Citation: Xue, W.; Zhao, C.; Fu, W.;
Du, J.; Yao, Y. On-Machine Detection
of Sub-Microscale Defects in
Diamond Tool Grinding during the
Manufacturing Process Based on
DToolnet. Sensors 2022, 22, 2426.
https://doi.org/10.3390/s22072426
Academic Editor: Nico P. Avdelidis
Received: 24 January 2022
Accepted: 14 March 2022
Published: 22 March 2022
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Article
On-Machine Detection of Sub-Microscale Defects in
Diamond Tool Grinding during the Manufacturing Process
Based on DToolnet
Wen Xue, Chenyang Zhao *, Wenpeng Fu, Jianjun Du and Yingxue Yao
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen),
Shenzhen 518055, China; xuewencool@163.com (W.X.); 20s153227@stu.hit.edu.cn (W.F.); jjdu@hit.edu.cn (J.D.);
yxyao@hit.edu.cn (Y.Y.)
* Correspondence: zhaochenyang@hit.edu.cn
Abstract:
Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources
and improve efficiency in the process of machining parts, has developed many mature methods.
However, TCM during the production of cutting tools is less studied and has different properties.
The scale of the defects in the tool production process is tiny, generally between 10
µ
m and 100
µ
m for
diamond tools. There are also very few samples with defects produced by the diamond tool grinding
process, with only about 600 pictures. Among the many TCM methods, the direct inspection method
using machine vision has the advantage of obtaining diamond tool information on-machine at a
low cost and with high efficiency, and the method is accurate enough to meet the requirements of
this task. Considering the specific, above problems, to analyze the images acquired by the vision
system, a neural network model that is suitable for defect detection in diamond tool grinding is
proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the
small-sample diamond tool features to intuitively and quickly detect defects in their production. The
improvement of the feature extraction network, the optimization of the target recognition network,
and the adjustment of the parameters during the network training process are performed in DToolnet.
The imaging system and related mechanical structures for TCM are also constructed. A series of
validation experiments is carried out and the experiment results show that DToolnet can achieve an
89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms
other classical network models. Lastly, the DToolnet parameters are optimized, improving the
accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the
manufacturing process.
Keywords: tool condition monitoring; diamond tools; small target detection; neural networks
1. Introduction
Cutting tools are indispensable for machining, especially in turning, milling, drilling,
and other subtractive manufacturing processes. The timely acquisition of tool condition
information is very important for production and processing. The cost components of the
machined products show that tool wear accounts for 2–30% of the total cost [
1
], which
does not take into account the production of defective products due to excessive tool wear.
Production lines that do not incorporate a tool condition monitoring (TCM) system cost
more to operate. In terms of production time, the downtime due to accidents caused by
tool wear accounts for 20% of the whole production time [
2
]. Therefore, TCM has always
been an important topic and issue of focus in the machining field. TCM generally means
the detection of the tool’s condition or the prediction of its service life by collecting a signal
from the tool during or after machining, such as the current signal, the power signal, the
cutting force signal, the vibration signal, tool surface image information, etc. [3].
Sensors 2022, 22, 2426. https://doi.org/10.3390/s22072426 https://www.mdpi.com/journal/sensors
