Journal ArticleDOI
Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model
Haixi Wu,Zhonghua Yu,Yan Wang +2 more
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TLDR
In this paper, a real-time lightweight AM machine condition monitoring approach is proposed, where acoustic emission (AE) sensor is used, and the original AE waveform signals are first simplified as AE hits, and then segmental and principal component analyses are applied to further reduce the data size and computational cost.Abstract:
Machine condition monitoring is considered as an important diagnostic and maintenance strategy to ensure product quality and reduce manufacturing cost. However, currently, most additive manufacturing (AM) machines are not equipped with sensors for system monitoring. In this paper, a real-time lightweight AM machine condition monitoring approach is proposed, where acoustic emission (AE) sensor is used. In the proposed method, the original AE waveform signals are first simplified as AE hits, and then segmental and principal component analyses are applied to further reduce the data size and computational cost. From AE hits, the hidden semi-Markov model (HSMM) is applied to identify the machine states, including both normal and abnormal ones. Experimental studies on fused deposition modeling (FDM), one of the most popular AM technology, show that the typical machine failures can be identified in a real-time manner. This monitoring method can serve as a diagnostic tool for FDM machines.read more
Citations
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Journal ArticleDOI
Machine learning in additive manufacturing: State-of-the-art and perspectives
TL;DR: A comprehensive review on the state-of-the-art of ML applications in a variety of additive manufacturing domains can be found in this paper, where the authors provide a section summarizing the main findings from the literature and provide perspectives on some selected interesting applications.
Journal ArticleDOI
Data-driven cost estimation for additive manufacturing in cybermanufacturing
Siu L. Chan,Yanglong Lu,Yan Wang +2 more
TL;DR: A new cost estimation framework is developed based on big data analytics tools so that the manufacturing cost associated with a new job can be estimated based on the similar ones in the past, based on historical data.
Journal ArticleDOI
A Systematic Review of Hidden Markov Models and Their Applications
TL;DR: The paper represents a short but comprehensive description of research on hidden Markov model and its variants for various applications and shows the significant trends in the research onhiddenMarkov model variants and their applications.
Journal ArticleDOI
Machine health management in smart factory: A review
Gil-Yong Lee,Mincheol Kim,Ying-Jun Quan,Min-Sik Kim,Thomas J. Y. Kim,Hae-Sung Yoon,Sangkee Min,Dong-Hyeon Kim,Jeong-Wook Mun,Jin Woo Oh,In-Gyu Choi,Chung-Soo Kim,Won-Shik Chu,Jinkyu Yang,Binayak Bhandari,Choon-Man Lee,Jeong-Beom Ihn,Sung-Hoon Ahn +17 more
TL;DR: The implementations of machine health managements within the smart factory are discussed in terms of data connectivity, communications, Cyber-physical system (CPS) and virtual factory, relating them to Internet of things (IoT), cloud computing, and big data management.
Proceedings ArticleDOI
A Review of Machine Learning Applications in Additive Manufacturing
TL;DR: The review identifies areas in the AM lifecycle, including design, process plan, build, post process, and test and validation, that have been researched using ML, as well as existing hurdles currently limiting applications.
References
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