Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The growing demand for precision and reliability in
aerospace manufacturing requires intelligent, automated
solutions for tool condition monitoring. This project
introduces a deep learning–based system for real-time
detection and prediction of tool wear during high-speed
drilling of Carbon Fiber Reinforced Polymer (CFRP)
components. At its core is a hybrid CNN-LSTM model
that captures both spatial and temporal patterns from
multi-sensor data streams, including vibration, acoustic
emission, and spindle current. The system operates
non-intrusively, continuously analyzing drilling signals
without disrupting production workflows. By mapping
sensor signatures to tool wear stages, it enables
predictive insights that support optimized tool
replacement schedules, extended tool life, and improved
safety standards. The proposed framework enhances
production efficiency through automated, scalable, and
intelligent wear monitoring, tailored for aerospace
industry applications.
"Automated Tool Wear Detection for Aircraft Industry Applications ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a472-a476, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605056.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator