Next Article in Journal
Improvement of Organosolv Fractionation Performance for Rice Husk through a Low Acid-Catalyzation
Next Article in Special Issue
Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net
Previous Article in Journal
Energy Performance Comparison between Liquid-Desiccant-Assisted Air Conditioning System and Dedicated Outdoor Air System in Different Climatic Regions
Previous Article in Special Issue
Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling
Open AccessArticle

Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

1
Department of Industrial & Management System Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Korea
2
SkAD Labs SA, Chemin de la Raye 13, 1024 Ecublens, Switzerland
*
Author to whom correspondence should be addressed.
Energies 2019, 12(9), 1799; https://doi.org/10.3390/en12091799
Received: 22 March 2019 / Revised: 28 April 2019 / Accepted: 1 May 2019 / Published: 11 May 2019
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods. View Full-Text
Keywords: remote laser welding; energy-efficient process; machine learning; welding process; neural network remote laser welding; energy-efficient process; machine learning; welding process; neural network
Show Figures

Figure 1

MDPI and ACS Style

Um, J.; Stroud, I.A.; Park, Y.-K. Deep Learning Approach of Energy Estimation Model of Remote Laser Welding. Energies 2019, 12, 1799.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop