Automated Monitoring of Manufacturing Processes, Part 2: Applications

1995 ◽  
Vol 117 (2) ◽  
pp. 133-141 ◽  
Author(s):  
R. Du ◽  
M. A. Elbestawi ◽  
S. M. Wu
Keyword(s):  
Condition Monitoring ◽  
Rate Sensitivity ◽  
Index Selection ◽  
Manufacturing Processes ◽  
Test Results ◽  
Monitoring Methods ◽  
Decision Under Uncertainty ◽  
Automated Monitoring ◽  
Tool Condition ◽  
Monitoring Index

In Part 2 of this paper, three applications of automated monitoring of manufacturing processes are presented to demonstrate the use of monitoring methods discussed in Part 1 of the paper. These applications are: (1) tool condition monitoring in turning, (2) machining condition monitoring in tapping, and (3) metallographic condition monitoring in arc welding. For each application, a background review, monitoring index selection and experimental setup are first presented. Then, monitoring methods discussed in Part 1 of the paper were applied and the test results are investigated. Discussions of the monitoring success rate, sensitivity, robustness, monitoring index selection, and decision under uncertainty are also included.

Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

1995 ◽  
Vol 117 (2) ◽  
pp. 121-132 ◽  
Author(s):  
R. Du ◽  
M. A. Elbestawi ◽  
S. M. Wu
Keyword(s):  
Spectral Analysis ◽  
System Modeling ◽  
Process Condition ◽  
Process Conditions ◽  
Manufacturing Processes ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Automated Monitoring ◽  
Two Phases ◽  
Analysis System

This paper presents a systematic study of various monitoring methods suitable for automated monitoring of manufacturing processes. In general, monitoring is composed of two phases: learning and classification. In the learning phase, the key issue is to establish the relationship between monitoring indices (selected signature features) and the process conditions. Based on this relationship and the current sensor signals, the process condition is then estimated in the classification phase. The monitoring methods discussed in this paper include pattern recognition, fuzzy systems, decision trees, expert systems and neural networks. A brief review of signal processing techniques commonly used in monitoring, such as statistical analysis, spectral analysis, system modeling, bi-spectral analysis and time-frequency distribution, is also included.

Quality and Inspection of Machining Operations: Tool Condition Monitoring

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
John T. Roth ◽  
Dragan Djurdjanovic ◽  
Xiaoping Yang ◽  
Laine Mears ◽  
Thomas Kurfess
Keyword(s):  
Condition Monitoring ◽  
Lower Cost ◽  
Production Systems ◽  
Tool Condition Monitoring ◽  
Rapid Expansion ◽  
Condition Based Maintenance ◽  
Manufacturing Processes ◽  
Tool Condition ◽  
Cutting Processes ◽  
Machining Operations

Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.

scholarly journals State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 335
Author(s):  
Wei Dai ◽  
Kui Liang ◽  
Bin Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Manufacturing Processes ◽  
State Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

scholarly journals Review of Tool Condition Monitoring Methods

2018 ◽  
Vol 179 (37) ◽  
pp. 29-32
Author(s):  
Ramesh Visariya ◽  
Ronak Ruparel ◽  
Rahul Yadav
Keyword(s):  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Monitoring Methods ◽  
Tool Condition

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.

scholarly journals Applicability of Tool Condition Monitoring Methods Used for Conventional Milling in Micromilling: A Comparative Review

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soumen Mandal
Keyword(s):  
Condition Monitoring ◽  
Cutting Tools ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Research Trend ◽  
Monitoring Methods ◽  
Nose Radius ◽  
Continuous Contact ◽  
Tool Condition ◽  
Comparative Review

Micromilling is a contact based material removal process in which a rotating tool with nose radius in microns is fed over a stationary workpiece. In the process small amount of material gets chipped off from the workpiece. Due to continuous contact between tool and workpiece significant damage occurs to the cutting tools. Mitigating tool damage to make micromilling systems more reliable for batch production is the current research trend. In macroscale or conventional milling process a number of methods have been proposed for tool condition monitoring. Few of them have been applied for micromilling. This paper reviews different methods proposed and used in last two decades for monitoring the condition of micromilling tools. Applicability of tool condition monitoring methods used in conventional milling has been compared with the similar ones proposed for micromilling. Further, the challenges and opportunities on the applicability issues have been discussed.

Sign in / Sign up

Export Citation Format

Share Document