Application of the artificial neural network method to detect defective assembling processes by using a wearable technology

Autor: İlker Küçükoğlu, Onder Tokcalar, Hilal Atici-Ulusu, Tülin Gündüz
Přispěvatelé: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü., Küçükoğlu, İlker, Atıcı-Ulusu, Hilal, Gündüz, Tülin, D-8543-2015
Rok vydání: 2018
Předmět:
Artificial neural network
0209 industrial biotechnology
Operations research & management science
Wearable device
Computer science
Process (engineering)
Assembly
Automotive industry
Wearable computer
Neural network structures
Augmented reality
02 engineering and technology
Gloves
Finger Joint
Hand
Industrial and Manufacturing Engineering
Smartwatch
Engineering
020901 industrial engineering & automation
0202 electrical engineering
electronic engineering
information engineering
Glove
Wearable computers
MATLAB
Wearable technology
Sensor
computer.programming_language
Production operations
business.industry
Industry 4.0
Signal classification
Artificial neural network methods
Engineering
manufacturing
Hardware and Architecture
Control and Systems Engineering
Production technology
Engineering
industrial
Defects
020201 artificial intelligence & image processing
Ergonomics
Digital technologies
business
Automotive companies
computer
Neural networks
Software
Computer hardware
Zdroj: Journal of Manufacturing Systems. 49:163-171
ISSN: 0278-6125
DOI: 10.1016/j.jmsy.2018.10.001
Popis: Recently, the Industry 4.0 connects production processes and smart production technologies to lead up to a new technological age. The Industry 4.0 utilizes digital technologies such as augmented reality, sensors and wearables (e.g. smart watches, gloves, and glasses) to track all production operations. This study considers the problem of distinguishing proper and defective operations in connector assembly tasks in an automotive company. A digital assembly glove is developed as a wearable technology prototype. This glove is introduced to measure vibration and force values on the fingers to classify proper and defective operations in connector assembly processes. Experiments were conducted with 17 subjects to obtain force and vibration signals of the considered assembly task. For the signal classification of the digital assembly glove, the artificial neural network (ANN) methodology was used. Performance of the ANN was tested on the case of connector assembly process of the company. The collected proper and defective connection measurements were used for the training, validation, and testing of the ANN. As a result of the MATLAB computations, a neural network structure was obtained with 95% accuracy. The performance of the neural network showed that the ANN is an applicable method for the considered wearable technology to detect defective operations.
Databáze: OpenAIRE
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