TY - GEN
T1 - Concept of Easy-to-use Versatile Artificial Intelligence in Industrial Small & Medium-sized Enterprises
AU - Hansen, Emil Blixt
AU - Iftikhar, Nadeem
AU - Bøgh, Simon
N1 - Publisher Copyright: © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.
PY - 2020
Y1 - 2020
N2 - In this paper, the concept of what we call AI-Box is presented. This concept is targeting small and medium-sized enterprises within the manufacturing industry sector. The AI-Box aims to bring technologies from Industry 4.0 to them, with the use of easy-to-use and versatile implementation. Preliminary experiments have been conducted at Aalborg University and at an industrial partner to solve vision tasks, which would be too expensive with conventional vision techniques. Moreover, three different convolutional neural networks were tested to find the best-suited architecture. The three networks tested were the simple AlexNet, the complex ResNeXt, and small and complex SqueezeNet. Our results show that it is possible to solve the classification problem in a few epochs. Furthermore, with the use of augmented data, the performance can be improved. Our preliminary results also showed that the simpler convolutional neural network architecture from AlexNet yields a better result when classifying simple data.
AB - In this paper, the concept of what we call AI-Box is presented. This concept is targeting small and medium-sized enterprises within the manufacturing industry sector. The AI-Box aims to bring technologies from Industry 4.0 to them, with the use of easy-to-use and versatile implementation. Preliminary experiments have been conducted at Aalborg University and at an industrial partner to solve vision tasks, which would be too expensive with conventional vision techniques. Moreover, three different convolutional neural networks were tested to find the best-suited architecture. The three networks tested were the simple AlexNet, the complex ResNeXt, and small and complex SqueezeNet. Our results show that it is possible to solve the classification problem in a few epochs. Furthermore, with the use of augmented data, the performance can be improved. Our preliminary results also showed that the simpler convolutional neural network architecture from AlexNet yields a better result when classifying simple data.
UR - http://www.scopus.com/inward/record.url?scp=85099853194&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.10.161
DO - 10.1016/j.promfg.2020.10.161
M3 - Conference article
SN - 2351-9789
VL - 51
SP - 1146
EP - 1152
JO - Procedia Manufacturing
JF - Procedia Manufacturing
ER -