Kamat Pooja, Kumar Satish, Kotecha Ketan
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.
Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharashtra, India.
MethodsX. 2024 Sep 19;13:102965. doi: 10.1016/j.mex.2024.102965. eCollection 2024 Dec.
Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.
铣削刀具的可用性及其使用寿命估计对于铣削操作的优化、可靠性和成本降低至关重要。这项工作展示了DeepTool，这是一个基于深度学习的系统，可预测刀具的使用寿命并检测其磨损的开始。DeepTool展示了一个全面的特征提取过程，以及一个自收集的传感器数据集，该数据集来自在不同切削设置下进行的铣削测试，以从传感器信号中提取相关信息。本研究的主要贡献包括：
自收集数据集：利用广泛的自收集数据集在铣削过程中记录精确的传感器信号。
先进的预测建模：采用混合自动编码器-长短期记忆网络（autoencoder-LSTM）和编码器-解码器长短期记忆网络（encoder-decoder LSTM）模型来估计刀具磨损的开始，并以超过95%的决定系数（R2）准确率预测其剩余使用寿命。
全面的特征提取：采用一种从收集到的传感器数据中进行有效特征提取的技术，强调与刀具磨损相关的时域和频域方面。
