One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

Bone-drilling operation necessitates an accurate and efficient surgical drill bit to minimize thermal damage to the bone. This article provides a methodology for predicting the bone temperature elevation during surgical bone drilling and to gain a better understanding on the influences of the point angle, helix angle and web thickness of the drill bit. The proposed approach utilized the normalized Cockroft–Latham damage criterion to predict material cracking in the drilling process. Drilling simulation software DEFORM-3D is used to approximate the bone temperature elevation corresponding to different drill bit geometries. To validate the simulation results, bone temperature elevations were evaluated by comparison with ex vivo bone-drilling process using bovine femurs. The computational results fit well with the ex vivo experiments with respect to different drill geometries. All the investigated drill bit geometries significantly affect bone temperature rise. It is discovered that the thermal osteonecrosis risk regions could be reduced with a point angle of 110° to 140°, a helix angle of 5° to 30° and a web thickness of 5% to 40%. The drilling simulation could accurately estimate the maximum bone temperature elevation for various surgical drill bit point angles, web thickness and helix angles. Looking into the future, this work will lead to the research and redesign of the optimum surgical drill bit to minimize thermal insult during bone-drilling surgeries.