Inconel 718 is considered the most widely adopted nickel-based superalloy, and drilling of this alloy is always challenging for researchers. Cemented carbide twist drills have been evaluated in the drilling of this alloy by changing the cutting environment or by varying the tool geometry. In the latter case, the cutting speed has been extended from 30 m/min to 60 m/min when drills are micro-textured or ground. In this study, contrary to cemented carbide twist drills, for the first time, inserts named stepped (central) and peripheral (wiper) are evaluated in the drilling of this alloy. The central insert is designed for balanced forces, while the peripheral is a wiper insert designed for better surface finish. Drilling experiments are conducted in flood cooling conditions with a 12 mm diameter twist drill equipped with novel stepped and wiper inserts at varying cutting speeds (25, 35, and 45 m/min) and feed rates (0.04, 0.06, and 0.08 mm/rev). At a cutting speed (Vc) of 25 m/min and feed rate (f) of 0.04 mm/rev, 25 holes are drilled with roughness (Ra) values ranging from 0.40 µm to 0.60 µm, which represents a significant increase in the number of holes per drill and improved surface finish over to previous work. The new inserts showed almost three-fold longer tool life compared to a standard drill bit at a higher Vc of 45 m/min and 0.04 mm/rev f with an Ra between 0.22 µm to 0.43 µm, which is deemed acceptable for aerospace applications. In addition, minimal surface and sub-surface defects were observed, eliminating the need for a post-drilling finishing operation; therefore, a one-step drilling operation was achieved.
Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.
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