A novel predictive model for abrasive waterjet deep hole drilling on AL7075 T6 using machine learning and evolutionary algorithmic approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259226" target="_blank" >RIV/61989100:27230/25:10259226 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001642184200013" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001642184200013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-28058-7" target="_blank" >10.1038/s41598-025-28058-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A novel predictive model for abrasive waterjet deep hole drilling on AL7075 T6 using machine learning and evolutionary algorithmic approach

Popis výsledku v původním jazyce

Abrasive Waterjet (AWJ) is a promising non-traditional method for precision cutting of aerospace materials like AL7075 T6. This work explores AL7075 T6 AWJ-based Deep Hole Drilling (DHD) using a full factorial design with accurate modeling and optimization performed through machine learning and evolutionary algorithms. The objective is to investigate the influence of process parameters and to model, and predict the optimal AWJ-DHD settings, such as waterjet pressure, standoff distance, and abrasive mass flow rate, on drilling qualities including geometrical and dimensional precision (kerf angle, kerf ratio), surface roughness, and drilling efficiency. Four machine learning models, Adaptive Boosted Regression (ABR), Extreme Gradient Boosting (XGB), Decision Tree (DT), and Random Forest (RF) were developed with experimental data to enhance prediction accuracy and process efficiency. Among the developed models, RF had the lowest testing error value for all responses with root mean square values of 0.046 (kerf angle), 0.0078 (kerf ratio), 0.044 (surface roughness), and 0.027 (drilling rate). Moth-Flame Optimization (MFO), Differential Evolution (DE), and Sine Cosine Algorithm (SCA) were used for multi-response optimization of AWJ deep hole drilling parameters. The optimal algorithm for each response was selected using Deng&apos;s similarity-based ranking. The ranking revealed SCA algorithm outperformed MFO and DE. The SCA algorithm discovered optimal parameter setting for AWJ-DHD as a water pressure of 350 MPa, standoff distance of 1.5 mm, and an abrasive mass flow rate of 300 g/min. Under these conditions, the predicted responses were a kerf angle of 0.048 degrees, kerf ratio of 0.011, a surface roughness of 1.438 mu m, and a drilling rate 0.769 mm/s. The validation trials using optimized parameters yielded a kerf angle of 0.047 degrees, a kerf ratio of 0.066, a surface roughness of 1.40 mu m, and a drilling rate of 0.769 mm/s, with percentage variations of 2.08%, 3.03%, 2.14%, and 2.65%, respectively, thereby demonstrating the efficiency of the developed machine learning model and optimization technique. The integrated machine learning and evolutionary algorithm framework improved drilling efficiency and hole quality by minimizing surface roughness.

Název v anglickém jazyce

A novel predictive model for abrasive waterjet deep hole drilling on AL7075 T6 using machine learning and evolutionary algorithmic approach

Popis výsledku anglicky

Abrasive Waterjet (AWJ) is a promising non-traditional method for precision cutting of aerospace materials like AL7075 T6. This work explores AL7075 T6 AWJ-based Deep Hole Drilling (DHD) using a full factorial design with accurate modeling and optimization performed through machine learning and evolutionary algorithms. The objective is to investigate the influence of process parameters and to model, and predict the optimal AWJ-DHD settings, such as waterjet pressure, standoff distance, and abrasive mass flow rate, on drilling qualities including geometrical and dimensional precision (kerf angle, kerf ratio), surface roughness, and drilling efficiency. Four machine learning models, Adaptive Boosted Regression (ABR), Extreme Gradient Boosting (XGB), Decision Tree (DT), and Random Forest (RF) were developed with experimental data to enhance prediction accuracy and process efficiency. Among the developed models, RF had the lowest testing error value for all responses with root mean square values of 0.046 (kerf angle), 0.0078 (kerf ratio), 0.044 (surface roughness), and 0.027 (drilling rate). Moth-Flame Optimization (MFO), Differential Evolution (DE), and Sine Cosine Algorithm (SCA) were used for multi-response optimization of AWJ deep hole drilling parameters. The optimal algorithm for each response was selected using Deng&apos;s similarity-based ranking. The ranking revealed SCA algorithm outperformed MFO and DE. The SCA algorithm discovered optimal parameter setting for AWJ-DHD as a water pressure of 350 MPa, standoff distance of 1.5 mm, and an abrasive mass flow rate of 300 g/min. Under these conditions, the predicted responses were a kerf angle of 0.048 degrees, kerf ratio of 0.011, a surface roughness of 1.438 mu m, and a drilling rate 0.769 mm/s. The validation trials using optimized parameters yielded a kerf angle of 0.047 degrees, a kerf ratio of 0.066, a surface roughness of 1.40 mu m, and a drilling rate of 0.769 mm/s, with percentage variations of 2.08%, 3.03%, 2.14%, and 2.65%, respectively, thereby demonstrating the efficiency of the developed machine learning model and optimization technique. The integrated machine learning and evolutionary algorithm framework improved drilling efficiency and hole quality by minimizing surface roughness.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

23

Strana od-do

nestránkováno

Kód UT WoS článku

001642184200013

EID výsledku v databázi Scopus

—