Analysis and Optimization of Geometric Accuracy of ZY-1 02C HRC Image

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

Remote Sensing ◽

10.3390/rs13050957 ◽

2021 ◽

Vol 13 (5) ◽

pp. 957

Author(s):

Guglielmo Grechi ◽

Matteo Fiorucci ◽

Gian Marco Marmoni ◽

Salvatore Martino

Keyword(s):

Surface Temperature ◽

Infrared Thermography ◽

Point Cloud ◽

Engineering Geology ◽

Point Clouds ◽

Jointed Rock ◽

Geometric Accuracy ◽

Rock Masses ◽

Natural Systems ◽

Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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Influence of Wear Pattern of Graphite Electrode on EDM Geometric Accuracy of Slot Machining

Procedia CIRP ◽

10.1016/j.procir.2020.02.265 ◽

2020 ◽

Vol 95 ◽

pp. 408-413

Author(s):

Yuchao Jia ◽

Guanxin Chi ◽

Wentong Li ◽

Zhenlong Wang ◽

Lijuan Cui

Keyword(s):

Graphite Electrode ◽

Geometric Accuracy ◽

Wear Pattern

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Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-06712-4 ◽

2021 ◽

Author(s):

Sherwan Mohammed Najm ◽

Imre Paniti

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Parameters ◽

Single Point ◽

Tool Material ◽

Deformation Mode ◽

Geometric Accuracy ◽

Forming Process ◽

Tool Shape ◽

Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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