Key Takeaways & Executive Findings
- •• The proposed image-based method robustly handles uneven illumination and arbitrary tool orientation in micro-milling tool wear monitoring. • The algorithm restores lost pixels during segmentation, accurately extracting wear areas despite challenging imaging conditions. • The method exhibits low computational complexity and fast execution, significantly reducing in-situ detection time. • This approach enhances tool condition monitoring, potentially reducing downtime and production costs in micro-milling.
Abstract
In micro milling machining, tool wear directly affects workpiece quality and accuracy, making effective tool wear monitoring a key factor in ensuring product integrity. The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions. However, micro milling tools have non-flat flanks, thin coatings can peel off, and spindle orientation is uncertain during downtime. These factors result in low pixel values, uneven illumination, and arbitrary tool position. To address this, we propose an image-based tool wear monitoring method. It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas. Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images, effectively addressing the effects of illumination and spindle orientation. Additionally, the algorithm has low complexity, fast execution time, and significantly reduces the detection time in situ.
1. Introduction
Micro-milling is highly regarded as a versatile machining process for producing micro components and features due to its broad material selection, true 3D microgeometry machining capability, exceptional precision, and cost-effectiveness [1]. However, during milling processes, tool wear is accelerated due to the high spindle speed of the machine and the contact between the cutting portion of the tool and the surface of the workpiece [2]. Severe wear can lead to tool breakage, fracture, and even damage to the machine. Studies have shown that tool wear and breakage can account for up to 20% of the total production time in machine downtime [3]. An accurate and reliable Tool Condition Monitoring (TCM) system can reduce costs by 10–40% through minimizing downtime and maximizing tool life [4]. Therefore, tool condition monitoring is crucial in timely decision-making for tool replacement to ensure surface accuracy of machined parts and reduce production costs [5].
Compared to traditional milling, micro-milling is a reduction in the machining scale. In order to ensure that the micro milling cutter has sufficient strength, the feed rate during the milling process must be small enough, resulting in a decrease in instantaneous cutting thickness. However, studies by Biermann et al. [6] have shown that a small ratio of instantaneous cutting thickness to tool edge radius can induce a size effect, increasing milling forces and accelerating tool wear. Moreover, the high spindle speeds and small diameters in micro-milling result in significant tool runouts. Typically, tool runout in micro-milling is tens to several tens of times greater than in traditional milling [7]. Excessive tool runout can cause intermittent cutting during milling, leading to rapid tool wear, failure, decreased machining precision, and lower workpiece quality [8]. In severe cases, it can even lead to tool breakage, damaging the machining system and posing safety hazards. Efficient and accurate monitoring methods for tracking tool wear status are crucial research directions driving the advancement of micro-milling technology.
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Yuan Li, Geok Soon Hong, Kunpeng Zhu (2025). Robust and Fast Monitoring Method of Micro-Milling Tool Wear Using Image Processing. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01225-7
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Frequently Asked Questions
What is the main challenge in micro-milling tool wear monitoring using machine vision?
The main challenges include non-flat flanks, peeling coatings, and uncertain spindle orientation, which lead to low pixel values, uneven illumination, and arbitrary tool position, making accurate wear area extraction difficult.
How does the proposed method address uneven illumination?
The method combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation, ensuring accurate extraction of wear areas.
What are the advantages of the proposed tool wear monitoring method?
The method exhibits high robustness to challenging imaging conditions, low computational complexity, fast execution time, and significantly reduces detection time in situ.
Why is tool condition monitoring important in micro-milling?
Tool wear directly affects workpiece quality and accuracy, and severe wear can lead to tool breakage and machine damage. Effective monitoring can reduce downtime and production costs by 10-40%.
What is the significance of the experimental results?
The experimental results demonstrate that the proposed algorithm effectively addresses the effects of illumination and spindle orientation, showing high robustness and efficiency, making it suitable for real-time industrial applications.
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