Decentralized neural network variable structure controller design for wood drying process
This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating decentralized variable structure control (DVSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description of the dynamics of the wood drying process by means of the time-delay neural network is also presented, in which the back-propagation algorithm was implemented for testing, training and validation. Then this identified model is used for simulation purpose and controller design. For comparison purpose, a conventional proportional-integral-derivative (PID) controller is also employed and system performance is evaluated through simulations. The results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln and the DVSC strategy promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying.
Modeling of temperature-humidity for wood drying based on time-delay neural network
The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model, which revealed the relation between controlling signal and temperature-humidity and the relation between wood moisture content and temperature-humidity of wood drying, were separately presented. The models were simulated by using the measured data of the experimental drying kiln. The numerical simulation results showed that the modeling method was feasible, and the models were effective.
Traceability strategy for gear-pitch-measuring instruments: development and calibration of a multiball artifact
There is a strong demand to ensure the traceability of gear-measuring instruments (GMIs). We propose a multiball artifact (MBA) for the evaluation of pitch-measuring accuracy. The aim of the MBA is to transfer the minimum uncertainty from a calibrated value at the National Metrology Institute of Japan (NMIJ) to a measured value of GMIs at a shop floor. The MBA is composed of equally spaced high-accuracy balls around an axis. The pitch-measuring accuracy of GMIs is evaluated by measuring the angular pitch deviation of the balls instead of the angular pitch deviation of a gear. We calibrated the angular pitch deviation using a coordinate measuring instrument (CMM) at the NMIJ and adapting a multiple-orientation technique. We proposed a calibration strategy of the angular pitch deviation. The calibration value of the pitch deviation for the MBA at the NMIJ was obtained with a measurement uncertainty (U95) of 0.2 µm. We evaluated the pitch-measuring accuracy of a GMI using the calibrated MBA. Each value of U95 for the cumulative and single pitch deviations for the left and right flanks was less than or equal to 0.5 µm. The small uncertainty was transferred from the NMIJ to the GMI.