Radiation Thermometry for Plastics Processing

2007 ◽  
pp. 1019-1043
Author(s):  
William Barron
Keyword(s):  
Radiation Thermometry ◽  
Plastics Processing

Detection method of combustion oscillation characteristics under strong noise background

2020 ◽  
Vol 21 (6) ◽  
pp. 612
Author(s):  
Yunkun Wei ◽  
Tianhong Zhang ◽  
Zhonglin Lin ◽  
Qi Xie ◽  
Yan Zhang
Keyword(s):  
Radiation Thermometry ◽  
Hydroxyl Group ◽  
Interference Signal ◽  
Engine Vibration ◽  
Combustion Technology ◽  
Lean Fuel ◽  
Combustion Oscillation ◽  
Aero Engines ◽  
Temperature Measurement System ◽  
Multispectral Radiation Thermometry

After the lean fuel premixed combustion technology is applied to aero engines, severe combustion oscillations will be cased and led to hidden safety hazards such as engine vibration, further energy waste and other problems. Therefore, it is increasingly important to actively control combustion oscillations. In this paper, a multispectral radiation thermometry (MRT) is used to analyze the hydroxyl group, which is a measurable research object in the combustion chamber of an aero engine, and to fit the functional relationship between the radiation intensity ratio and the temperature in different bands. The theoretical value of the error is <2%. At the same time, in order to solve the problem of weak detection signal and excessive interference signal, an improved frequency domain filtering method based on fast Fourier transform is designed. Besides, the FPGA platform is used to ensure the real-time performance of the temperature measurement system, and simulations and experiments are performed. An oscillating signal with an oscillation frequency of 315 Hz is obtained on the established test platform, and the error is only 1.42%.

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

Ambient temperature-independent dual-band mid-infrared radiation thermometry

2016 ◽  
Vol 55 (9) ◽  
pp. 2169 ◽  
Author(s):  
You Lü ◽  
Xin He ◽  
Zhong-Hui Wei ◽  
Zhi-Yuan Sun ◽  
Song-Tao Chang
Keyword(s):  
Ambient Temperature ◽  
Infrared Radiation ◽  
Radiation Thermometry ◽  
Dual Band ◽  
Mid Infrared
Sign in / Sign up

Export Citation Format

Share Document