In this research an attempt has been made to develop mathematical models for predicting mechanical properties including ultimate tensile strength, impact toughness, and hardness of the friction stir welded AA 6061-T6 joints at 95% confidence level. Response surface methodology with central composite design having four parameters and five levels has been used. The four parameters considered were tool pin profile, rotational speed, welding speed and tool tilt angle. Three confirmation tests were performed to validate the empirical relations. In addition, the influence of the process parameters on ultimate tensile strength, impact toughness, and hardness were investigated. The results indicated that tool pin profile is the most significant parameter in terms of mechanical properties; tool with simple cylindrical pin profile produced weld with high ultimate tensile strength, impact toughness, and hardness. In addition to tool pin profile, rotational speed was more significant compared to welding speed for ultimate tensile strength and impact toughness; whereas, welding speed showed dominancy over rotational speed in case of hardness. Optimum conditions of process parameters have been found at which tensile strength of 92%, impact toughness of 87%, and hardness of 95% was achieved in comparison to the base metal. This research will contribute to expand the scientific foundation of friction stir welding of Aluminum alloys with emphasis on AA 6061-T6. The results will aid the practitioners to develop a clear understanding of the influence of process parameters on mechanical properties, and will allow the selection of best combinations of parameters to achieve desired mechanical properties.

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Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept. INDEX TERMS Access control, hybrid access control, secure vehicle localization, machine learning, neural networks, Internet of Things.

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Cell formation is the fundamental step while designing a cellular manufacturing system. Integration of job sequencing with cell formation can attain lower make-spans. The traditional cell formation and scheduling problems consider performance indicators such as productivity, time and flexibility in cellular manufacturing system; however, energy consumption has not been given due attention. Therefore, this research addressed the minimization of total energy consumption by implementing an energy-efficient schedule at the cell formation stage of cellular manufacturing system. For this purpose, a two-phase approach is proposed; in phase I, formation of independent cells is being carried out by considering energy-efficient routings and genetic algorithm is used for improving search performance. In phase II, a formulation is being developed to compute the total energy of the system based on optimal job sequence with respect to minimum idle running of the machines in each independent cell. For the proposed approach, a code is being developed in MATLAB software. Different sample problems have been evaluated. The results showed that the proposed approach is effective in generating independent cells and sequences with minimum energy consumption and make-span.