. Using this algorithm, a combination of machining parameters is obtained, which ensures maximum material removal speed and minimum surface roughness [20]. Neural networks and genetic algorithms are also used to predict machining effects and optimize the input parameters of the process [21].…”

Section: Wavelet Analysismentioning

confidence: 99%

Analysis of the Possibility of Using Wavelet Transform to Assess the Condition of the Surface Layer of Elements with Flat-Top Structures

2020

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The paper focused on a topic related to the possibilities of using wavelet analysis to evaluate the changes in the geometrical structures of the surfaces arising during the honing process with whetstones with variable granularity. The cylinder liners of the combustion engine are machined elements. The basics of the wavelet analysis and the differences between filtering with standardized filters (e.g., Gauss filter), Fourier analysis, and the analysis of the results obtained when measuring the surface roughness with other wavelets were described. Trials of honing four cylinder liners were carried out. Roughness measurements of 3D spatial structures of the prepared liners were made. The principle of selecting wavelets for roughness assessment of structures with cross-hatch pattern was described. Roughness structures generated on the honed surfaces of cylinder liners were assessed using Gaussian filtration and Morlet, Daubechies Db6, and Mexican hat wavelets. In order to demonstrate the differences generated when the Gaussian filtration and selected wavelets were used on surface structures, the surfaces obtained with the use of these filtering tools were subtracted from each other, which allowed obtaining information about the changes occurring on the assessed surfaces, which were generated after the use of various filtering tools. For the assessed surfaces, during the subtraction operation, the mean square error was calculated, informing about the degree of similarity of both compared surfaces. The result of the work carried out is the creation of basic recommendations for the selection of wavelets when assessing honed surfaces with different degrees of regularity of the traces generated on them.

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“…In recent years, bio-inspired optimization algorithms such as genetic algorithms, artificial bee colony algorithms, and cuckoo search algorithms have achieved significant success in the field of machining [ 17 ], which provide novel approaches for optimizing process parameters in machining. Tanvir et al [ 18 ] proposed a hybrid whale optimization algorithm and applied it to optimize the cutting parameters of stainless steel. The results showed significant improvements in the performance of stainless steel turning operations after multi-objective optimization using the hybrid whale algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm

Zhu

Chen

et al. 2023

Sensors

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Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead to the deterioration of the machined surface roughness. This paper proposes a brand-new multi-objective dung beetle algorithm. It does not take the cutting parameters (i.e., cutting speed, feed rate, and depth of cut) as the optimization objects but directly optimizes cutting forces and vibration signals monitored using a multi-sensor (i.e., dynamometer and accelerometer). The cutting parameters in the WHA turning process are analyzed through the use of the response surface method (RSM) and the improved dung beetle optimization algorithm. Experimental verification shows that the algorithm has better convergence speed and optimization ability compared with similar algorithms. The optimized forces and vibration are reduced by 9.7% and 46.47%, respectively, and the surface roughness Ra of the machined surface is reduced by 18.2%. The proposed modeling and optimization algorithms are anticipated to be powerful to provide the basis for the parameter optimization in the cutting of WHAs.

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Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

Cited by 20 publications

References 27 publications

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Analysis of the Possibility of Using Wavelet Transform to Assess the Condition of the Surface Layer of Elements with Flat-Top Structures

Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm

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