A photoacoustic imaging method for in-situ monitoring of laser assisted ceramic additive manufacturing

Cheng, Baokai; Lei, Jincheng; Xiao, Hai

doi:10.1016/j.optlastec.2019.02.055

Cited by 19 publications

(3 citation statements)

References 15 publications

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“…The adoption of additive manufacturing is increasing across industries such as pharmaceutical, infrastructure, biomedical, aerospace, and automotive industries have moved from just production of prototypes to on-demand manufacturing of final parts. Undoubtedly, the additive manufacturing technique has found usage in a plethora of applications across various industries; nevertheless, there are some doubts about the applicability of this technology in the production of standardized parts in industrial quantities requiring uniformity in properties like shape, intrinsic and mechanical properties [3][4] [5].…”

Section: Introductionmentioning

confidence: 99%

A methodology for evaluation of the dimensional quality of 3D printed parts

Bowoto

2023

Preprint

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In this study, the dimensional variation defect between the CAD designed model and a 3D printed model using material extrusion technique was investigated by a software algorithm developed. This software analyses the CAD and sliced model to scans for dimensional data, which can be used as a reference to check dimensional deviations during the actual 3D printing operation. It is worthy of note that despite the wide adoption of 3D printing technology in various industries, defects such as dimensional variations hinder its mass production potential. There has been a spike in the adoption of 3D printing technology across various industries due to increased industrial research and development. However, not quite a lot has been done regarding the dimensional accuracy of printed components as this affects the usage of printed components across its various areas of application. In lieu of this, a software algorithm was developed for this study which investigates the dimensional deviations of the printed model through the utilization of computer vision algorithm. This solution will be applicable to a wide range of three dimensional geometries to be printed and hence will anticipate dimensional variance, which could lead to a failed printing, thereby saving economic and human resources in additive manufacturing.

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Section: Introductionmentioning

confidence: 99%

A methodology for evaluation of the dimensional quality of 3D printed parts

Bowoto

2023

Preprint

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“…A method based on a deep belief network (DBN) was proposed to study the relationship between acoustic signals and five melting states [5]. Cheng et al [18] used a microphone to record acoustic signals of laser-assisted ceramic additive manufacturing. The acoustic signals were utilized to monitor the part forming process by combining a data processing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

Luo

et al. 2021

Sensors

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As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.

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“…This manufacturing method has attracted increasing attention over the years, and advancement in this technology has seen a positive shift from just rapid prototyping of parts, which is mostly known for the production of complex components parts. Although 3D printing has been widely used for the development of materials and structures with complex geometry, the employment of this manufacturing method is still limited in attaining or ensuring uniformity of workpieces mechanical properties and shapes, especially in mass production [3][4][5]. Presently, Components are manufactured one after the other to avoid mistakes.…”

Section: Introductionmentioning

confidence: 99%

Analytical modelling of in situ layer-wise defect detection in 3D-printed parts: additive manufacturing

Bowoto

Oladapo

Zahedi

et al. 2020

Int J Adv Manuf Technol

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This study analyses a software algorithm developed on MATLAB, which can be used to examine fused filament fabrication-based 3D printed materials for porosity and other defects that might affect the mechanical property of the final component under manufacture or the general aesthetic quality of a product. An in-depth literature review into the 3D printed materials reveals a rapidly increasing trend in its application in the industrial sector. Hence the quality of manufactured products cannot be compromised. Despite much research found to be done on this subject, there is still little or no work reported on porosity or defect detection in 3D printed components during (real-time) or after manufacturing operation. The algorithm developed in this study is tested for two different 3-D object geometry and the same filament color. The results showed that the algorithm effectively detected the presence or absence of defects in a 3D printed part geometry and filament colors. Hence, this technique can be generalized to a considerable range of 3-D printer geometries, which solve material wastages by spotting defects during the workpieces layer-wise manufacturing process, thereby improving the economic advantages of additive manufacturing.

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A photoacoustic imaging method for in-situ monitoring of laser assisted ceramic additive manufacturing

Cited by 19 publications

References 15 publications

A methodology for evaluation of the dimensional quality of 3D printed parts

A methodology for evaluation of the dimensional quality of 3D printed parts

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

Analytical modelling of in situ layer-wise defect detection in 3D-printed parts: additive manufacturing

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