Ultra-Precision Polishing of N-Bk7 Using an Innovative Self-Propelled Abrasive Fluid Multi-Jet Polishing Tool

As the demand for optical glasses has increased, precision requirements for specific shapes, forms, surface textures, and sizes (miniaturization) have also increased. The standards and surface finishes applied to the reference mirrors used in measuring appliances are crucial. Hence, enhancements in figuring and surface finishing are indispensable to manufacturing industries. In this article, a novel self-propelled multi-jet abrasive fluid polishing technique is proposed for an ultra-precision polishing process in which a blade-less Tesla turbine was used as a prime mover. The turbine was characterized by high swirling velocity at the outlet; therefore, high levels of kinetic energy moving away from the turbine were used as polishing energy. Computational fluid dynamics (CFD) was also used to simulate the flow on the turbine blades. With a newly designed and manufactured polishing tool, the optimal polishing parameters for improving the surface roughness of crown optical glasses (N-BK7) were investigated. Taguchi's experimental approach, an L₁₈ orthogonal array, was employed to obtain the optimal process parameters. An analysis of variance (ANOVA) was also conducted to determine the significant factors. The surface roughness has been improved by approximately 94.44% from (R_a) 0.36 μm to (R_a) 0.02 μm. This study also presents a discussion on the influence of significant factors on improving surface roughness.     

Self-Management Framework for Mobile Autonomous Systems

The advent of mobile and ubiquitous systems has enabled the development of autonomous systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application-domains comes a new challenge—enabling self-management in mobile autonomous systems. Autonomous systems have to be able to manage themselves individually as well as form self-managing teams which are able to adapt to failures, protect themselves from attacks and optimise performance. This paper proposes a novel distributed policy-based framework that enables autonomous systems of varying scale to perform self-management individually and as a team. The framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach for optimal role assignment, caters for communication link maintenance amongst team-members and recovery from failure. Adaptive management is achieved by employing a policy-based architecture to enable dynamic modification of the management strategy relating to resources, role behaviour, communications and team management, without interrupting the basic software within the system. Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems involved in the mission. It is also optimal with respect to role assignments, and robust to intermittent communication link and permanent team-member failures.