Multi-Objective Optimization of AISI P20 Mold Steel Machining in Dry Conditions Using Machine Learning—TOPSIS Approach

Abbas, Adel T.; Sharma, Neeraj; Al-Suhaibani, Zeyad; Sharma, Abhishek; Farooq, Irfan; Elkaseer, Ahmed

doi:10.3390/machines11070748

Cited by 4 publications

(3 citation statements)

References 28 publications

(34 reference statements)

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“…Upon analyzing the figures, distinctive surface defects were discerned in The distinctive characteristics of up-and-down milling contribute to these observed differences. In up-milling, the chip thickness begins at zero as the tool engages the workpiece, gradually progressing to maximum chip thickness at the end of the cutting [19]. This phenomenon encourages rubbing between the tool and the work material due to friction, leading to heightened plastic deformation and bulging.…”

Section: Effect Of Single-mode Milling Strategies On Surface Roughnessmentioning

confidence: 99%

Effect of Milling Strategy on the Surface Quality of AISI P20 Mold Steel

Abbas,

Abdelnasser,

Naeim

et al. 2023

Metals

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This paper explores the impact of various milling strategies, including up-milling, down-milling, and hybrid approaches, on the surface roughness of AISI P20 mold steel. The study is methodically divided into three stages to comprehensively understand the effects of these strategies. The first stage involves milling single slots with varying cutting parameters to establish baseline effects. The second stage examines the effects of consistent milling strategies (up-up and down-down) on surface quality. The third stage probes into hybrid strategies (up-down and down-up) to assess their effectiveness. Central to this investigation is not only the type of milling strategy but also how cutting speed and feed rate influence the resultant surface roughness. Our findings indicate that up-milling generally leads to a 22% increase in surface roughness compared to down-milling. This trend is visually verified by surface texture analyses. When comparing consistent strategies, up-up milling tends to produce rougher surfaces than down-down milling by approximately 25%, characterized by distinctive scratches and feed mark overlays. Remarkably, while the hybrid milling strategies do not exhibit significant differences in surface roughness, variations in cutting speed and feed rate play a crucial role. Specifically, at lower speeds, hybrid milling achieves smoother surfaces than the identical double milling mode, while at a cutting speed of 100 m/min, the double mode demonstrates a notable decrease in roughness. Additionally, this study introduces a color mapping simulation for machined pockets, validated by experimental results, to predict surface roughness based on the strategic history of milling, thereby offering valuable insights for optimizing milling processes.

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Section: Effect Of Single-mode Milling Strategies On Surface Roughnessmentioning

confidence: 99%

Effect of Milling Strategy on the Surface Quality of AISI P20 Mold Steel

Abbas,

Abdelnasser,

Naeim

et al. 2023

Metals

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“…It is clear from the literature that most of the research has been conducted on different hard alloys (Ti, Ni, and its alloys) [43]. There are some works conducted by Peng et al [44,45] on the wear behavior of Ni and Ti alloys while processing them with conventional machining using a high-pressure coolant supply.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM

Abbas,

Sharma,

Al-Bahkali

et al. 2023

JMMP

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Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.

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“…In this regrade, machine learning (ML) algorithms offer significant potential for solving optimization problems [25,26]. Extreme gradient boosting (XGBoost) is a redundant boosted tree mode that is currently the fastest of its kind.…”

Section: Introductionmentioning

confidence: 99%

Precision Face Milling of Maraging Steel 350: An Experimental Investigation and Optimization Using Different Machine Learning Techniques

Abbas,

Helmy,

Al-Abduljabbar

et al. 2023

Machines

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Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges.

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Multi-Objective Optimization of AISI P20 Mold Steel Machining in Dry Conditions Using Machine Learning—TOPSIS Approach

Cited by 4 publications

References 28 publications

Effect of Milling Strategy on the Surface Quality of AISI P20 Mold Steel

Effect of Milling Strategy on the Surface Quality of AISI P20 Mold Steel

A Machine Learning Perspective to the Investigation of Surface Integrity of Al/SiC/Gr Composite on EDM

Precision Face Milling of Maraging Steel 350: An Experimental Investigation and Optimization Using Different Machine Learning Techniques

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