Purpose

The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.

Design/methodology/approach

In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management.

Findings

In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise.

Research limitations/implications

The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models.

Originality/value

In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.

中文翻译：

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遗传编程在刀具磨损识别中的应用

目的

本文的目的是建立与刀具磨损相关的回归模型，该回归模型用于识别刀具磨损的状态。

设计/方法/方法

在本文中，遗传规划（GP）最初用于解决符号回归问题，用于构建与刀具磨损相关的回归模型，具有较强的回归能力。GP在遗传操作和加权矩阵方面得到改进。GP的性能在2010 prognostics Health management提供的工具振动、力和声发射数据中得到验证。

发现

因此，GP 发现的回归模型可以识别刀具磨损状态。与支持向量回归、多项式回归等其他回归算法相比，GP的识别更加精准。

研究限制/影响

本文建立的回归模型只能用刀具的当前信号来评估当前的磨损状态。它无法预测或估计当前状态之后的刀具磨损。此外，模型的泛化也有一定的局限性。模型的性能只是在相同类型工具和相同工作条件下的信号中得到证明，不同工具和不同工作条件可能对模型的性能产生影响。

原创性/价值

本研究发现的回归模型能够准确识别刀具磨损状态，应用于其他刀具的模型识别性能也非常出色。它可以提供有关刀具健康状况的重要信息，从而可以及时更换或修理刀具，避免刀具损坏造成的损失。