MATEC Web Conf.
Volume 185, 20182018 The 3rd International Conference on Precision Machinery and Manufacturing Technology (ICPMMT 2018)
|Number of page(s)||8|
|Published online||31 July 2018|
Design of deep learning on intelligent levelling system for industry 4.0 technology
Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Corresponding author : firstname.lastname@example.org
Sheet metal is widely used in the industry for metal forming purposes, such as metal stamping and laser cutting as shown. It is often winded and stored in a coil form in order for better transportation. In the recent years, industry 4.0 has been a widely discussed topic in terms of industry manufacturing solutions, the manufacturing is required to be more flexible, efficient and also require more customization. In conventional coil levelling system, the machine settings are often tuned by the experienced technicians with many years of experiences. However, as industry 4.0 focused on information process through real objects, it is required to digitize the experience through deep learning method. Therefore, it is required to be adapted through data information transfer between real world and machines, or even machines to machines. In addition, the data information is often processed and analysed through computers which are often desired to mimic the operations of the experienced machine technicians through machine learning or deep learning methods. This paper is aimed to describe and develop the deep learning algorithm with application based on coil levelling system. Finally, through this paper, design of the deep learning algorithm with application based on coil levelling system is verified.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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