Evidence map›Paper›PMID 40870230›Full record

ArticleMaterials (Basel, Switzerland)2025

Optimizing Gun Drilling Parameters for Oxygen-Free Copper Using Response Surface Methodology and Genetic Algorithm.

Xiaolan Han, Hailong Wang, Yazhou Feng, Shengdun Zhao

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaolan HanMechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China.
Hailong WangMechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China.
Yazhou FengMechanical Engineering College, Xi'an Shiyou University, Xi'an 710065, China.ORCID 0000-0002-4158-3789
Shengdun ZhaoState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.ORCID 0000-0002-9191-9017

Funding

Natural Science Basic Research Program of Shaanxi 2023-JC-YB-387State Key Laboratory for Mechanical Behavior of Materials 20222424State Key Laboratory of Metal Forming Technology and Heavy Equipment S2308100.W10Xixian New Area Science and Technology Plan Project XJZZ-2023-010
6 · The paper itself

Abstract

To improve chip removal efficiency and drilling performance in oxygen-free copper, a multi-objective optimization of gun drilling process parameters was conducted using a response surface methodology and a genetic algorithm. The Box-Behnken Design (BBD) response surface analysis method was employed to evaluate the effects of feed rate, cutting speed, and cutting fluid pressure on the chip evacuation coefficient and chip volume ratio. Experimental results indicate that among the three factors, the feed rate has the most significant influence, followed by the cutting speed and the cutting fluid pressure. Additionally, the interaction between the cutting speed and the cutting fluid pressure notably impacts both chip evacuation and chip volume ratio. Using response surface modeling, a three-dimensional predictive model was developed. Based on this fitted model, optimal gun drilling parameters were identified through genetic algorithm optimization, minimizing the chip evacuation coefficient and chip volume ratio to achieve an optimized machining configuration. The optimal drilling parameters were identified as a feed rate of 0.019 mm/r, a spindle speed of 47.1 m/min, and a cutting fluid pressure of 2.4 MPa. Under these conditions, a chip evacuation coefficient of 3.2951 and a chip volume ratio of 3.3345 were achieved. The resulting chips predominantly exhibited a C-shaped morphology, accompanied by smooth and efficient evacuation.

Indexed as

gun drillingoxygen-free copperresponse surface methodology-genetic algorithm

Identifiers

PMID40870230
PMCPMC12387514

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