Energy simulation of the fused deposition modeling process using machine learning approach

Yi, Li; Gläßner, Christopher; Krenkel, Nicole; Aurich, Jan C.

doi:10.1016/j.procir.2020.01.002

Cited by 13 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sukumar et al (2014) developed the ANN model to optimize the process conditions for face milling of AI alloy. Yi et al (2020) employed the random forest algorithm for performance evaluation in the fused deposition modeling process. In a similar context, Imran et al (2017) employed genetic algorithm techniques for the cellular manufacturing systems.…”

Section: In Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

View full text Add to dashboard Cite

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

show abstract

Section: In Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The integration of these algorithms with additive manufacturing has emerged as a solution to surmount the challenges currently encountered in this technology [12]. Hence, many researchers have turned to employ deep learning algorithms to address issues in additive manufacturing, including design optimization, real-time monitoring, performance prediction, and energy management [13][14][15][16]. Some researchers collect large amounts of data through simulations or experiments that can be used to train deep learning models that predict the mechanical properties of the parts [17,18].…”

Section: Introductionmentioning

confidence: 99%

Detecting defects in fused deposition modeling based on improved YOLO v4

Xu,

Zhang,

et al. 2023

Mater. Res. Express

View full text Add to dashboard Cite

Fused deposition modeling comes with many conveniences for the manufacturing industry, but many defects tend to appear in actual production due to the problems of the FDM mechanism itself. Although some deep learning-based object detection models show excellent performance in detecting defects in the additive manufacturing process, their detection efficiency is relatively low, and they are prone to drawbacks in the face of large numbers of defects. In this paper, an improved model based on the YOLO v4 network structure is developed. We lightweight the model and modify its loss function to achieve better performance. Experimental results show that the improved model, MobileNetV2-YOLO v4, achieves a mAP of 98.96% and an FPS of 50.8 after training, which obtains higher detection accuracy and faster detection speed than the original YOLO v4 algorithm model. Through testing, this improved model can accurately identify the location and information of target defects, which has great potential for real-time detection in the additive manufacturing process.

show abstract