摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据新闻报道NewsRx新闻记者在巴西伊塔朱巴进行的一项研究表明，“干翻减少了环境污染。”与切削液相关的imp行为和成本，但由于切削液的影响，它对刀具寿命的优化提出了挑战产生的热量。本研究评估了机器学习模型预测刀具寿命(T)通过分析切削速度（Vc）、进给速度（f）和切削深度（ap）对AISI H13钢进行干式车削。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators discuss new findings in Machine Learning. According to news reportingoriginating in Itajuba, Brazil, by NewsRx journalists, research stated, “Dry turning reduces the environmentalimp act and costs associated with cutting fluids, but it challenges the optimization of tool life dueto the generated heat. This study evaluated machine learning m odels to predict tool life (T) during thedry turning of AISI H13 steel by analy zing cutting speed (Vc), feed rate (f), and depth of cut (ap).”
NETL
NSTL