Build Strategy Investigation of Ti-6Al-4V Produced Via a Hybrid Manufacturing Process

Yan, Lei; Cui, Wenyuan; Newkirk, Joseph William; Liou, Frank W.; Thomas, Eric; Baker, Andrew; Castle, James

doi:10.1007/s11837-018-3009-7

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…For industrial tooling applications, however, the slow and expensive PB-SLM process is an obstacle to creating components in the >10kg range. One way to minimize cost and time is through hybrid AM [7,8]. As the beneficial conformal cooling channels are in the upper cavity portions of tooling components, it would be preferable to machine conventionally the bulky, simple bases and then to additively manufacture on top of these bases.…”

Section: Introductionmentioning

confidence: 99%

Metallurgical and mechanical assessment of hybrid additively-manufactured maraging tool steels via selective laser melting

Azizi

Ghiaasiaan

Prager

et al. 2019

Additive Manufacturing

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Section: Introductionmentioning

confidence: 99%

Metallurgical and mechanical assessment of hybrid additively-manufactured maraging tool steels via selective laser melting

Azizi

Ghiaasiaan

Prager

et al. 2019

Additive Manufacturing

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“…Table 1 lists the process parameters employed to fabricate the parts. The energy density is defined as E = Laser Power/Scan speed × Layer thickness (J/mm 2 ) [38,39], which is considered as a key factor affecting the quality. For the quality inspection, the samples were transverse cross-sectioned and prepared with the standard metallographic procedure.…”

Section: Sample Preparationmentioning

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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Readiness of subtractive and additive manufacturing and their sustainable amalgamation from the perspective of Industry 4.0: a comprehensive review

Iqbal

Zhao

Suhaimi

et al. 2020

Int J Adv Manuf Technol

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Build Strategy Investigation of Ti-6Al-4V Produced Via a Hybrid Manufacturing Process

Cited by 8 publications

References 9 publications

Metallurgical and mechanical assessment of hybrid additively-manufactured maraging tool steels via selective laser melting

Metallurgical and mechanical assessment of hybrid additively-manufactured maraging tool steels via selective laser melting

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Readiness of subtractive and additive manufacturing and their sustainable amalgamation from the perspective of Industry 4.0: a comprehensive review

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