Analysis of blasted rocks fragmentation using digital image processing (Case study: Limestone quarry of Obajana Cement Company)

Purpose. Blasting is an important aspect of mining activities in which fragmentation is the key component that determines its efficiency. Fragmentation is the first result of blasting, and is directly related to the costs of mining. Methods. There are two basic methods for determining the degree of rock fragmentation, the direct and indirect methods. The direct method includes sieve analysis while the indirect method involves observational, empirical and digital image processing methods. The digital image processing method with the aid of Split Desktop software was used in this study, to analyze the size of fragmentation in Obajana limestone quarry. Two pits of similar line of operation were considered. Findings. In each of the pits considered, five muckpiles of blasted rocks after blasting with different blasting patterns were analyzed to study the fragmentation phenomenon. The F80 and F90 values from the Split Desktop image analysis for the 5×3 m and 4×3 m in Pit 1 and Pit 2 were approximately 87.96 and 96.20 cm; and 91.34 and 98.66 cm respectively. Also, the F80 and F90 values obtained from the Kuz-Ram model for the 5×3 m and 4×3 m of Pit 1and Pit 2 were 99.9967 and 99.9994 cm; and 99.9957 and 99.9993 cm respectively. The results of the Split Desktop were compared to the results of the Kuz-Ram experiential model. The values of F80 and F90 of the blasted rocks are very close to the crusher gape value of 1 m, which reduces the production costs, and that is an outcome practically realized for the two pits of Obajana quarry. Originality. The findings showed that the output obtained from the Split Desktop software which is a digital image processing method were in conformity with the Kuz-Ram experiential model which is based on empirical relationship. Practical implications. In conclusion, the results of the investigation have significant implications for the practical application. It gives more options to explore for rock blast fragmentation efficiency of the desired area.

Image processing method for delamination and fiber pull-out assessment during drilling of natural fiber composites

Natural fiber-reinforced composites are promising alternative materials in the manufacture of modern moderate-to-high-technology products. However, their heterogeneous structure causes processing defects uncommon with metallic parts. Drilling of composites is an essential machining process to facilitate assembly and fastening of composite components. The occurrence of delamination damage around the drilled hole and fiber pull-out within the hole are critical factors that affect the performance of these parts when assembled. A new image processing method using digital scanning and tracing for characterizing delamination and fiber pull-out induced by drilling has been developed to address the limitations in the existing methods of quantifying drilled hole qualities. The capability of the proposed method as a delamination and fiber pull-out assessment tool was verified using simulated and real images of drilled holes. The method was also used to investigate the effect of drilling parameters on delamination and fiber pull-out in jute reinforced epoxy composite produced via resin transfer molding. The results show that drill bit diameter, feed rate, and spindle speed have varying effects on both delamination areas and fiber pull-out within the drilled hole.

Development of an image processing method for wake meandering studies and its application on data sets from scanning wind lidar and large-eddy simulation

Wake meandering studies require knowledge of the instantaneous wake shape and its evolution. Scanning lidar data are used to identify the wake pattern behind offshore wind turbines but do not immediately reveal the wake shape. The precise detection of the wake shape and centerline helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with the distance from the rotor and require good data resolution for an accurate fit. The thresholding methods usually imply a fixed value or manual selection of a threshold, which hinders the wake detection on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake detection method, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The image processing method is developed to reduce dependency on the supplementary reference data such as wind speed and direction. We show that the centerline found with the image processing is in a good agreement with the manually detected centerline and the Gaussian fit method. We also discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.