Kim Ryanne Gail, Abisado Mideth, Villaverde Jocelyn, Sampedro Gabriel Avelino
Research and Development Center, Philippine Coding Camp, 2401 Taft Ave, Malate, Manila 1004, Philippines.
College of Computing and Information Technologies, National University, Manila 1008, Philippines.
Sensors (Basel). 2023 Jul 31;23(15):6821. doi: 10.3390/s23156821.
Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.
增材制造（AM）已成为各行业的变革性技术，能够生产复杂且定制化的零件。然而，确保增材制造零件的质量和可靠性仍然是一项严峻挑战。因此，基于图像的故障监测作为一种检测和分类增材制造过程中故障的有效方法，受到了广泛关注。本文对增材制造中基于图像的故障监测进行了全面综述，重点关注近期进展和未来方向。具体而言，支持者们收集了2019年至2023年的相关论文，共收集到53篇论文。本文讨论了基于图像的故障监测中使用的基本技术、方法和算法。此外，还探讨了近期的进展，如新型图像采集技术、算法和方法的应用。本文还提供了对未来方向的见解，如需要更强大的图像处理算法、高效的数据采集和分析方法、标准化的基准和数据集，以及在故障监测方面进行更多研究。通过应对这些挑战并朝着未来方向发展，可以增强增材制造中基于图像的故障监测，改善质量控制、过程优化和整体制造可靠性。
