This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation.

No abstract

No abstract

No abstract

The high cost of inertial units is the main obstacle for their inclusion in precision navigation systems to support a variety of application areas. Standard inertial navigation systems (INS) use precise gyro and accelerometer sensors; however, newer inertial devices with compact, lower precision sensors have become available in recent years. This group of instruments, called motion sensors, is six to eight times less costly than a standard INS. Given their weak stand-alone accuracy and poor run-to-run stability, such devices are not usable as sole navigation systems. Even the integration of a motion sensor into a navigation system as a supporting device requires the development of non-traditional approaches and algorithms. The objective of this paper is to assess the feasibility of using a motion sensor, specifically the MotionPak™, integrated with DGPS and DGLONASS information, to provide accurate position and attitude information, and to assess its capability to bridge satellite outages for up to 20 seconds. The motion sensor has three orthogonally mounted ‘solid-state’ micro- machined quartz angular rate sensors, and three high performance linear servo accelerometers mounted in a compact, rugged package. Advanced algorithms are used to integrate the GPS and motion sensor data. These include INS error damping, calculated platform corrections using DGPS (or DGPS/DGLONASS) output, velocity correction, attitude correction and error model estimation for prediction. This multi-loop algorithm structure is very robust, which guarantees a high level of software reliability. Vehicular and aircraft test trials were conducted with the system in land vehicle mode and the results are discussed. Simulated outages in GPS availability were made to assess the bridging accuracy of the system. Results show that a bridging accuracy of up to 3 m after 10 seconds in vehicular mode and a corresponding accuracy of 6 m after 20 seconds in aircraft mode can be obtained, depending on vehicle dynamics and the specific MotionPak™ unit used. The attitude accuracy was on the order of 22 to 25 arcmin for roll and pitch, and about 44 arcmin for heading.