A deep neural network for classification of melt-pool images in metal additive manufacturing

Kwon, Ohyung; Kim, Hyung Giun; Ham, Min Ji; Kim, Wonrae; Kim, Gun-Hee; Cho, Jae-Hyung; Kim, Namil; Kim, Kangil

doi:10.1007/s10845-018-1451-6

Cited by 185 publications

(65 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recent applications of machine learning to additive manufacturing (Aminzadeh and Kurfess 2018;Kwon et al 2018) have focused on quality detection during or postmanufacturing or on optimisation problems around build process parameters (Panda et al 2016;Yicha et al 2015). To the best of our knowledge, a fully systematic approach to printability analysis, allowing identification of problematic regions before manufacture does not exist.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for predicting printability in metal additive manufacturing processes

et al. 2020

View full text Add to dashboard Cite

Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process.

show abstract

Section: Introductionmentioning

confidence: 99%

A data-driven approach for predicting printability in metal additive manufacturing processes

et al. 2020

View full text Add to dashboard Cite

show abstract

“…CNNs approaches are capable of analysing MWIR thermal images (see Fig. 1) to extract parameters of laser processes (Kwon et al 2018) and quality indicators (Aminzadeh and Kurfess 2018; Zhang et al 2019). We designed ConvLBM as a modular system that allows on-line quality control and defect detection in manufactured components.…”

Section: Introductionmentioning

confidence: 99%

A convolutional approach to quality monitoring for laser manufacturing

et al. 2019

View full text Add to dashboard Cite

The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing.

show abstract

“…NNs have the significant advantage of not needing to be programmed with a description of the underlying physical processes, as instead the neural network can be trained directly from the experimental data. Neural networks have used acoustic signatures for characterization of depth of weld penetration in laser welding [18,19] and classification of melt-pool image in additive manufacturing [20]. Here, convolutional neural networks (CNNs) are used as they are particularly well suited to image analysis, as their architecture contains a hierarchy of convolutional processes that can identify the presence, or lack thereof, of specific features in an image [21].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for the monitoring and process control of femtosecond laser machining

et al. 2019

View full text Add to dashboard Cite

Whilst advances in lasers now allow the processing of practically any material, further optimisation in precision and efficiency is highly desirable, in particular via the development of real-time detection and feedback systems. Here, we demonstrate the application of neural networks for system monitoring via visual observation of the work-piece during laser processing. Specifically, we show quantification of unintended laser beam modifications, namely translation and rotation, along with real-time closed-loop feedback capable of halting laser processing immediately after machining through a ∼450 nm thick copper layer. We show that this approach can detect translations in beam position that are smaller than the pixels of the camera used for observation. We also show a method of data augmentation that can be used to significantly reduce the quantity of experimental data needed for training a neural network. Unintentional beam translations and rotations are detected concurrently, hence demonstrating the feasibility for simultaneous identification of many laser machining parameters. Neural networks are an ideal solution, as they require zero understanding of the physical properties of laser machining, and instead are trained directly from experimental data.

show abstract

A deep neural network for classification of melt-pool images in metal additive manufacturing

Cited by 185 publications

References 32 publications

A data-driven approach for predicting printability in metal additive manufacturing processes

A data-driven approach for predicting printability in metal additive manufacturing processes

A convolutional approach to quality monitoring for laser manufacturing

Deep learning for the monitoring and process control of femtosecond laser machining

Contact Info

Product

Resources

About