Quality analysis in metal additive manufacturing with deep learning

Li, Xiang; Jia, Xiaodong; Yang, Qibo; Lee, Jay

doi:10.1007/s10845-020-01549-2

Cited by 107 publications

(28 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of neural networks has also been extended to other areas of laser manufacturing, such as laser welding (Asif et al 2020;Günther et al 2014Günther et al , 2016, additive manufacturing (Li et al 2020;Mahato et al 2020;Mycroft et al 2020), and a method to reconstruct laser pulses (Zahavy et al 2018). Here, we extend on both these, and previous (Arnaldo et al 2018), works in the area of laser surface texturing.…”

Section: Introductionmentioning

confidence: 88%

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

et al. 2021

View full text Add to dashboard Cite

Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

show abstract

Section: Introductionmentioning

confidence: 88%

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

et al. 2021

View full text Add to dashboard Cite

show abstract

“…CV is one of the fields where deep learning has been used so far, as neural networks can extract features from images, making the image processing procedure much easier. One of its applications is object recognition for applications such as traffic sign detection for advanced driver assistance systems [ 19 ], fruit recognition systems to optimise harvesting in the agricultural environment [ 20 ], quality analysis of materials for manufacturing processes [ 21 ], 3D modelling systems to obtain the structure of the objects based on different images [ 22 ], etc.…”

Section: State-of-the-artmentioning

confidence: 99%

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Gorospe

Mulero²,

Arbelaitz

et al. 2021

Sensors

View full text Add to dashboard Cite

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

show abstract

“…Finally, neural network (NN) and especially deep learning (DL) architectures are becoming more and more popular nowadays thanks to the impressive results achieved with their image analysis and classification capabilities (Redmon and Farhadi 2018), but there is still debate on what is the best way to feed temporal information to a DL architecture (Lipton et al 2015) and on how to achieve the same good results with shallower, and thus faster, networks able to keep up with the typical acquisition rate of high-speed video imaging. Nevertheless, in the last few years the first examples of application of neural networks to AM process monitoring have been developed, demonstrating their automatic feature extraction capabilities and classification performance when applied to in-situ process data (Kwon et al 2020;Li et al 2020;Gonzalez-Val et al 2020). However, most of them focus on classifying the static observations, i.e.…”

Section: State Of the Artmentioning

confidence: 99%

Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

Bugatti

Colosimo

2021

J Intell Manuf

View full text Add to dashboard Cite

The increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

show abstract

Quality analysis in metal additive manufacturing with deep learning

Cited by 107 publications

References 35 publications

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

Contact Info

Product

Resources

About