Machine learning-based image processing for on-line defect recognition in additive manufacturing

Caggiano, Alessandra; Zhang, Jianjing; Alfieri, Vittorio; Caiazzo, Fabrizia; Gao, Robert X.; Teti, Roberto

doi:10.1016/j.cirp.2019.03.021

Cited by 304 publications

(92 citation statements)

References 14 publications

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“…In contrast to most studies, the authors devised an ensemble algorithm comprising random forest (RF), classification and regression tree (CART), random vector functional link (RVFL), ridge regression (RR), support vector regression (SVR), and adaptive boosting (Adaboost) in their study which was found to have better prediction performance than the individual ones. Caggiano et al [58] proposed an ML approach for online fault detection (via image processing) to precisely identify defected components in selective laser melting (SLM). The deep convolutional neural network (DCNN) algorithm developed in this study was able to characterize layer-wise images of the SLM process and thereby identify defects induced by process nonconformities with a classification accuracy as high at 99.4%.…”

Section: For Shop Floor Monitoring and Controlmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

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According to statistics provided by the UN in 2018, the global urban population rose from 0.75 billion in 1950 to 4.2 billion by 2018, a 5.5-fold increase in nearly seven decades. Currently, this 4.2 billion urban population accounts for 55% of the current global population, and given the trend, the global urban population is expected to touch 68% of total population by 2050. [1,2] To keep abreast with this growing shift toward urbanization, the last decade witnessed a multitude of global projects across the USA, EU, Asia Pacific, and Middle East regions to cater to the needs of a futuristic urban population through the concept of "smart cities." [3,4] Some of the popular programs include Smart Cities Initiatives by the USA, [5] EU H2020 Smart Cities and Communities project, [6] Smart Nation Program by Singapore, [7] Global Initiative for Resource Efficient Cities (GI-REC) by UN, [8] etc., to mention a few. Existing literature shows that there is no single or standardized definition to the concept of a smart city. [9] However, the central theme usually revolves around the application of information and digital technologies to provide intelligent and innovative solutions to the demanding requirements of an urban ecosystem including infrastructure, transport, healthcare, governance, and security. [2,10] The overall essence and motivation of a smart city is to facilitate the highest quality of life to its residents, while simultaneously optimizing the resources and energy efficiency, aiding the overall sustainability, social, and economic development of the city. Given the fact that the concept of smart cities is constantly evolving, devising a single template to describe the various characteristic features of a smart city can be a challenging task. [9] However, notable efforts have been made to determine and define a few salient features or indices of a smart city, published by research organizations as well as by consulting firms. [11] Both types of reports present commonalities in terms of indices, which are integral to smart cities and hence enable their acceptance with a degree of confidence. Some of the most notable and cited reports include Cities in Motion Index by IESE, [12] Smart Cities Ranking by Juniper Research, [13] Top 50 Smart City

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Section: For Shop Floor Monitoring and Controlmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

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“…Metal additive manufacturing (AM) processes have introduced some capabilities unparalleled by traditional manufacturing, as they realize custom-designed shape, complex features, and low materials consumptions provided by AM [1]. Laser metal deposition (LMD) is a form of AM which accomplishes the layer-by-layer fabrication of near net-shaped components by introducing a powder stream into a high-energy laser beam.…”

Section: Introductionmentioning

confidence: 99%

“…The above parameters constantly affect the parts being formed. Some studies have focused on the process parameter selection and optimization of the performance of LMD parts, but the defect presence is still high compared to traditional manufacturing [1,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Cui

Zhang

et al. 2020

Applied Sciences

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Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

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“…Based on the levels of the governing factors, the process can be conducted either in Selective Laser Sintering (SLS) or Melting (SLM) mode [4]. Since many concurrent phenomena are in place and since LPBF is primarily aimed at industries with stringent standards, different methods of in-line monitoring are required and are currently under development [5,6].…”

Section: Introductionmentioning

confidence: 99%

On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

2020

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Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

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Machine learning-based image processing for on-line defect recognition in additive manufacturing

Cited by 304 publications

References 14 publications

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

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