An explainable ensemble machine learning model to elucidate the influential drilling parameters based on rate of penetration prediction

Zhipeng, Feng; Gani, Hamdan; Damayanti, Annisa Dwi; Gani, Hendra Saputra

doi:10.1016/j.geoen.2023.212231

Cited by 6 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through soft computing modeling, researchers can rely on a robust approach that consistently yields thorough, accurate, and dependable results. Recently, researchers have been using various soft computing techniques, including response surface methodology (RSM) [17], random forest technique (RF) [18], artificial neural network (ANN) [19], and design of experiments (DOE) [20]. These methods provide researchers with versatile tools to navigate the complexities of the drilling process in composites.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study on the Optimization of Drilling Performance in Hybrid Nano-Composites and Neat CFRP Composites Using Statistical and Machine Learning Approaches

Nargis,

Shahabaz,

Acharya

et al. 2024

JMMP

View full text Add to dashboard Cite

Carbon fiber-reinforced polymer (CFRP) composites have gradually replaced metals due to their exceptional strength-to-weight ratio compared to metallic materials. However, the drilling process often reveals various defects, such as surface roughness, influenced by different drilling parameters. This study explores the drilling quality of uni-directional CFRP composites, as well as hybrid Al2O3 alumina and hybrid SiC silicon carbide nano-composites, through experimental exploration using step, core, and twist drills. Response surface methodology (RSM) and statistical tools, including main effect plots, ANOVA, contour plots, and optimization techniques, were used to analyze the surface roughness of the hole. Optimization plots were drawn for optimal conditions, suggesting a spindle speed of 1500 rpm, feed of 0.01 mm/rev, and a 4 mm drill diameter for achieving minimum surface roughness. Furthermore, two machine learning models, artificial neural network (ANN) and random forest (RF), were used for predictive analysis. The findings revealed the robust predictive capabilities of both models, with RF demonstrating superior performance over ANN and RSM. Through visual comparisons and error analyses, more insights were gained into model accuracy and potential avenues for improvement.

show abstract