Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Chintakindi, Sanjay; Al-Samhan, Ali M.; Abidi, Mustufa Haider; Kumar, Maduri Praveen

doi:10.1007/s44196-022-00070-z

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…is work [13] investigated the impact of the annealing process at 1000 °C at varying machining parameters using principal component analysis, hyper-parameter optimization, and particle swarm optimization. e forecasted results were verified with experimental trial results.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Rajesh

Prabhuswamy

Naik

2022

Advances in Materials Science and Engineering

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Roughness is a prime parameter in any process/operation as it aids in confirming the quality status of the product. The insert and workpiece would develop a lot of friction and as a result, it generates heat in the cutting zone, which affects the machined surface. The speed, feed, and depth of cut were chosen as processing factors. L27 Orthogonal array is used based on the Taguchi technique. The regression analysis is used to develop an equation to predict the roughness. The impact of the processing factors on the machined surface is studied with help of ANOVA (Analysis of Variance). Furthermore, the estimation of surface roughness is carried out using a machine learning-based model-feed forward (nonlinear autoregressive network) NARX network, and the evaluated surface roughness is compared with the values predicted by the regression model and experimental results. The average percentage error observed with the predicted values by NARX is observed as 3.01%, which is lower than the average percentage error observed by the regression model 5.131%. Thus, this work provides the best machine learning approach to the prognosis of the roughness in dry turning of Inconel 625, which would save a lot of time and unnecessary wastage of the work material.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Rajesh

Prabhuswamy

Naik

2022

Advances in Materials Science and Engineering

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“…Several optimization methods were found in the literature for optimizing the parameters of different machining techniques. For example, a multi-objective particle swarm method was applied for the optimization of process parameters while turning Monel-400 alloy [36]. Abdo et al [37] used the MOGA tool to optimize the parameters of rotary ultrasonic machining for the microchannelling of Alumina ceramic.…”

Section: Optimization Resultsmentioning

confidence: 99%

“…As per the author's knowledge, there are no studies on the optimization of the drilling process parameters of Monel-400. However, the surface roughness of the machined surface of Monel-400 at optimal parameters of the turning process was found to be Ra = 2.26 µm and 2.17 µm, as documented in [36,40]. The optimal design points are presented in Figure 17 and listed in Table 14.…”

Section: Optimum Pointsmentioning

confidence: 88%

Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy

Abdo,

Almuzaiqer,

Noman

et al. 2023

JMMP

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A nickel-based copper alloy known as Monel-400 is extensively applied in many industries including aerospace, marine engineering, and nuclear power generation, owing to its exceptional characteristics such as extreme tensile strength and toughness, excellent corrosion resistance, and the ability to retain shape even at extremely high temperatures. Traditional methods of drilling Monel-400 alloy are difficult due to quick tool wear and poor surface polishing, resulting in expensive machining costs. In this study, a technique called heat annealing was implemented to externally heat-treat the Monel-400 alloy material before the drilling process. Cutting force, surface roughness, and tool wear were used as the responses to investigate the effect of heat annealing and the drilling parameters on the machinability of Monel-400. The results revealed that the cutting force (Fz) and surface roughness (Ra and Rt) could be reduced by 33%, 31%, and 25%, respectively, after annealing at 700 °C compared to the results of the drilled Monel-400 at room temperature. It can be observed that the maximum improvement can reach 42% of Fz, 35% of Ra, and 59% of Rt while annealing Monel-400 at 1000 °C. A significant reduction was observed in the tool wear for machining the annealed material, which minimized the tooling and overall machining cost. Regarding the effects of the drilling process on the considered responses, the results revealed that the spindle speed has a greater effect on the cutting force, whereas the feed rate has the most significant effect on Ra. The significance of the drilling input parameters on the outputs is determined by analysis of the main effect plots and surface plots. Subsequently, the multi-objective genetic algorithm (MOGA) is used to identify the optimal parametric conditions for minimizing the cutting force and surface roughness of the drilled holes. The optimized values achieved via multi-objective optimization are the cutting force, Fz = 388–466 N, and the surface roughness, Ra = 0.17–0.19 μm and Rt = 3–3.5 μm, respectively.

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“…Second, the trail is terminated when the predefined condition is not met. Optuna’s last design feature is associated with its ease of setup, which allows it to be easily configured for lightweight experiments to heavy-weight distributed computations under the versatile architecture [ 60 , 61 ].…”

Section: Tree-based Machine Learning Architecture To Predict Impedanc...mentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Cited by 14 publications

References 46 publications

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625

Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

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