Drilling generated heat is considered to have significant effect on the product. In this study, woven glass fiber reinforced epoxy composites were fabricated using Vacuum Resin Infusion technique. The composites were drilled using HSS twist drill bit at various drilling parameters. The temperature during drill was measured using infra red thermometer. The damage factor of drilled holes was measured using 3D non-contact surface measurement and correlates it with temperature. Results showed that low feed rate recorded maximum temperature and high fede rate showed lower heat generated during drilling. Damage factor measured at lower feed rate was high and low at high feed rate. This was considered due to evaporation of resin matrix.
The demand for mechanical fastening in composite materials is increasing due to their potential in large assemblies, aerospace and automotive industries. In practice, small components are integrated into large assemblies drilling holes in composite materials. Drilling defect free holes in composite presents many challenges during part assembly and services. This study presents the effects of cutting parameters used for drilling holes in glass fiber reinforced polymeric (GFRP) composites and hybrid fiber reinforced polymeric (HFRP) composites. Both the composites plates of 3 mm thickness were fabricated using a hand lay-up technique for the purpose of evaluating the effects of parameters on the quality of drilled holes. The holes were drilled using a 5 mm solid carbide twist drill at different spindle speed and feed rate. The quality of holes was assessed with respect to damage factor (Fd) and surface roughness (Ra) of the drilled holes. Results showed that the HFRP composite experienced lower damage factor (Fd) as compared to GFRP composite at lower feed rate or spindle speed. Scanning electron microscopic (SEM) examination revealed that the occurrence of delamination, fiber pull-out and matrix cracking was accelerated in the drilled holes at high spindle speed and feed rate.
Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R2, while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy.
Abstract. Drilling hybrid fiber reinforced polymer (HFRP) composite is a novel approach in fiber reinforced polymer (FRP) composite machining studies as this material combining two different fibers in a single matrix that resulted in considerable improvement in mechanical properties and cost saving as compared to conventional fiber composite material. This study presents the development and optimized way of drilling HFRP composite at various drilling parameters such as drill point angle, feed rate and cutting speed by using the full factorial design experiment with the combination of analysis of variance (ANOVA) approach and signal to noise (S/N) ratio analysis. The results identified optimum drilling parameters for drilling the HFRP composite using small drill point angle at low feed rate and medium cutting speed that resulted in lower thrust force.
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