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  • Open Access

    ARTICLE

    Development of AI-Based Monitoring System for Stratified Quality Assessment of 3D Printed Parts

    Yewon Choi1,2, Song Hyeon Ju2, Jungsoo Nam2,*, Min Ku Kim1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071817 - 29 January 2026

    Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

  • Open Access

    ARTICLE

    Machine Learning for Smart Soil Monitoring

    Khaoula Ben Abdellafou1, Kamel Zidi2, Ahamed Aljuhani1, Okba Taouali1,*, Mohamed Faouzi Harkat3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

    Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

  • Open Access

    PROCEEDINGS

    In-Situ Process Monitoring and Quality Evaluation for Fused Deposition Modeling with Foaming Materials

    Zhaowei Zhou1, Kaicheng Ruan1, Donghua Zhao1, Yi Xiong1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011376

    Abstract Fused deposition modeling (FDM) with foaming materials offers the capability to generate internal porous structures through in-situ foaming, imparting favorable characteristics such as weight reduction, shock absorption, thermal insulation, and sound insulation to printed objects. However, the process planning for this technology presents challenges due to the difficulty in accurately controlling the foaming rate, stemming from a complex underlying mechanism that remains poorly understood. This study introduces a multi-sensor platform for FDM with foaming materials, facilitating in-situ process monitoring of temperature field information during material modeling and quality evaluation of printed objects, i.e., abnormal foaming… More >

  • Open Access

    ARTICLE

    A Hybrid Manufacturing Process Monitoring Method Using Stacked Gated Recurrent Unit and Random Forest

    Chao-Lung Yang1,*, Atinkut Atinafu Yilma1,2, Bereket Haile Woldegiorgis2, Hendrik Tampubolon3,4, Hendri Sutrisno5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091 - 21 May 2024

    Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random… More >

  • Open Access

    ARTICLE

    Process Monitoring and Terminal Verification of Cable-Stayed Bridges with Corrugated Steel Webs under Contruction

    Kexin Zhang, Xinyuan Shen, Longsheng Bao, He Liu*

    Structural Durability & Health Monitoring, Vol.17, No.2, pp. 131-158, 2023, DOI:10.32604/sdhm.2023.023431 - 09 May 2023

    Abstract In this paper, the construction process of a cable-stayed bridge with corrugated steel webs was monitored. Moreover, the end performance of the bridge was verified by load test. Owing to the consideration of the bridge structure safety, it is necessary to monitor the main girder deflection, stress, construction error and safety state during construction. Furthermore, to verify whether the bridge can meet the design requirements, the static and dynamic load tests are carried out after the completion of the bridge. The results of construction monitoring show that the stress state of the structure during construction… More >

  • Open Access

    ARTICLE

    Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

    Jinfu Wang1, Shunyi Zhao1,*, Fei Liu1, Zhenyi Ma2

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521 - 15 June 2022

    Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices, especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the hierarchy study method, has the ability to make the latent variables follow the Gaussian More >

  • Open Access

    ARTICLE

    Error Detection and Pattern Prediction Through Phase II Process Monitoring

    Azam Zaka1, Riffat Jabeen2,*, Kanwal Iqbal Khan3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4781-4802, 2022, DOI:10.32604/cmc.2022.020316 - 11 October 2021

    Abstract The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The More >

  • Open Access

    ARTICLE

    Efficient Process Monitoring Under General Weibull Distribution

    Saman Hanif Shahbaz, Muhammad Qaiser Shahbaz*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 287-297, 2022, DOI:10.32604/csse.2022.018219 - 26 August 2021

    Abstract Product testing is a key ingredient in maintaining the quality of a production process. The production process is considered an efficient process if it is capable of quick identification of faulty products. The items produced by any production process are usually packed and acceptance or rejection of the pack depends upon its conformity to some specified quality level. Generally, the specified quality level is based upon the number of defective items found in the inspected number of items. Such decisions are based upon some rules and usually acceptance of the pack is based upon a… More >

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