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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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1. Introduction space and many other weight-critical applications. According to the report in [Qui01], nearly half of the Boeing 787 airplane is comprised of CFRP and other composites, which saves on average 20% of the total weight compared with conventional aluminium designs. Based on themarket report publishedin 2012 [Cas12], theglobal CFRP mar- ket size is forecast to have an annual growth rate of 17%, reaching 118,600tonneswithanestimatedvalueof7.3billionUSdollars (USD) by2017. There is no doubt that CFRP will become one of the most important and demanding materials in the near future. However, there are also challenges from different aspects, such as high costs and manufactur- ing complexities. For most industrial areas, cost reduction is a key priority that has to be concerned during the manufacture process. Al- though the price of carbon fiber material has been decreasing during the last few years (about 20 USD per kg in 2014 [cfr]), curing of one CFRP workpiece is still expensive. The most common way to cure a CFRP workpiece is to layer the uncured prepreg into a mould, put a vacuum bag to the mould and cure it in an autoclave. The curing process within the autoclave takes a long time and the corresponding energy consumption is large, which leads to the increase of the total cost. Besides the autoclave curing, there are a number of other cur- ingmethods. Theirmainadvantagesanddrawbacksareshowninthe followingtable 1.1. Comparedwithothercuringmethodsintable 1.1,microwaveheating hasahigherheating efficiencyandlowercuringcost. Inaddition,mi- crowave heating also has a large penetration depth, which means the heat can be directly generated deeply inside the CFRP workpiece and rapidcuring ispossible for thickcompositematerials. However, there are still a number of problems that limit the implementation of mi- crowaveheatinginthepracticalCFRPcuringprocess,suchasthermal insulation, temperature measurement, and most important, tempera- turecontrolproblems. Duringtheheatingprocess, thetemperatureoftheentireheatedwork- pieceshouldbecontrolledandheldinapredefinedrange. Inaddition, the overall temperature homogeneity of the workpiece should also be sustained in a certain level, to avoid any local overheating (hot spots) [RMS96] or thermal runaway [Kri92]. According to investigations in 4
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Title
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Author
Yiming Sun
Publisher
KIT Scientific Publishing
Location
Karlsruhe
Date
2016
Language
English
License
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Size
14.8 x 21.0 cm
Pages
260
Keywords
Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources