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Multi-spindle drilling simultaneously produces multiple holes to save time and increase productivity. The assessment of hole quality is important in any drilling process and is influenced by characteristics of the cutting tool, drilling parameters and machine capacity. This study investigates the drilling performance of uncoated carbide, and coated carbide (TiN and TiCN) drills when machining Al2024 aluminium alloy. Thrust force and characteristics of hole quality, such as the presence of burrs and surface roughness, were evaluated. The results show that the uncoated carbide drills performed better than the TiN and TiCN coated tools at low spindle speeds, while TiCN coated drills produced better hole quality at higher spindle speeds. The TiN coated drills gave the highest thrust force and poorest hole quality when compared with the uncoated carbide and TiCN coated carbide drills. Additionally, a multi-layer perceptron neural network model was developed, which could be useful for industries and manufacturing engineers for predicting the surface roughness in multi-hole simultaneous drilling processes.

In the past, several experimental and theoretical studies have been carried out to evaluate the ultimate bearing capacity (UBC) of geosynthetic-reinforced sandy soil foundations (GRSSFs). The experimental studies consist of model footing load tests which are expensive and time consuming whereas the results obtained by theoretical expressions often lack consistency. In the study reported in this paper, a cost-effective, extreme learning machine (ELM) model was used for the first time to obtain a more realistic prediction of the UBC of a GRSSF. A large dataset consisting of actual field and laboratory measurements of UBC was used to develop and validate the model. Its predictive performance was then compared against robust machine learning regression models and traditional theoretical methods. The study shows that the proposed model is useful and attains an adequate level of accuracy in predicting the UBC of GRSSFs when compared with other data-driven models and some traditional methods. The research also shows that the ELM technique is a realistic and reliable approach that could be employed in geotechnical engineering intelligent systems for the prediction of multivariate non-linear problems.

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value.