Prediction of Abrasive Belt Wear Based on BP Neural Network

Cao, Yuanxun; Zhao, Ji Feng; Qu, Xingtian; Wang, Xin; Liu, Bowen

doi:10.3390/machines9120314

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…As one of the current mainstream research directions, intelligent algorithms are widely used in all walks of life with their characteristics including their multi-quantization of processing information, high prediction accuracy, and ability to find efficient solutions to complex problems. The research of scholars on intelligent algorithms has focused on analyzing prediction theory, model establishment, and algorithm optimization [40][41][42]. Common prediction models include the discrete choice model (DCM), support vector machines (SVMs), decision tree (DT), and the ANN.…”

Section: Based On Intelligent Algorithmic Modelsmentioning

confidence: 99%

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Jiang,

Ding,

et al. 2024

Algorithms

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The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

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Section: Based On Intelligent Algorithmic Modelsmentioning

confidence: 99%

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Jiang,

Ding,

et al. 2024

Algorithms

View full text Add to dashboard Cite

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“…Firstly, neural networks can model complex relationships owing to their substantial nonlinear fitting ability, allowing them to capture intricate relationships between input and output parameters. This capability enables the establishment of mapping relationships that explicit mathematical models cannot construct [16]. Secondly, neural networks exhibit strong generalization ability, providing reliable prediction results even in the face of unknown working conditions and parameter combinations by generalizing from training samples.…”

Section: Introductionmentioning

confidence: 99%

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

Ling,

Zhang,

et al. 2024

Machines

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In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.

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“…Compared with other studies, Sun et al [ 12 ] used 3700 records of images as a dataset to train a ResNet-based CNN from scratch, and the dataset must go through image pre-processing as well. Most of the studies [ 25 , 26 ] on the degree of wear of abrasive belts have mainly focused on the conditions of the abrasive belt or the grinding acoustic signal of the abrasive belts [ 27 , 28 ], while the present research concentrates on the surface texture of the workpieces after grinding. As summarized by Xie et al [ 8 ] and reported in the introduction of the manuscript, most defect detection techniques focus on local defects, not on tonality defects.…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Data-Driven Model for Texture Inspection of Ground Workpieces

Wang

Lai

et al. 2022

Sensors

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Nowadays, the grinding process is mostly automatic, yet post-grinding quality inspection is mostly carried out manually. Although the conventional inspection technique may have cumbersome setup and tuning processes, the data-driven model, with its vision-based dataset, provides an opportunity to automate the inspection process. In this study, a convolutional neural network technique with transfer learning is proposed for three kinds of inspections based on 750–1000 surface raw images of the ground workpieces in each task: classifying the grit number of the abrasive belt that grinds the workpiece, estimating the surface roughness of the ground workpiece, and classifying the degree of wear of the abrasive belts. The results show that a deep convolutional neural network can recognize the texture on the abrasive surface images and that the classification model can achieve an accuracy of 0.9 or higher. In addition, the external coaxial white light was the most suitable light source among the three tested light sources: the external coaxial white light, the high-angle ring light, and the external coaxial red light. Finally, the model that classifies the degree of wear of the abrasive belts can also be utilized as the abrasive belt life estimator.

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Prediction of Abrasive Belt Wear Based on BP Neural Network

Cited by 7 publications

References 21 publications

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

An Image-Based Data-Driven Model for Texture Inspection of Ground Workpieces

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