Surface Roughness Measurement of Parts Manufactured by FDM Process using Light Sectioning Vision System

Kelkar, Aditya; Kumbhar, N. N.; Mulay, A. V.

doi:10.1007/s40032-016-0341-y

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Light sectioning method is more efficient for measurement of FDM produced parts, as it is non-contact and reliable method. It can be concluded as light sectioning method has middle-level accuracy and provides results with acceptable tolerance [9].…”

Section: Related Workmentioning

confidence: 84%

Surface Analysis in Additive Manufacturing Using Image Processing

Patil¹

2022

IJSREM

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Fused Deposition Modeling (FDM) is a type of additive manufacturing process where prototypes are developed by depositing layers on previously deposited layer of material. Surface roughness is an important factor in determining quality of produced parts. This work proposes a methodology to calculate the surface roughness of part manufactured using FDM process. The surface roughness values to be measured using conventional stylus instrument and Digital image processing technique. Stylus comes in direct contact with surface so it destroys the surface topography. Image processing is a non-contact method hence it overcomes this problem. A camera and directional lightings will be used to capture images of surface of specimens. These images will be analyzed and processed using various image processing techniques available in MATLAB image processing toolbox. Experimental results will be validated by comparing the results obtained by using image processing technique with conventional stylus system. The system is also proposed to inspect surface irregularities. Key Words: Surface roughness, image processing, MATLAB, FDM.

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Section: Related Workmentioning

confidence: 84%

Surface Analysis in Additive Manufacturing Using Image Processing

Patil¹

2022

IJSREM

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“…Another case where the surface roughness of parts manufactured by Fast Prototyping using thermoplastics (ABS) for layer deposition of the fused material using sectioned light vision systems is studied, it has been found that the results are comparable with the traditional method of profilometry for estimate the arithmetic mean of the roughness (Ra) with the benefits of vision inspection. It is mentioned that the most important process variables affecting the finish of the surface are the thickness of the layer and the construction orientation [13]. For the detection of low contrast defects the application of the Wavelet Transform was found under various lighting conditions.…”

Section: Related Workmentioning

confidence: 99%

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Landeros¹,

Meneses²,

Rodríguez³

et al. 2018

CyS

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A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

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“…In Table 1, the surface roughness of most additively manufactured techniques is presented. 0.02 [20] 5-25 [21] Fused Deposition Modeling (FDM) 0.05 [22] 0.5-20 [23] Laminated Object Manufacturing (LOM) 0.10 [24] 0.8-2.5 [25] Electron Beam Melting (EBM) 0.05 [26] 1-20 [27,28] Direct Metal Laser Sintering (DMLS) 0.02 [29] 3-12 [30] Binder Jetting 0.035 [31] 3-13 [32] Direct Energy Deposition (DED) 0.25 [33] 5.08-227 [34,35] Laser powder bed fusion (L-PBF) 0.02 [36] 3.5-13.45 [37] Selective Laser Melting (SLM) 0.02 [38] 30-60 [39] The surface quality of additively manufactured components is influenced by a multitude of factors that interact in intricate ways. These factors include the choice of AM technique, powder characteristics, layer thickness, scanning strategy, and energy parameters specific to each technique.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Batu,

Lemu,

Shimels

2023

Materials

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Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

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Surface Roughness Measurement of Parts Manufactured by FDM Process using Light Sectioning Vision System

Cited by 7 publications

References 8 publications

Surface Analysis in Additive Manufacturing Using Image Processing

Surface Analysis in Additive Manufacturing Using Image Processing

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

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