Search

This paper presents a methodology of machined surface error compensation based on an inspection database by using an on-machine measurement system in profile milling. First, we make compensation for the geometric errors of the machining center by using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also taken into account. We manufacture a specimen workpiece and then inspect machined surface error distribution. In order to efficiently analyze the surface errors, two characteristic surface error parameters Werr and Derr are defined. Subsequently, it is possible to fit these parameters by using polynomial functions. These polynomial functions allow us to determine the surface errors in the domain of modeled cutting conditions. Based on these functional relationships between cutting conditions and surface errors, we try to correct the tool path in order to effectively reduce the errors by using an iterative algorithm. An iterative algorithm allows us to integrate changes of the cutting conditions according to the corrected tool path. To compensate for the surface errors, manufacturing tolerances are also taken into account. Experimentation is carried out in order to validate the approaches proposed in this paper. [Copyright &y& Elsevier]

Bearing is an important machine detail that appears in almost all mechanical systems. Estimating its operating condition online in order to hold the initiative in exploiting the systems, therefore, is one of the most urgent requirements. In this paper, we propose an online bearing damage identifying method named ASSBDIM based on adaptive neuro-fuzzy inference system (ANFIS), singular spectrum analysis (SSA), and sparse filtering (SF). It is an online process with offline and online phases. In the offline phase, by applying SSA and SF to the measured data stream typed big data with noise, both preprocessing data and extracting valuable information are implemented to build two offline databases signed Off_DaB and Off_testDaB. The ANFIS identifies the dynamic response of the mechanical system via Off_DaB. Based on Off_testDaB, the parameters of the ASSBDIM are then optimized. In the online phase, at each time, another database called On_DaB is built in the way similar to the one used for building the input space of the two offline databases. On_DaB participates as inputs of the ANFIS to estimate its outputs, which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated machining error compensation method based on polynomial neural network (PNN) approach and inspection database of on-machine-measurement (OMM) system. To improve the accuracy of the OMM system, geometric errors of the CNC machining center and probing errors are compensated. Machining error distributions of a specimen workpiece are measured to obtain error compensation parameters. To efficiently analyze the machining errors, two machining error parameters, Werr and Derr , are defined. Subsequently, these parameters can be modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Consequently, by using an iterative algorithm, tool path can be corrected to effectively reduce machining errors in the end-milling process. Required programs are developed using Ch language, and modified termination method are applied to reduce computation times. Experiments are carried out to validate the approaches proposed in this paper. The proposed integrated machining error compensation method can be effectively implemented in a real machining situation, producing much fewer errors. [Copyright &y& Elsevier]

In this paper, the design of an adaptive optimal fuzzy sliding controller (AOFSC) for semi-active magnetorheological damper (MRD) vehicle suspension system subjected to uncertainty and disturbance (UAD) whose time-varying rate may be high but bounded is presented. This is a combination of an adaptive optimal fuzzy sliding mode controller (FSMC op ), a nonlinear disturbance observer (NDO) and an inverse MRD model to create the desired control force, including the compensation part for UAD. An adaptive control law is proposed to establish a fuzzy-gain update law for the AOFSC and a parameter tie for the NDO such that the convergence of system's dynamic response is a Lyapunov asymptotically stable process. Surveys via simulations along with via an experimental apparatus showed that the high capability to exterminate vibration, robust stability, and the economic efficiency are the main advantages of the proposed AOFSC. [ABSTRACT FROM AUTHOR]