Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures

2019 ◽  
Vol 61 (3) ◽  
pp. 282-288 ◽  
Author(s):  
Thangamuthu Mohanraj ◽  
Subramaniam Shankar ◽  
Rathanasamy Rajasekar ◽  
Ramasamy Deivasigamani ◽  
Pallakkattur Muthusamy Arunkumar
Keyword(s):  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Cutting Fluids ◽  
Tool Condition ◽  
Vibration Signatures

Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625

2019 ◽  
Vol 18 (04) ◽  
pp. 563-581 ◽  
Author(s):  
S. Shankar ◽  
T. Mohanraj ◽  
A. Pramanik
Keyword(s):  
Condition Monitoring ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Mean Square ◽  
Mean Values ◽  
Tool Condition ◽  
Vibration Signatures

This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.

scholarly journals A Review of Sensor System and Application in Milling Process for Tool Condition Monitoring

2014 ◽  
Vol 7 (10) ◽  
pp. 2083-2097 ◽  
Author(s):  
Muhammad Rizal ◽  
Jaharah A. Ghani ◽  
Mohd Zaki Nuawi ◽  
Che Hassan Che Haron
Keyword(s):  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Sensor System ◽  
Tool Condition

scholarly journals A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

2018 ◽  
Author(s):  
Md. Shafiul Alam ◽  
Maryam Aramesh ◽  
Stephen Veldhuis
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
Cutting Tool ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Tool Wear Monitoring ◽  
Tool Condition ◽  
Wear Monitoring

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

scholarly journals Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

2021 ◽  
Author(s):  
Kui Liang ◽  
Wei Dai ◽  
Tingting Huang ◽  
Zhiyuan Lu
Keyword(s):  
Pattern Recognition ◽  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Monitoring Methods ◽  
Model Framework ◽  
Recognition Model ◽  
Practical Applications ◽  
Transfer Path ◽  
Tool Condition

Abstract In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

Sign in / Sign up

Export Citation Format

Share Document