Study on Performance of Remanufactured Engine Based on EMD
According to the non-stationary property of vibration signal, the vibration signal is decomposed by empirical mode decomposition (EMD). And this method is applied to study the remanufacturing level of engine. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF (Intrinsic Mode Function) components and original signal, and dynamic structure of IMF components is analyzed by correlation dimension. In order to study the remanufacturing level of engine, correlation coefficient and correlation dimension are considered in the function of IMF, and an index of vibration intensity is proposed. A corresponding relationship is established between remanufacturing level and vibration intensity. This method has been proved that the running state of engine can be reflected by vibration intensity, and this index can be used to evaluate the remanufacturing level of engine. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3896
Study on Iterative Learning Control bandwidth tuning using particle swarm optimization technique for high precision motion
This paper utilized the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, Iterative Learning Control (ILC) and the bandwidth of zero-phase Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and try to learn the error signals. However the learnable and unlearnable error signals should be separated for bettering control process as repetition goes. Producing high frequency error condition should be avoided before the phase margin caused any trouble. The filter bandwidth should be changed at every repetition. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as repetition goes. The correlation coefficient validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in next repetition. The learnable error signals of the Intrinsic Mode Functions (IMFs) through the Empirical Mode Decomposition (EMD) correlate efficiently with reduced tracking error as repetition goes. Simulation results validate the application for positioning of a robot arm system for high precision motion control.
An automated GD&T inspection system based on non-contact 3D digitization
Rapid developments and improvements in advanced manufacturing technology mean that the inspection process plays an important role in the quality control of manufactured products. The Geometric Dimension and Tolerance (GD&T) inspection process is used in industries to examine the conformity of manufactured parts with the part specification defined at the design stage. However, as the modern manufacturing trend is towards producing low-volume, high-variety, high-complexity, and large products, current inspection techniques show difficulties in coping with this. This is especially true in automatically inspecting the geometric tolerance items of a manufactured part, such as parallelism and perpendicularity. This paper proposes a non-contact digitization-based automated GD&T inspection system. In this study, we defined a Nominal Inspection Frame (NIF) for a nominal CAD model or a digitized model, in which every GD&T item required may be defined and specified. Using the NIF, an algorithm for automatically extracting the GD&T information on a measured model was developed and implemented in a Reverse Engineering (RE) environment with software. We examine this proposed approach with several applications on dimension tolerance, geometric tolerance, and free-form surface inspection, and discuss the inspection results in the paper.