Neural Network Driven Automated Guided Vehicle Platform Development for Industry 4.0 Environment

Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.

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Neural Network Comparison for Paint Errors Classification for Automotive Industry in Compliance with Industry 4.0 Concept

Advances in Intelligent Systems and Computing - Artificial Intelligence Methods in Intelligent Algorithms ◽

10.1007/978-3-030-19810-7_35 ◽

2019 ◽

pp. 353-359

Author(s):

Michal Kebisek ◽

Lukas Spendla ◽

Pavol Tanuska ◽

Gabriel Gaspar ◽

Lukas Hrcka

Keyword(s):

Neural Network ◽

Automotive Industry ◽

Industry 4.0 ◽

Network Comparison

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Neural-network steering control of an automated guided vehicle

Electrical Engineering in Japan ◽

10.1002/eej.4391140713 ◽

1994 ◽

Vol 114 (7) ◽

pp. 135-143 ◽

Cited By ~ 3

Author(s):

Shigeyuki Funabiki ◽

Michio Mino

Keyword(s):

Neural Network ◽

Automated Guided Vehicle ◽

Steering Control

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Identificación de figuras geométricas en un sistema de visión basándose en el entrenamiento de una red neuronal artificial convolucional utilizando Python

Revista de Tecnologías Computacionales ◽

10.35429/joct.2019.11.3.7.16 ◽

2019 ◽

pp. 7-16

Author(s):

Martha Isabel Aguilera-Hernández ◽

Jorge Alan Velasco-Marín ◽

Manuel Ortiz-Salazar ◽

Jose Luis Ortiz-Simón

Keyword(s):

Neural Network ◽

Neural Networks ◽

Industry 4.0 ◽

Vision System ◽

Support Point ◽

Process Systems ◽

Geometric Figures ◽

Different Types ◽

Artificial Neural ◽

Python Programming

The image processing projects through vision systems, are a great didactic support point in the mechatronics career, since they have wide application in the industry in the process lines primarily to perform assembly, inspection, selection and component placement. One of the methods used is to apply artificial neural networks for the identification of images and a factor to analyze is the evaluation of the learning capacities of these networks in the identification of geometric figures. In this article, the training of a convolutional artificial neural network using Python is presented. This type of work is focused on joining projects based on industry 4.0 that may contain link options with process systems based on these technologies. In this work, a vision system based on python programming was made and has its contribution in the libraries that were designed and can be linked to different types of applications within a manufacturing process.

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Modeling SMEs’ Trust in the Implementation of Industry 4.0 using Kansei Engineering and Artificial Neural Network: Food and Beverage SMEs Context

Journal of Engineering and Technological Sciences ◽

10.5614/j.eng.technol.sci.2021.53.2.3 ◽

2021 ◽

Vol 53 (2) ◽

Author(s):

Mirwan Ushada ◽

Titis Wijayanto ◽

Fitri Trapsilawati ◽

Tsuyoshi Okayama

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Industry 4.0 ◽

Appropriate Technology ◽

Preference Ranking ◽

Neural Network Modeling ◽

Kansei Engineering ◽

Ann Model ◽

Food And Beverage ◽

Artificial Neural

Trust is an important aspect for policy makers in recommending the implementation of Industry 4.0 in food and beverage small and medium-sized enterprises (SMEs). SMEs’ trust in the implementation of Industry 4.0 is defined as the level of belief in applying appropriate technology for Industry 4.0 based on their knowledge, familiarity, agreement and preference. Trust is a complex construct involving several Kansei words, or human mentality parameters. Artificial neural network modeling was utilized to model SMEs’ trust in implementation of Industry 4.0. The research objectives were: 1) to analyze the trust of SMEs in the implementation of Industry 4.0 using Kansei Engineering; 2) to model the trust of SMEs in the implementation of Industry 4.0 using an artificial neural network (ANN). A questionnaire was developed using Kansei words that were generated from adjectives to represent human mentality parameters, which were stimulated by visual samples of Industry 4.0 technology. The questionnaires were distributed among 190 respondents from the three large islands of Indonesia. The data were recapitulated for training, validating and testing the ANN model based on the backpropagation supervised learning method. The output was classification of trust as ‘distrust’, ‘trust’ or ‘overtrust’. The research results indicated that the SMEs’ trust was influenced by education, knowledge, familiarity, benefit, preference ranking and verbal components.

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