Journal ArticleDOI
Metal-based additive manufacturing condition monitoring methods: From measurement to control
TLDR
In this article, the authors present a survey of the state-of-the-art metal-based additive manufacturing (MAM) process monitoring and control systems, and discuss the advantages and disadvantages of their algorithmic implementations and applications.Abstract:
Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide applications of MAM technologies. These decrease the repeatability and consistency of build quality. In order to overcome these shortcomings and to produce high-quality parts, it is very important to carry out online monitoring and process control in the building process. A process monitoring system is demanded which can automatically optimize the process parameters to eliminate incipient defects, improve the process stability and the final build quality. In this paper, the current representative studies are selected from the literature, and the research progress of MAM process monitoring and control are surveyed. Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised.read more
Citations
More filters
Journal ArticleDOI
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Hasan Tercan,T. Meisen +1 more
TL;DR: A comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing is presented in this paper , where the publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use.
Journal ArticleDOI
Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process
Yiming Wang,Xingwang Xu,Zhuang Zhao,Wenxiang Deng,Jing Han,Lianfa Bai,Xianglong Liang,Jianyong Yao +7 more
TL;DR: Based on the monitoring of weld width and reinforcement, a regression network for extracting the global information of molten pool is proposed, and an active disturbance rejection control (ADRC) is designed to adjust the welding current.
Journal ArticleDOI
Rapid Surface Defects Detection in Wire and Arc Additive Manufacturing Based on Laser Profilometer
Journal ArticleDOI
Motion Feature Based Melt Pool Monitoring For Selective Laser Melting Process
TL;DR: In this paper , a new motion feature is introduced to describe the moving melt pool, and the distance between the centroid and the boundary of melt pool is calculated from the unfolded clockwise at a step angle, which constructs a high dimensional feature vector as the motion features.
Journal ArticleDOI
Roadmap on signal processing for next generation measurement systems
Dimitris K. Iakovidis,Melanie Ooi,Ye Chow Kuang,Serge Demidenko,Serge Demidenko,Alexandr Shestakov,Vladimir Sinitsin,Manus Henry,Manus Henry,Andrea Sciacchitano,Stefano Discetti,Silvano Donati,Michele Norgia,Andreas Menychtas,Ilias Maglogiannis,Selina Wriessnegger,Luis Alberto Barradas Chacon,George Dimas,Dimitris Filos,Dimitris Filos,Anthony H. Aletras,Anthony H. Aletras,Johannes Töger,Feng Dong,Shangjie Ren,Andreas Uhl,Jacek Paziewski,Jianghui Geng,Francesco Fioranelli,Ram M. Narayanan,Carlos Fernandez,Christoph Stiller,Konstantina Malamousi,Spyros Kamnis,Konstantinos K. Delibasis,Dong Wang,Jianjing Zhang,Robert X. Gao +37 more
TL;DR: In this paper, the authors present a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.