Machine learning-based prediction of bushing dimensions, surface roughness and induced temperature during friction drilling of pre-heated A356 aluminum alloy

Aug 5, 2026

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This study applies machine learning algorithms—Random Forest Regressor (RFR) and Gradient Boosting Regressor (GBR)—to predict critical outcomes of friction drilling of A356 aluminum alloy. Rotational speed (RS), feed rate (FR), and preheat temperature (PH) are optimized to obtain high-quality bushings. Using a dataset of 27 experiments, the work predicts bush height (ha), thickness (t), surface roughness (Ra), and induced interface temperature (T). RS and PH strongly govern ha and T, while FR more affects t and Ra. GBR more accurately predicts ha, t, and Ra, whereas RFR better models T.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Friction drilling and its benefits

Influence of drilling parameters on bushing quality

Motivation: as-cast A356 and the role of preheating

Need for optimization and machine learning approaches

Materials Today Communications 45 (2025) 112420 Contents lists available at ScienceDirect Materials Today Communications journal [homepage:](homepage: www.elsevier.com/locate/mtcomm)[ www.elsevier.com/locate/mtcomm](homepage: www.elsevier.com/locate/mtcomm) | Machine learning-based prediction of bushing dimensions, surface roughness and induced temperature during friction drilling of pre-heated A356 aluminum alloy
Mahmoud Khedra,b,* , Ahmed Abdalkareem a, Amr Moniera,c, Rasha Afifya,
Tamer S. Mahmoud a, Antti J¨arvenp¨¨aa d
a Mechanical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt
b Future Manufacturing Technologies (FMT), Kerttu Saalasti Institute, University of Oulu, Pajatie 5, Nivala FI-85500, Finland c Institute of Ultrasonic Technology, Shenzhen Polytechnic University, Shenzhen, 518055, P.R. China
d Laser processing and additive manufacturing, Mechanical Engineering Department, School of Energy Systems, Lappeenranta-Lahti University of Technology LUT, P.O. Box 20, FI-53851 Lappeenranta, Finland | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Thermal drilling Surface roughness Bush dimensions Machine learning
Random Forest Regressor Gradient Boosting Regressor Data-driven manufacturing | | This study explores the application of machine learning algorithms, specifically Random Forest Regressor (RFR) and Gradient Boosting Regressor (GBR), to predict key outcomes of the friction drilling of A356 aluminum alloy. Optimizing process parameters such as rotational speed (RS), feed rate (FR), and preheat temperature (PH) is critical to achieve high-quality bushings during friction drilling. The study focused on predicting bush height (ha), thickness (t), surface roughness (Ra), and the induced temperature at workpiece/drilling-tool interface (T) through a dataset consisting of 27 experiments. The results showed that RS and PH had a significant influence on ha and T, with higher values of both parameters leading to increased bush height and induced temperature. Nevertheless, FR demonstrated a weaker effect on these responses but had a more pronounced impact on t and Ra. Feature importance analysis revealed that RS and PH were the most critical parameters for optimizing the friction drilling process, while FR had a lower effect. Additionally, the GBR model outperformed the RFR model in predicting ha, t, and Ra, providing more accurate results for these dimensions. Whereas the RFR exhibited abetter behavior in predicting T, demonstrating the machine learning potential to enhance precision of the formed bushings. | 1. Introduction Friction drilling, also known as form or thermal drilling, is a nonconventional method of creating bushings and holes in thin-walled materials such as aluminum alloys [1,2]. The process employs a rotating conical or hexagonal tool to generate heat through friction, softening the material and displacing it to form the desired hole [3,4]. Unlike conventional drilling, friction drilling offers benefits such as bushing formation, which increases material strength and offers better load-bearing capacity, making it widely used in aerospace, and automotive industries [5,6]. The dimensions and quality of the formed bushings are intensively affected by the friction drilling parameters such as the drilling tool rotational speed and feed rate [7,8]. Generally, rises in the rotational speed and reductions in the feed rate led to a better surface roughness and a longer bushing height [9]. However, brittle materials in the as-cast condition are rarely investigated through friction drilling processing to avoid petal formation in the formed bushings, such as A356 aluminum alloy [10–12]. A356 aluminum alloy is commonly used in marine and automotive applications for its excellent castability, mechanical strength, and corrosion resistance [13]. Furthermore, A356 is utilized in industrial applications

FAQs

Which machine learning models are used to predict friction drilling outcomes?

Random Forest Regressor (RFR) and Gradient Boosting Regressor (GBR) are used to predict bushing dimensions, surface roughness, and induced temperature.

What inputs and outputs are modeled in the study?

Inputs are rotational speed (RS), feed rate (FR), and preheat temperature (PH). Outputs include bush height (ha), thickness (t), surface roughness (Ra), and induced interface temperature (T).

How do RS, FR, and PH affect the predicted responses?

Higher RS and PH significantly increase bush height and induced temperature, while FR has a weaker overall effect on ha and T but a stronger impact on thickness (t) and surface roughness (Ra).

Which model performs better for different responses?

GBR outperforms RFR for predicting ha, t, and Ra with higher accuracy, while RFR shows better behavior for predicting the induced temperature (T).

Aug 5, 2026

Upload by Eden