Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

Section: Types Of Sensors and Variables Measuredmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

“…The result shows that the method can effectively detect and evaluate small defects on the spiral cutting edges with a high detection accuracy. Brili et al [ 38 ] used infrared imaging technology to obtain the state of the tool’s wear, and combined computer vision and deep learning methods to determine the tool’s state. Ivan Kuric et al [ 39 ] used the improved AlexNet to achieve the automatic target detection of small samples.…”

Section: Introductionmentioning

confidence: 99%

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

Xue

Zhao

et al. 2022

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 μm and 100 μm 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.

“…In addition, at present, various sensors have been achieved that allow the monitoring of cutting processes. Thus, a series of sensors have been developed that allow temperature monitoring during cutting processes, but also these sensors have not allowed to obtain the best results in practice due to the fact that temperature measurements during cutting are not of best accuracy and, thus, a series of errors may occur [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Cutting Tools Used in the Processing of Aluminum Alloys

Dobrotă

Racz

Oleksik

et al. 2021

The processing of aluminum alloys in optimal conditions is a problem that has not yet been fully resolved. The research carried out so far has proposed various intelligent tools, but which cannot be used in the presence of cooling-lubricating fluids. The objective of the research carried out in the paper was to design intelligent tools that would allow a control of the vibrations of the tool tip and to determine a better roughness of the processed surfaces. The designed intelligent tools can be used successfully in the processing of aluminum alloys, not being sensitive to coolants-lubricants. In the research, the processing by longitudinal turning of a semi-finished product with a diameter Ø = 55 mm of aluminum alloy A2024-T3510 was considered. Two constructive variants of smart tools were designed, realized, and used, and the obtained results were compared with those registered for the tools in the classic constructive variant. The analysis of vibrations that occur during the cutting process was performed using the following methods: Fast Fourier Transform (FFT); Short-Time Fourier-Transformation (STFT); the analysis of signal of vibrations. A vibration analysis was also performed by modeling using the Finite Element Method (FEM). In the last part of the research, an analysis of the roughness of the processed surfaces, was carried out and a series of diagrams were drawn regarding curved profiles; filtered profiles; Abbott–Firestone curve. Research has shown that the use of smart tools in the proposed construction variants is a solution that can be used in very good conditions for processing aluminum alloys, in the presence of cooling-lubrication fluids.