A joint data-driven process monitoring method using knowledge propagation based on manifold clustering

2020 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Chuanfang Zhang ◽  
Kaixiang Peng ◽  
Jie Dong
Keyword(s):  
Process Monitoring ◽  
Data Driven ◽  
Monitoring Method ◽  
Knowledge Propagation ◽  
Manifold Clustering

Körperschallemission in CFK und Fräswerkzeugen/Acoustic emission in CFRP and milling tools – Analysis of emitted acoustic emission in analogous experiments

2021 ◽  
Vol 111 (07-08) ◽  
pp. 495-500
Author(s):  
Eckart Uhlmann ◽  
Tobias Holznagel ◽  
Sebastian Ospina Mora
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Process Monitoring ◽  
Detailed Examination ◽  
Data Driven ◽  
Material Damage ◽  
Emission Measurement ◽  
Wear Rates ◽  
Milling Tools ◽  
Acoustic Emission Measurement

Herausforderungen bei der spanenden Bearbeitung von CFK sind unerwünschte Materialschädigungen im Bauteil sowie hohe Werkzeugverschleißraten. Für diese Arbeit wurden zerstörende Analogversuche an CFK und diamantbeschichteten Hartmetallproben durchgeführt und die Körperschallmesssignale analysiert. Die eingehende Untersuchung der Messwerte aus den Analogversuchen kann zu einer datengetriebenen Parametrierung einer körperschallbasierten Prozessüberwachung für die Fräsbearbeitung von CFK beitragen.   Challenges in machining CFRP are unwanted material damage in the component and high tool wear rates. For this work, destructive analogy experiments were carried out on CFRP and diamond-coated milling tools while analyzing the acoustic emission measurement signals. The detailed examination of the measured values from the analogy experiment can contribute to a data-driven parameterization of an acoustic emission-based process monitoring for the milling of CFRP.

scholarly journals A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sheng Hu ◽  
Shuanjun Song ◽  
Wenhui Liu
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Method ◽  
Cloud Model ◽  
Data Driven ◽  
Monitoring Method ◽  
Control Approach ◽  
Acceptable Method ◽  
And Performance ◽  
Model Similarity

Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.

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