Ultimate tensile strength of SMMC B

Ultimate tensile strength of SMMC B4C-160 μm is 527 Mpa. The above expressions are used to calculate the penetration depth of projectile intoTarget-1 A similar approach was adopted for evaluating the penetration depth in SMMC B4C-60 μm and SMMC-30μm, and the penetration depth of 40.5mmand 38.17 mm were obtained, respectively. An attempt was made to introduce an empirical constant such that analytical obtained depth of penetration approach experimental one.
Results and discussion
Conclusions
Acknowledgement The authors would like to thank Director, Defence Metallurgical Research Laboratory and Hyderabad, India for his continued encouragement and permission to publish this work. Financial assistance from Armament research board, New Delhi, India is gratefully acknowledged.
Introduction The main mission of missile autopilot is to track the guidance commands with a guaranteed level of system performance. In order to successfully achieve this mission, the performance characteristics of the autopilot must have a fast response to intercept a maneuvering target and reasonable robustness for system prostanoid receptors under the effect of un-modeled dynamics and noise. Basically, the concept of open-loop transfer function is the cornerstone of feedback control system analysis, where the relative stability and the robustness can be determined from analysis of the stability margins. However, Ref. [1] shows that ignoring the value of open-loop crossover frequency in the design procedure, even with good phase and gain margins, will cause design instability for relatively innocuous plant perturbations. In fact, this design may cause too high crossover frequency, which indicates that the system may go unstable when it is built and tested. Moreover, Ref. [2] concludes that the concept of open-loop gain and phase margins is not as useful realistically at high frequency design due to the increase of model non-linearity, which leads to considerable difference between the predicted gain and phase values and their real values at high frequency. A common approach to address this problem is by modifying the crossover frequency value to make sure that the open-loop gain is below some desired level at high frequencies. This value is set based on the assumptions about the high-frequency modeling errors, sometimes based on test data, and often comes from hard-learned experience. A classical “rule of thumb” that addressed this value is introduced in Refs. [1,3]. As a result, the crossover frequency is an important parameter in gain design process to achieve good trade-off between fastness and robustness. Nevertheless, in multi-loop autopilot design different gain combinations could meet the same open-loop crossover frequency with different flight performances. Consequently, different methods and strategies have been implemented by researchers in order to introduce the open-loop frequency requirements into the autopilot design procedure. From optimal design prospective, some methods are considered as weight adjusted LQR technique for the objective of minimum error between desired and actual open loop crossover frequency [4–7]. Although it is possible to get the same crossover frequency for different gain designs, these techniques take the prescribed crossover frequency as the design goal; this scheme will not essentially guarantee an ideal autopilot. Besides, it is based on initial guessing of weights which might need to be carried out and repeated many times to adjust the required initial performance. In a different way, the multi-objective optimization technique is introduced in Ref. [8], where both time and frequency performance design aspects are combined into one objective function through multi-weight technique. Even so, this method optimizes the autopilot to a certain specified performance level with the challenge of objective\'s weight adjustment. Moreover, Ref. [9] introduces a dynamic inversion technique, which uses a constraint optimization algorithm to get the design parameters for the autopilot system. However, this method is considered for a specified controller structure with some assumptions and totally numerical procedure. In addition, the system stability is confirmed by the inequality constraints on gain and phase margins and minimum controller cycle, which may perform a hard optimization problem with some performance degradation.