2 research outputs found
Novel control of a high performance rotary wood planing machine
Rotary planing, and moulding, machining operations have been employed within the
woodworking industry for a number of years. Due to the rotational nature of the machining
process, cuttermarks, in the form of waves, are created on the machined timber surface. It is
the nature of these cuttermarks that determine the surface quality of the machined timber. It
has been established that cutting tool inaccuracies and vibrations are a prime factor in the
form of the cuttermarks on the timber surface. A principal aim of this thesis is to create a
control architecture that is suitable for the adaptive operation of a wood planing machine in
order to improve the surface quality of the machined timber.
In order to improve the surface quality, a thorough understanding of the principals of wood
planing is required. These principals are stated within this thesis and the ability to manipulate
the rotary wood planing process, in order to achieve a higher surface quality, is shown. An
existing test rig facility is utilised within this thesis, however upgrades to facilitate higher
cutting and feed speeds, as well as possible future implementations such as extended cutting
regimes, the test rig has been modified and enlarged. This test rig allows for the dynamic
positioning of the centre of rotation of the cutterhead during a cutting operation through the
use of piezo electric actuators, with a displacement range of Ā±15Ī¼m.
A new controller for the system has been generated. Within this controller are a number of
tuneable parameters. It was found that these parameters were dependant on a high number
external factors, such as operating speeds and runāout of the cutting knives. A novel approach
to the generation of these parameters has been developed and implemented within the
overall system.
Both cutterhead inaccuracies and vibrations can be overcome, to some degree, by the vertical
displacement of the cutterhead. However a crucial information element is not known, the
particular displacement profile. Therefore a novel approach, consisting of a subtle change to
the displacement profile and then a pattern matching approach, has been implemented onto
the test rig.
Within the pattern matching approach the surface profiles are simplified to a basic form. This
basic form allows for a much simplified approach to the pattern matching whilst producing a
result suitable for the subtle change approach. In order to compress the data levels a Principal
Component Analysis was performed on the measured surface data. Patterns were found to be
present in the resultant data matrix and so investigations into defect classification techniques
have been carried out using both KāNearest Neighbour techniques and Neural Networks.
The application of these novel approaches has yielded a higher system performance, for no
additional cost to the mechanical components of the wood planing machine, both in terms of
wood throughput and machined timber surface quality
Some effects of database corruption in system prediction performance
Many types of intelligent adaptive systems use
vast databases of a-priori knowledge during training
phases. These systems are then reliant on both the accuracy
of this data and on the breadth of the data. It is assumed
whilst training that the data encompasses the total
operating window for the system in enough detail to
generate an accurate āblack boxā model of the plant under
control. It may be that under certain unforeseen operating
conditions, or in a scenario where there is little prior
knowledge, the system may be forced to operate outside the
scope of the original a-priori knowledge. Lastly the data
gathered into the a-priori source may have been
unintentionally corrupted. This paper aims to examine some
of these effects upon two common adaptive intelligent tools,
neural networks and an adaptive neuro-fuzzy inference
system, ANFIS, network