Metal-based additive manufacturing condition monitoring methods: From measurement to control

Lin, Xin; Zhu, Kunpeng; Fuh, Jerry Ying Hsi; Duan, Xianyin

doi:10.1016/j.isatra.2021.03.001

Cited by 42 publications

(8 citation statements)

References 122 publications

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“…Metal additive manufacturing is seeking more attention in the aerospace industry for its weight reduction technique [83]. The metal-based AM can build parts with high precision and density compared with other AM processes [84]. The bracket connector for Airbus A350 XWB is manufactured using laser AM of titanium material [85].…”

Section: Methodsmentioning

confidence: 99%

A review on additive manufacturing for aerospace application

Shanmugam,

Ramoni

et al. 2024

Mater. Res. Express

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Additive manufacturing or 3D printing technology is used to create a component by adapting layer by layer method. The adroitness of additive manufacturing in creating a complex geometry as a whole is very much harnessed by the aerospace Industry. Generating a component using additive manufacturing involves optimal design, methods, and processes. This review gives a broad knowledge in developing a part or product by choosing the appropriate design, method, and processes. The end-to-end flow process (from scratch to finished model) for developing a component by additive manufacturing is described with a detailed flow diagram. The flow process proposed in this review will act as a primary source for manufacturing any component as per the industry standards. Also, the role of additive manufacturing in the aerospace industry is the need of the hour and greatly in demand of innovative ideas. But as an infant technology, AM for aerospace has its fair share of issues The paper discusses issues and challenges of AM for aerospace applications to enable the widespread adoption of additively manufactured components in the aerospace industry.

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Section: Methodsmentioning

confidence: 99%

A review on additive manufacturing for aerospace application

Shanmugam,

Ramoni

et al. 2024

Mater. Res. Express

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“…Based on increasing research in AM technologies, recent research topics are in the niche research areas of condition monitoring, sustainability [102], environmental degradation [103], defect assessment on structural integrity Fig. 6.…”

Section: Multidisciplinary Dimension Of Additive Manufacturingmentioning

confidence: 99%

“…The 3D printing technique has been used to fabricate various electronic devices [97][98][99][100][101][102]. For instance, [206] proposed a 3D printer-like data interface for a machining robot.…”

Section: Additive Manufacturing In Emerging Energy and Electronic Dev...mentioning

confidence: 99%

Global perspective and African outlook on additive manufacturing research − an overview

et al. 2022

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Additive manufacturing (AM) technologies and advances made globally in medicine, construction, aerospace, and energy sectors are discussed. The paper further explores the current state of AM innovation and development landscape in Africa as a late comer to this area of smart manufacturing. Peer-reviewed and published literature were retrieved from Scopus database from 2005 to 2021 and analysed. In Africa, out of 500 published articles, South Africa has the highest research throughput, whereas about two-thirds of the continent is not actively participating in this burgeoning field. The main AM techniques most widely used are selective laser melting, fused deposition modelling, and direct energy deposition. Globally, there is an interplay of computational (machine learning and mechanistic models) and experimental approaches to understanding the physical metallurgy of AM techniques and processes. Though this trend is consistent with global practices, Africa lags the world in AM technologies, a niche that could leapfrog the manufacturing sector. Thus, Africa need to foster collaborative partnership within and globally to become an active global player in this industry.

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“…lized in inertial navigation systems with data fusion architectures for accurate state estimation to aid in navigation and control [31][32][33][34][35][36][37][38]. Maximizing the throughput of manufacturing processes [39][40][41][42][43][44][45][46][47][48], minimizing traffic congestion [12][13][14], and monitoring structural health [49][50][51] are some of the other areas where sensor fusion plays a critical role.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators

Balakrishnan,

Hasnain,

Egbert

et al. 2021

Preprint

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Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output measurements with state measurements affects the dictionary selection process in Koopman operator learning problems. While prior methods use dynamical conjugacy to show a direct link between Koopman eigenfunctions in two distinct data spaces (measurement channels), we explore the specific case where output measurements are nonlinear, non-invertible functions of the system state. This setup reflects the measurement constraints of many classes of physical systems, e.g., biological measurement data, where one type of measurement does not directly transform to another. We propose output constrained Koopman operators (OC-KOs) as a new framework to fuse two measurement sets. We show that OC-KOs are effective for sensor fusion by proving that when learning a Koopman operator, output measurement functions serve to constrain the space of potential Koopman observables and their eigenfunctions. Further, low-dimensional output measurements can be embedded to inform selection of Koopman dictionary functions for high-dimensional models. We propose two algorithms to identify OC-KO representations directly from data: a direct optimization method that uses state and output data simultaneously and a sequential optimization method. We prove a theorem to show that the solution spaces of the two optimization problems are equivalent. We illustrate these findings with a theoretical example and two numerical simulations.

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Metal-based additive manufacturing condition monitoring methods: From measurement to control

Cited by 42 publications

References 122 publications

A review on additive manufacturing for aerospace application

A review on additive manufacturing for aerospace application

Global perspective and African outlook on additive manufacturing research − an overview

The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators

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