scholarly journals Numerical and Experimental Investigation on the Surface Defect Generation during the Hot Extrusion of Al6063 Alloy

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6768
Author(s):  
Namsu Park ◽  
Yeonghwan Song ◽  
Seon-Ho Jung ◽  
Junghan Song ◽  
Jongsup Lee ◽  
...  
Keyword(s):  
Surface Quality ◽  
Manufacturing Industry ◽  
Surface Defect ◽  
Surface Defects ◽  
Scale Up ◽  
Laboratory Scale ◽  
Line Defect ◽  
White Line ◽  
Production Scale ◽  
Defect Generation

The surface quality control of extruded products is a critical concern in the home appliance manufacturing industry owing to the increasing need for products with a high surface quality, in addition to the essential mechanical properties of the final product. The underlying issue with achieving high-quality extrusion products is that surface defects, especially those resulting in surface gloss differences, called white line defects, are only observed after surface treatment. In this study, we aim to investigate the cause of white line defect generation on the surface of an extruded product. Accordingly, an experimental extrusion program is established using an L-shaped die that has a noticeable change in its bearing length along the inner corner of its cross-sectional profile. Laboratory-scale experiments were performed for the L-shaped extrusion of homogenized Al 6063 alloy at various ram speeds, in order to induce surface defects, considering the production yield rate required for mass production. Subsequently, the microstructural changes near the surface failure region were investigated using an arbitrary Lagrangian–Eulerian (ALE) technique-based thermomechanical finite element (FE) analysis. To scale-up the defect observation method from laboratory-scale to production-scale manufacturing and confirm the reproducibility of the surface defect, scaled-up L-shaped extrusions were performed in an actual industrial production line. Finally, the potential cause of white line defect generation is discussed by comparing the numerical and metallurgical analyses, including the scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) observations.

scholarly journals Study on Hesitant Fuzzy Multiattribute Quality Evaluation Based on Surface Defect Information of Autobody Panels

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Mao ◽  
Qi Sun ◽  
Kun Gui
Keyword(s):  
Surface Quality ◽  
Surface Defect ◽  
Evaluation Method ◽  
Surface Defects ◽  
Evaluation Model ◽  
Score Function ◽  
Hesitant Fuzzy Set ◽  
Arithmetic Average ◽  
Weighted Arithmetic ◽  
Subjective Weight

Surface defects of autobody panels have the greatest impact on the surface quality of the automobile body, but many enterprises lack a scientific and reasonable evaluation method of surface quality, relying solely on the subjective judgment of decision makers which will lead to an increase in the probability of misjudgment. In this paper, the subjective weight is determined by the genetic algorithm based on optimization, and the objective weight is determined by the improved deviation maximization method. Combining the hesitant fuzzy set theory, the hesitant fuzzy mixed weighted arithmetic average operator (HFHWA), and the score function, the surface defect information of the panel is quantified. On this basis, a complete set of hesitant fuzzy multiattribute evaluation model of surface defect information is proposed. Taking a batch of inner panels of the automobile door produced by A automobile enterprise as an example, five common defects including hidden pit, bump and scratch, rust, indentation pockmark, and ripple are selected as evaluation attributes to evaluate their surface quality, which verifies the validity and practicability of the model.

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals In-Situ Laser Polishing Additive Manufactured AlSi10Mg: Effect of Laser Polishing Strategy on Surface Morphology, Roughness and Microhardness

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 393
Author(s):  
Jiantao Zhou ◽  
Xu Han ◽  
Hui Li ◽  
Sheng Liu ◽  
Shengnan Shen ◽  
...  
Keyword(s):  
Surface Quality ◽  
Molten Pool ◽  
Surface Defects ◽  
Polished Surface ◽  
Transient Model ◽  
Surface Evolution ◽  
Powder Bed ◽  
Laser Polishing ◽  
Surface Morphologies

Laser polishing is a widely used technology to improve the surface quality of the products. However, the investigation on the physical mechanism is still lacking. In this paper, the established numerical transient model reveals the rough surface evolution mechanism during laser polishing. Mass transfer driven by Marangoni force, surface tension and gravity appears in the laser-induced molten pool so that the polished surface topography tends to be smoother. The AlSi10Mg samples fabricated by laser-based powder bed fusion were polished at different laser hatching spaces, passes and directions to gain insight into the variation of the surface morphologies, roughness and microhardness in this paper. The experimental results show that after laser polishing, the surface roughness of Ra and Sa of the upper surface can be reduced from 12.5 μm to 3.7 μm and from to 29.3 μm to 8.4 μm, respectively, due to sufficient wetting in the molten pool. The microhardness of the upper surface can be elevated from 112.3 HV to 176.9 HV under the combined influence of the grain refinement, elements distribution change and surface defects elimination. Better surface quality can be gained by decreasing the hatching space, increasing polishing pass or choosing apposite laser direction.

scholarly journals Investigation on the Surface Quality Obtained during Trochoidal Milling of 6082 Aluminum Alloy

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 75
Author(s):  
Nikolaos E. Karkalos ◽  
Panagiotis Karmiris-Obratański ◽  
Szymon Kurpiel ◽  
Krzysztof Zagórski ◽  
Angelos P. Markopoulos
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Process Parameters ◽  
Manufacturing Industry ◽  
High Surface ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
High Surface Quality ◽  
Trochoidal Milling

Surface quality has always been an important goal in the manufacturing industry, as it is not only related to the achievement of appropriate geometrical tolerances but also plays an important role in the tribological behavior of the surface as well as its resistance to fatigue and corrosion. Usually, in order to achieve sufficiently high surface quality, process parameters, such as cutting speed and feed, are regulated or special types of cutting tools are used. In the present work, an alternative strategy for slot milling is adopted, namely, trochoidal milling, which employs a more complex trajectory for the cutting tool. Two series of experiments were initially conducted with traditional and trochoidal milling under various feed and cutting speed values in order to evaluate the capabilities of trochoidal milling. The findings showed a clear difference between the two milling strategies, and it was shown that the trochoidal milling strategy is able to provide superior surface quality when the appropriate process parameters are also chosen. Finally, the effect of the depth of cut, coolant and trochoidal stepover on surface roughness during trochoidal milling was also investigated, and it was found that lower depths of cut, the use of coolant and low values of trochoidal stepover can lead to a considerable decrease in surface roughness.

Surface defect chemistry of Y-substituted and hydrated BaZrO3 with subsurface space-charge regions

2016 ◽  
Vol 4 (19) ◽  
pp. 7437-7444 ◽  
Author(s):  
Jonathan M. Polfus ◽  
Tor S. Bjørheim ◽  
Truls Norby ◽  
Rune Bredesen
Keyword(s):  
Surface Layer ◽  
Space Charge ◽  
Space Charge Region ◽  
First Principles ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Chemistry ◽  
First Principles Calculations ◽  
Proton Conducting ◽  
Charged Species

First-principles calculations were utilized to elucidate the complete defect equilibria of surfaces of proton conducting BaZrO3, encompassing charged species adsorbed to the surface, defects in the surface layer as well as in the subsurface space-charge region and bulk.

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