科创中国

原文链接：万方

• 摘要：

  The mass production of printed electronics can be achieved by roll-to-roll(R2R) printing system, so highly accurate web tension is required that can minimize the register error and keep the thickness and roughness of printed devices in limits. The web tension of a R2R system is regulated by the use of integrated load cells and active dancer system for printed electronics applications using decentralized multi-input-single-output(MISO) regularized variable learning rate backpropagation artificial neural networks. The active dancer system is used before printing system to reduce disturbances in the web tension of process span. The classical PID control result in tension spikes with the change in roll diameter of winder and unwinder rolls. The presence of dancer in R2R system shows that improved web tension control in printing span and the web tension can be enhanced from 3.75 N to 4.75 N. The overshoot of system is less than ±2.5 N and steady state error is within ±1 N where load cells have a signal noise of ±0.7 N. The integration of load cells and active dancer with self-adapting neural network control provide a solution to the web tension control of multispan roll-to-roll system.

• 关键词：

  roll-to-roll(R2R)system multispan printed electronics active dancer load cell artificial neural networks tension control multi-input-single-output(MISO)

• 作者单位：

  Department of Mechatronics Engineering, Jeju National University, Jeju, 690-756 Korea

• 基金项目：

  Basic Science Research Program through the National Research Foundation of Korea%Ministry of Education,Science and Technology,Korea(Grant2010-0026163)%Strategy Technology Development Project,Ministry of Knowledge Economy,Korea(Grant10032149)

• 来源期刊：

  中国机械工程学报

• 年，卷(期)：

  201427002