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使用最小数量的滑进行Inconel 718的可持续加工:基于人工智能的工艺建模

Muhammad Umar Farooq¹, Raman Kumar², Anamta Khan³

¹School of Mechanical Engineering, University of Leeds, LS2 9JT, Leeds, UK.

|February 17, 2025

概括

本研究介绍了一种人工智能策略,用于预测和减少数控加工的能耗. 决策树模型准确地预测了最小量滑 (MQL) 和纳米流体-MQL系统的功耗.

科学领域:

制造业工程制造工程
人工智能的人工智能
可持续制造可持续制造可持续制造

背景情况:

工业面临着减少碳排放和能源消耗的压力.
数控加工的能源需求往往与生产批量大小有关.
对于加工能源需求的现有分析模型缺乏整合能力.

研究的目的:

开发和实施基于人工智能的CNC加工功率降低策略.
评估机器学习算法,用于预测加工过程中的功耗.
为可持续制造业优化能源意识的战略.

主要方法:

在Inconel 718材料上实施基于AI的减电策略.
研究四个控制参数:切割速度,料速度,切割深度和流量.
四种机器学习算法的比较 (K-最近邻居,高斯回归,决策树,物流回归) 用于电力消耗预测.
使用最小量滑 (MQL) 和纳米流体-MQL (NF-MQL) 子系统进行评估.

主要成果:

决策树算法在预测电力消耗 (Pc) 方面表现出最高的准确性.
决策树模型的最佳性能是在2.0的最大深度下实现的.
决策树模型的结果是Pc MQL的R平方值为0.915,Pc NF-MQL的R平方值为0.931.

结论:

决策树模型是CNC加工功耗的最有效预测器.
由人工智能驱动的策略可以显著促进制造业的能源减少.
节能加工工艺对于实现可持续的工业实践至关重要.

关键词:

在Inconel中使用.一个MQL,一个MQL.机器学习是机器学习.磨削磨削的方法磨削电力消耗电力消耗电力消耗

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