Experimental techniques for tool temperature measurement in metal cutting: a review
Year: 2026
Type: review
Study design: Systematic Review
Abstract: This systematic review evaluates experimental tool-temperature measurement techniques reported between 2020 and 2025 for metal cutting applications. Literature was systematically collected from Scopus, Web of Science, ScienceDirect, and IEEE Xplore using predefined keywords related to tool temperature measurement, machining, thermocouples, infrared thermography, fibre-optic sensing, MEMS sensors, and thermal monitoring. Studies were screened using predefined inclusion and exclusion criteria based on relevance, methodological completeness, experimental validation, and applicability to metal cutting temperature measurement, resulting in a final dataset of 111 reviewed publications. Studies were screened for relevance, methodological completeness, and applicability to tool-temperature measurement in metal cutting. Heat generation at the tool–chip and tool–workpiece interfaces during metal cutting critically governs tool wear, surface integrity, and overall process efficiency. Temperatures at these interfaces can exceed 1000 °C with spatial gradients reaching 100–300 K/mm near the cutting edge, driving adhesion, diffusion, and oxidation wear mechanisms. Although finite element and analytical thermal models offer valuable process insights, experimental measurement remains indispensable for model validation, real-time monitoring, and adaptive process control. TFTCs and MEMS-integrated sensors offer superior spatial resolution (down to 30 μm from the cutting edge) and sub-microsecond response times, while IR thermography remains the dominant non-contact approach despite persistent emissivity calibration challenges. No single technique universally satisfies all requirements for spatial resolution, temporal response, robustness, and cost-effectiveness across all machining scenarios. The review identifies three critical research gaps: the absence of standardised calibration protocols applicable across techniques, insufficient sensor durability data under prolonged industrial conditions, and limited integration of experimental thermal data with AI-driven adaptive control systems. Future research directions converge on hybrid multi-modal sensing and physics-informed machine learning for thermal field reconstruction. This review provides a consolidated, critically evaluated reference for researchers and practitioners selecting or developing thermal measurement solutions for metal cutting applications.
Source: Manufacturing Review
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $791
OpenAlex ID: W7214354134