Adaptive CNC Machining Process Optimization of Near- net- shaped Blade based on Machining Error data Flow Control

Adaptive CNC machining process is one of the efficient processing solution for near- net- shaped blade, this study proposes an adaptive computer numerical control (CNC) machining process optimization scheme based on multi-process machining errors data flow control. The geometric and mechanical models of the multi-process adaptive CNC machining process are firstly constructed. The multi-process machining error data flow and the process system stiffness of near- net- shaped blade are then experimentally explored. The machining error flow collaborative control of the near- net- shaped blade multi-process CNC machining is finally realized by the adaptive CNC machining process under the premise of sufficient stiffness of the blade- fixture system. The results show that the dynamic displacement response of the blade multi-process CNC machining process is controlled within 0.007mm. The optimized adaptive CNC machining process based on the multi-process geometric machining error data flow control and the sufficient stiffness of blade- fixture system can realize the multi-process machining error control and high-precision machining of near- net- shaped blade. The process chain of the optimized adaptive CNC machining process is reduced by 87% compared with the low melting point alloy pouring process and 50% compared with adaptive CNC machining process of the twice on-machine measurement on the blade body.

User Privacy and Data Flow Control for Android Apps: Systematic Literature Review

Android mobile apps gain access to numerous users’ private data. Users of different Android mobile apps have less control over their sensitive data during their installation and run-time. Too often, these apps consider data privacy less serious than users’ expectations. Many mobile apps misbehave and upload users’ data without permission which confirmed the possibility of privacy leakage through different network channels. The literature has proposed various approaches to protect user’s data and avoid privacy violations. In this paper, we provide a comprehensive overview of state-of-art research on Android user privacy, and data flow control. the aim is to highlight the main trends, pinpoint the main methodologies applied, and enumerate the privacy violations faced by Android users. We also shed some light on the directions where the researcher’s community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we surveyed 114 relevant research papers published in leading conferences and journals. Our thorough examination of the relevant literature has led to a critical analysis of the proposed solutions with a focus on user privacy extensions and mechanism for the Android mobile platform. Furthermore, possible solutions and research directions have been discussed.