Surface Morphology Analysis Using Convolutional Autoencoder in Additive Manufacturing with Laser Engineered Net Shaping

Shi, Zhangyue; Mandal, Sumit; Harimkar, Sandip P.; Liu, Chenang

doi:10.1016/j.promfg.2021.06.005

Cited by 12 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A case study in laser engineered net shaping (LENS) validates the effectiveness of the proposed method, achieving a classification accuracy of 70%, outperforming benchmark methods. Thus, the developed convolutional autoencoder-based approach shows promise for extracting surface morphology features in additive manufacturing [ 170 ].…”

Section: Ai-based Surface Roughness Prediction Methods For Additively...mentioning

confidence: 99%

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Batu,

Lemu,

Shimels

2023

Materials

View full text Add to dashboard Cite

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

show abstract

Section: Ai-based Surface Roughness Prediction Methods For Additively...mentioning

confidence: 99%

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Batu,

Lemu,

Shimels

2023

Materials

View full text Add to dashboard Cite

show abstract

Hybrid data-driven feature extraction-enabled surface modeling for metal additive manufacturing

Shi

Mandal

Harimkar

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach

Liu

Wang

et al. 2022

J Intell Manuf

View full text Add to dashboard Cite

Surface Morphology Analysis Using Convolutional Autoencoder in Additive Manufacturing with Laser Engineered Net Shaping

Cited by 12 publications

References 30 publications

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Hybrid data-driven feature extraction-enabled surface modeling for metal additive manufacturing

Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach

Contact Info

Product

Resources

About