Abstract
An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.
Highlights
In the manufacturing industry, the milling process plays a crucial role because of its flexibility in production [1]
Real-time condition monitoring mainly performs the diagnosis by uninterrupted monitoring via software with the help of different sensors
Predictive maintenance 4.0 for Remaining Useful Life (RUL) estimation focuses on the prognostic approach rather than just diagnosis
Summary
The milling process plays a crucial role because of its flexibility in production [1]. The overall variable cost increases (cost of consumables decreases but the cost of maintenance increases) These losses continue until the plant gets back into working condition. In such a case, the cost of a severe outage failure cause due to unplanned downtime can be much more than the profit made in the same duration of time. Proper estimation of useful life is necessary to predict the life of equipment cost-effectively. The main objective of the prognostic is to estimate the RUL of the system by providing the machine's past operation status and current condition to predict the useful life before failure occurs. On average, up to 20% of machine downtime occurs due to the failure of the cutting tool. The accurate system monitoring improves productivity from 10-40%, with cost-saving up to 40% [18], [19]
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