Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network

Sikora, Jan; Wagnerova, Renata; Landryová, Lenka; Šíma, Jiří; Wrona, Stanisław

doi:10.3390/app11167484

Cited by 8 publications

(5 citation statements)

References 50 publications

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“…Noise in the large factories not only harm the health of operators and workers [ 1 ], but also increase the error rate during the operation process [ 2 ], both of which would inevitably result in the decrease of production efficiency and the increase of manufacturing cost [ 3 ]. Owing to the inevitable noise resulted from the working equipment, an effective method to reduce the damage of noise is to place the sound absorbing materials around the noise source.…”

Section: Introductionmentioning

confidence: 99%

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

et al. 2022

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An acoustic metamaterial absorber of parallel–connection square Helmholtz resonators is proposed in this study, and its sound absorption coefficients are optimized to reduce the noise for the given conditions in the factory. A two–dimensional equivalent simulation model is built to obtain the initial value of parameters and a three–dimensional finite element model is constructed to simulate the sound absorption performance of the metamaterial cell, which aims to improve the research efficiency. The optimal parameters of metamaterial cells are obtained through the particle swarm optimization algorithm, and its effectiveness and accuracy are validated through preparing the experimental sample using 3D printing and measuring the sound absorption coefficient by the standing wave tube detection. The consistency between the experimental data and simulation data verifies feasibility of the proposed optimization method and usefulness of the developed acoustic metamaterial absorber, and the desired sound absorption performances for given conditions are achieved. The experimental results prove that parallel–connection square Helmholtz resonators can achieve an adjustable frequency spectrum for the low frequency noise control by parameter optimization, which is propitious to promote its application in reducing the noise in the factory.

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Section: Introductionmentioning

confidence: 99%

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

et al. 2022

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“…In deep learning applications, synthetic data has been used to train convolutional neural networks for quality control (Sikora et al, 2021) and enable deep learning in automotive wiring harness manufacturing (Nguyen et al, 2022). It has also been used in a hierarchical approach for automatic quality inspection in the automotive industry (Rio-Torto et al, 2021) and to train deep learning models in a smart augmented reality instructional system for mechanical assembly (Lai et al, 2020).…”

Section: Physical Simulationmentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

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“…As a result, it helps learning more robust representations. With the technology of neural networks developing in a high pace, the current advanced neural networks in image classification are not convolutional neural networks [5]. However, CNN has been dominating for a very long time in recent years in most cases and tasks of image and video recognition and similar tasks.…”

Section: Advantages Of Cnnmentioning

confidence: 99%

Analysis of convolutional neural networks and its application in object detection

Gao

2023

ACE

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With the development of times and technology, artificial intelligence-based neural network algorithms have been widely used in scientific research and life. Among them, Convolutional neural network (CNN) is the most classic and the most representative. CNN have been widely used in images classification in recent years. This article focuses on the basic information of the convolutional neural networks and its applications especially in object detection. Also, the advantages of the CNN and the possible future improvements will be told in this article. In the end of this article some research of experiments will be shown in order to prove the accuracy of convolutional neural networks in detecting objects and visualizing results. This article gathers basic knowledge of convolutional neural networks. As a result, it may help new starters to get to know the CNN and then make contributions to the development of deep learning especially the CNN according to the advantages and future improvements mentioned in the article.

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Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network

Cited by 8 publications

References 50 publications

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Analysis of convolutional neural networks and its application in object detection

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