Experimental research of surface roughness and surface texture after laser cladding

Przestacki, Damian; Majchrowski, Radomir; Marciniak-Podsadna, Lidia

doi:10.1016/j.apsusc.2015.12.093

Cited by 41 publications

(33 citation statements)

References 4 publications

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“…However, the study did not provide universal testimony for the significance of these advantages for the accuracy of measurements . Extensive research is carried out on non‐contact‐type assessment of surface roughness parameters using machine vision system and artificial intelligence technology, which include methods such as laser speckle, light scattering, and optical interference . Pontes et al proposed the technique called multilayer perceptron (MLP) network architecture, which considerably reduces errors in predicting surface roughness parameters of machined components compared with currently used techniques.…”

Section: Introductionmentioning

confidence: 99%

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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Section: Introductionmentioning

confidence: 99%

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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“…Interestingly, 3D scanners can also be embedded in the forensic post-mortem investigation [18] The 3D scanning system is also started to be used in measuring the surface integrity after a machining process. This trend has started a decade ago after the related community came into agreement that a 3D surface geometry inspection will contribute significantly to the process performance [19]. The trends continue with the automation of the 3D scanning process which focuses on the integration of the 3D scanner with an industrial robot.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Surface Parameters to the Performance of Reverse Engineering Process

et al. 2018

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Abstract. Reverse engineering (RE) process is capable of producing CAD models from the existing part without the need of a blueprints or CAD drawing. One of the method to implement RE is via 3D scanning and the main goal is to produce CAD files that have the highest accuracy possible when compared to the actual parts. Any effort to improve the 3D scanning process should be encouraged because it will save a lot of time and expenses. As a result, this research tends to investigate the effects of surface parameters (Percentage of triangles and Grids resolution) to the performance of RE. Design of Experiments (DOE) method was used and the responses are measured in terms of File size and Error percentage. From the results, it is very interesting to note that the percentage of triangles does not have a significant effect to the file size but plays a major role in minimizing the dimension error. The grids resolution has the significant effects to the file size and error percentage. It can also be concluded that in order to get the best RE performance, the percentage of triangles and file size should be set to maximum. This will ensure that the generated CAD files will have the highest accuracy and from here, high quality products can be made from the RE process.

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“…The literature survey reveals that LAM of difficult-to-cut materials has been extensively investigated by many researchers [21]. However, the problems of machined surface integrity formed during LAM of Inconel 718, considering the cutting strategy have not been investigated sufficiently.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Surface Integrity During Machining of Inconel 718 with Various Laser Assistance Strategies

2017

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Abstract. The paper is focused on the evaluation of surface integrity formed during turning of Inconel 718 with the application of various laser assistance strategies. The primary objective of the work was to determine the relations between the applied machining strategy and the obtained surface integrity, in order to select the effective cutting conditions allowing the obtainment of high surface quality. The carried out experiment included the machining of Inconel 718 in the conventional turning conditions, as well as during the continuous laser assisted machining and sequential laser assistance. The surface integrity was evaluated by the measurements of machined surface topographies, microstructures and the microhardness. Results revealed that surface integrity of Inconel 718 is strongly affected by the selected machining strategy. The significant improvement of the surface roughness formed during machining of Inconel 718, can be reached by the application of simultaneous laser heating and cutting (LAM).

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Experimental research of surface roughness and surface texture after laser cladding

Cited by 41 publications

References 4 publications

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

The Effect of Surface Parameters to the Performance of Reverse Engineering Process

The Evaluation of Surface Integrity During Machining of Inconel 718 with Various Laser Assistance Strategies

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