Detecting surface roughness on SLS parts with various measuring techniques

Launhardt, Martin; Wörz, Andreas; Loderer, Andreas; Laumer, Tobias; Drummer, Dietmar; Hausotte, Tino; Schmidt, Michael

doi:10.1016/j.polymertesting.2016.05.022

Cited by 100 publications

(67 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, such evaluation is based on cross-sectional lines which may not well represent the inherent differences among these systems. To address such issue, some researchers proposed to inspect the arithmetic mean height of the surface which takes all the surface data into consideration [13,14]. Although the entire surface data points are utilized, the surface roughness parameters are essentially aggregated features of the data, and the geometric distributions of data points may be neglected.…”

Section: Introductionmentioning

confidence: 99%

Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts

Zheng

Zhang

Wang

et al. 2020

Optics and Lasers in Engineering

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts

Zheng

Zhang

Wang

et al. 2020

Optics and Lasers in Engineering

View full text Add to dashboard Cite

show abstract

“…Roughness measuring methods include laser reflectivity, contact stylus tracing, tactile profile measurement, focus variation, fringe projection technique and confocal laser scanning microscope [31,32]. Sun et al presented a novel method based on convolutional neural networks (CNN) for making intelligent surface roughness identifications and achieved high-precision surface roughness estimation [33].…”

Section: Morphology Observation By 3d Optical Profilermentioning

confidence: 99%

Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Selective laser melting (SLM) is a layer by layer process of melting and solidifying of metal powders. The surface quality of the previous layer directly affects the uniformity of the next layer. If the surface roughness value of the previous layer is large, there is the possibility of not being able to complete the layering process such that the entire process has to be abandoned. At least, it may result in long term durability problem and the inhomogeneity, may even make the processed structure not be able to be predicted. In the present study, the ability of a fiber laser to in-situ polish the rough surfaces of four typical additive-manufactured alloys, namely, Ti6Al4V, AlSi10Mg, 316L and IN718 was demonstrated. The results revealed that the surface roughness of the as-received alloys could be reduced to about 3 μm through the application of the laser-polishing process, and the initial surfaces had roughness values of 8.80–16.64 μm. Meanwhile, for a given energy density, a higher laser power produced a laser-polishing effect that was often more obvious, with the surface roughness decreasing with an increase in the laser power. Further, the polishing strategy will be optimized by simulation in our following study.

show abstract

“…The point cloud of each scanning line includes 1280 points. Due to the surface roughness, surface contamination, and material reflection, noise is inevitably produced during detection [11]. Although the output point cloud was preprocessed by the scanning device itself, the noise points contained in each scan line are limited.…”

Section: Denoisingmentioning

confidence: 99%

Detection System for U-Shaped Bellows Convolution Pitches Based on a Laser Line Scanner

Yang

Chen

Liu

et al. 2020

Sensors

View full text Add to dashboard Cite

An expansion joint is mainly composed of bellows and other components; it is attached on a container shell or pipe to compensate for the additional stress caused by temperature differences and mechanical vibrations. In China, the expansion joint fatigue tests are often used to assess the quality of products. After fatigue tests, convolution pitch will be changed. The amount of change is an important index that can be used to evaluate bellows expansion joints. However, the convolution pitch detection is mainly done manually and randomly by inspection agencies before shipping to the end users. This common practice is not efficient and is often subjective. This paper introduced a novel method for automatically detecting the change of the convolution pitch based on a laser line scanner and data processing technology. The laser line scanner is combined with a precision motorized stage to obtain the point cloud data of the bellows. After denoising and fitting, a peak-finding algorithm is applied to search for the crest of a convolution. The method to find the convolution pitch and the decision that needs to be made to ensure product eligibility are described in detail. A DN500 expansion joint is used as a sample to illustrate the efficiency of the system. The application of the technique intuitively allows a higher precision and relative efficiency in quality inspection of bellows expansion joints. It has also been implemented in the Special Equipment Safety Supervision and Inspection Institute of Jiangsu province with great success.

show abstract

Detecting surface roughness on SLS parts with various measuring techniques

Cited by 100 publications

References 1 publication

Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts

Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts

Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples

Detection System for U-Shaped Bellows Convolution Pitches Based on a Laser Line Scanner

Contact Info

Product

Resources

About