scholarly journals Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System

2018 ◽  
Vol 12 (3) ◽  
pp. 282-289 ◽  
Author(s):  
Jonny Herwan ◽  
◽  
Seisuke Kano ◽  
Ryabov Oleg ◽  
Hiroyuki Sawada ◽  
...  
Keyword(s):  
Tool Wear ◽  
Manufacturing System ◽  
Essential Feature ◽  
Vibration Sensor ◽  
Smart Manufacturing ◽  
Cnc Turning ◽  
Tool Condition ◽  
Tool Monitoring ◽  
Tool Shank ◽  
Sensor Positions

Tool condition monitoring, such as tool wear and breakage, is an essential feature in smart manufacturing system. One of most potential sensors that can be used in tool monitoring is vibration sensor, which usually assembled at tool shank. However, in case of CNC turning with rotating tool turret, it is impossible to assemble the vibration sensor at the tool shank because wire of the sensor will be damaged when the turret rotated. This paper is addressed to compare thoroughly alternative sensor positions. Ten sensor positions including tool shank, as a reference, are investigated. The signals from three types of cutting, namely; normal cutting, abnormal cutting with tool wear and abnormal cutting when tool breakage occurred, are investigated. Based on the magnitude of the output signals and their capability to predict tool wear and breakage, a suggestion on vibration sensor positions is proposed.

Morphological Filtering Algorithm for Estimation of Tool Wear in CNC Milling Machining

2011 ◽  
Vol 697-698 ◽  
pp. 566-569
Author(s):  
Qian Ning ◽  
Tai Yong Wang
Keyword(s):  
Tool Wear ◽  
Manufacturing System ◽  
Systematic Approach ◽  
Vibration Signal ◽  
Cnc Milling ◽  
Tool Condition ◽  
Morphological Filtering ◽  
Force Signal ◽  
Tool Wear Estimation ◽  
Feature Values

Estimation of tool condition has very important meaning to improve the product quality, continuous machining ability and reliability of the manufacturing system. Based on mathematical morphology, a systematic approach is developed to implement online estimation of tool wear in this paper. As the nonlinear filter, morphological filter is selected to reduce the higher frequency noises before feature values extraction. The feature vector consists of original characteristics of vibration signal and cutting force signal. Then, they are input into SVM for training and testing. Experiments show that this method can achieve tool wear estimation effectively.

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

Monitoring of Cutting Conditions with Dry Cutting on CNC Turning Machine

2010 ◽  
Vol 443 ◽  
pp. 382-387 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Suthas Ratanakuakangwan
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Forces ◽  
Cutting Temperature ◽  
Cutting Conditions ◽  
Cutting Condition ◽  
Nose Radius ◽  
Dry Cutting ◽  
Cnc Turning ◽  
Coated Carbide

This paper presents the additional work of the previous research in order to verify the previously obtained cutting condition by using the different cutting tool geometries. The effects of the cutting conditions with the dry cutting are monitored to obtain the proper cutting condition for the plain carbon steel with the coated carbide tool based on the consideration of the surface roughness and the tool life. The dynamometer is employed and installed on the turret of CNC turning machine to measure the in-process cutting forces. The in-process cutting forces are used to analyze the cutting temperature, the tool wear and the surface roughness. The experimentally obtained results show that the surface roughness and the tool wear can be well explained by the in-process cutting forces. Referring to the criteria, the experimentally obtained proper cutting condition is the same with the previous research except the rake angle and the tool nose radius.

In-Process Monitoring and Estimation of Tool Wear on CNC Turning by Applying Multi-Sensor with Back Propagation Technique

2011 ◽  
Vol 291-294 ◽  
pp. 3036-3043 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Channarong Rungruang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Monitoring System ◽  
Process Monitoring ◽  
Back Propagation ◽  
Force Sensor ◽  
Accelerometer Sensor ◽  
Cnc Turning ◽  
Propagation Technique ◽  
Cnc Turning Process

The aim of this research is to propose and develop the in-process monitoring system of the tool wear for the carbon steel (S45C) in CNC turning process by utilizing the multi-sensor which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The progress of the tool wear results in the larger cutting force, the higher amplitude of the acceleration signal, and the higher power spectrum densities of sound and acoustic emission signals. Hence, their signals have been integrated via the neural network with the back propagation technique to monitor the tool wear. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level with the higher accuracy and reliability.

Model of Smart Manufacturing System

2019 ◽  
pp. 356-362
Author(s):  
Maria Usova ◽  
Sergey Chuprov ◽  
Ilya Viksnin ◽  
Ruslan Gataullin ◽  
Antonina Komarova ◽  
...  
Keyword(s):  
Manufacturing System ◽  
Smart Manufacturing

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

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