throbber
WORKING PROCESS MONITORING DEVICE
`
`Field of the invention
`
`The present
`
`invention relates to a working process
`
`monitoring device; and, more particularly,
`
`to a monitoring
`
`device
`
`capable of determining whether
`
`a material of
`
`a
`
`workpiece is normal or not while the workpiece is being
`
`processed by a working machine driven by a driving source.
`
`10
`
`Background of the Invention
`
`Conventionally,
`
`there
`
`has
`
`been
`
`known
`
`a
`
`technique
`
`determining whether an anomaly exists in a machining process
`
`15
`
`by analyzing frequencies or amplitudes of
`
`sounds produced
`
`while
`
`a machine
`
`is machining
`
`a workpiece
`
`(see,
`
`e.g.,
`
`Japanese Patent Laid—open .Application No.2001—87863). The
`
`Japanese Patent Application discloses a technique detecting
`
`an anomaly occurring during the machining process due to an
`
`20
`
`anomaly in a gas or
`
`consumption of
`
`supply items while
`
`machining a plate by using a plasma torch.
`
`Since,
`
`however,
`
`the
`
`technique
`
`disclosed
`
`in
`
`the
`
`Japanese Patent Application detects
`
`an
`
`anomaly
`
`in the
`
`machining' process only from frequencies or amplitudes of
`
`25
`
`sounds produced by the machining operation, variations in
`
`the material
`
`of
`
`the workpiece
`
`can
`
`not
`
`be
`
`detected.
`
`

`

`Therefore,
`
`it
`
`cannot properly deal with such situations
`
`where
`
`the workpice contains
`
`a
`
`foreign substance therein
`
`and/or machining conditions of the workpiece are changed due
`
`to nonuniformity in the material of
`
`the workpiece,
`
`for
`
`example.
`
`Summary of the Invention
`
`10
`
`15
`
`20
`
`25
`
`In view of the above,
`
`the present
`
`invention provides a
`
`working process monitoring device
`
`capable
`
`of detecting
`
`variations in the material of
`
`aa workpiece which is being
`
`machined,
`
`so that it can deal with such situations where the
`
`workpiece contains a foreign substance and/or there exists
`
`nonuniformity in the material of the workpiece,
`
`for example.
`
`In accordance with an aspect of the present invention,
`
`a working process monitoring device includes: a sensor unit
`
`for detecting at
`
`least one of vibrations and sound waves
`
`produced while processing a workpiece by a working machine
`
`rotatably driven by a driving unit; a signal
`
`input unit for
`
`extracting target signals from an electric signal outputted
`
`from the
`
`sensor unit;
`
`and an amount of characteristics
`
`extracting unit
`
`for extracting amounts of characteristics
`
`including a plurality of parameters from the target signals.
`
`The working' process monitoring device further
`
`includes
`
`a
`
`material inspecting unit for detecting whether a material of
`
`a portion of
`
`the workpiece currently being processed is
`
`

`

`normal
`
`in quality or not.
`
`The material
`
`inspecting unit
`
`employs
`
`a neural
`
`network
`
`trained by using amounts
`
`of
`
`characteristics extracted from target signals obtained while
`
`processing a workpiece made of a normal quality material.
`
`In this configuration,
`
`the amounts of characteristics
`
`are extracted from vibrations and/or sound waves generated
`
`from the working machine,
`
`amd it is detected whether
`
`the
`
`material of
`
`the workpiece
`
`is normal
`
`in quality or not
`
`through the use of
`
`the neural network trained by using the
`
`10
`
`amounts of characteristics obtained with respect
`
`to the
`
`workpiece made of normal quality material. Therefore, it is
`
`possible to detect a possibility of breakdown of the working
`
`machine caused by, e.g., a foreign substance stuck into the
`
`workpiece or
`
`the nonuniformity in the material of
`
`the
`
`15
`
`workpiece.
`
`It
`
`is preferable that
`
`the workpiece is wood,
`
`and the
`
`working machine
`
`is a cutting device having a
`
`saw blade
`
`driven by the driving unit. Moreover,
`
`the working process
`
`monitoring
`
`device
`
`may
`
`further
`
`include
`
`a machining
`
`20
`
`terminating unit which terminates machining operation of the
`
`workpiece when the material
`
`inspecting unit detects that the
`
`material of the portion of the workpiece is not normal.
`
`In this configuration,
`
`if there is a possibility that
`
`saw teeth of
`
`the saw blade could be damaged due to a hard
`
`25
`
`foreign substance such as a piece of stone stuck into the
`
`workpiece or due to the nonuniformity in the material of the
`
`

`

`workpiece, or if proper cutting of
`
`the workpiece cannot be
`
`made due to a knot or a knot hole,
`
`the machining operation
`
`is terminated to prevent
`
`the occurrence of such situations
`
`which could take place otherwise.
`
`It
`
`is also preferable that
`
`the
`
`saw blade may be
`
`disposed at
`
`the working machine to cut
`
`the workpiece across
`
`wood grain thereof, and at least one of a cutting speed and
`
`a relative feed speed between the workpiece and the saw
`
`blade is controllable,
`
`early' wood. portions and late wood
`
`10
`
`portions of
`
`the workpiece being treated as normal material
`
`portions
`
`among portions being machined.
`
`Moreover,
`
`the
`
`working process monitoring device further includes a speed
`
`control unit which. controls at
`
`least one of
`
`the cutting
`
`speed and the feed speed so that an amount of contact per
`
`15
`
`unit time that saw teeth of the saw blade is in contact with
`
`the workpiece while cutting the
`
`late wood portions
`
`is
`
`greater than that while cutting the early wood portions.
`
`In this configuration,
`
`the material
`
`inspecting unit is
`
`trained to distinguish the early wood portions and the late
`
`wood portions which are harder
`
`than the former,
`
`and the
`
`amount of contact per unit time of the saw teeth of the saw
`
`blade with respect
`
`to the
`
`late wood portions are made
`
`greater than that with respect
`
`to the early wood portions.
`
`Therefore, cutting of the workpiece can be facilitated.
`
`In order
`
`to adjust
`
`the amount of contact of
`
`the saw
`
`teeth of
`
`the saw blade to a workpiece, at
`
`least one of a
`
`20
`
`25
`
`

`

`feed speed of
`
`the saw teeth of the saw blade (a rotational
`
`speed in case of a circular saw, and driving speed in case
`
`of a band saw) and a feed speed of the workpiece relative to
`
`that
`
`of
`
`the
`
`saw blade
`
`(mostly,
`
`the velocity of
`
`the
`
`workpiece)
`
`is adjusted.
`
`Cutting can be
`
`facilitated by
`
`adjusting the amount of contact of the saw teeth of the saw
`
`blade to the late wood portions to be greater than that
`
`to
`
`the early wood portions which are softer than the late wood
`
`portions.
`
`Further,
`
`the neural network may be
`
`a competitive
`
`learning neural network.
`
`Since the competitive learning neural network is used
`
`in this embodiment,
`
`simple configuration is possible and,
`
`moreover,
`
`learning can be simply carried out by colleting
`
`the training samples with respect
`
`to every category and
`
`assigning the training samples to the respective categories.
`
`Brief Description of the Drawings
`
`The objects and features of the present
`
`invention will
`
`become
`
`apparent
`
`from the
`
`following
`
`description
`
`of
`
`embodiments
`
`given
`
`in conjunction with the
`
`accompanying
`
`drawings,
`
`in which:
`
`Fig.
`
`l
`
`is
`
`a block diagram of
`
`a working process
`
`monitoring device in accordance with an embodiment of
`
`the
`
`present invention;
`
`10
`
`15
`
`2O
`
`25
`
`

`

`Fig.
`
`2
`
`illustrates a
`
`schematic configuration of
`
`a
`
`neural network used in the embodiment; and
`
`Fig.
`
`3 describes a relation between a workpiece and a
`
`saw blade in the embodiment.
`
`Detailed Description of the Embodiments
`
`Embodiments
`
`ofV the present
`
`invention will
`
`now be
`
`described with reference to the accompanying drawings which
`
`10
`
`form a part hereof.
`
`A working machine
`
`exemplified
`
`in
`
`an
`
`embodiment
`
`described below has
`
`a
`
`saw blade rotatably driven by a
`
`driving unit
`
`including driving source such as a motor.
`
`The
`
`saw blade may be a circular saw or a band saw.
`
`The working
`
`15
`
`machine may be an agitation apparatus, a mixing apparatus,
`
`an extrusion molding apparatus for molding a resin, or the
`
`like.
`
`In case of
`
`the agitation apparatus or
`
`the mixing
`
`apparatus,
`
`it
`
`is possible to detect
`
`abnormal vibrations
`
`caused. by, e.g.,
`
`foreign substances mixed.
`
`into a
`
`target
`
`20
`
`material
`
`to be agitated or mixed,
`
`and in case of
`
`the
`
`extrusion molding apparatus,
`
`it becomes possible to detect
`
`the nonuniformity in the resin.
`
`In case of
`
`the agitation
`
`apparatus and the mixing apparatus, vibrations of a rotation
`
`shaft may be detected, while,
`
`in case of
`
`the extrusion
`
`25
`
`molding apparatus, an extrusion pressure may be detected by
`
`a pressure sensor .
`
`

`

`A working process monitoring device shown in Fig.
`
`1 in
`
`accordance with
`
`the
`
`present
`
`embodiment
`
`employs
`
`unsupervised
`
`competitive
`
`learning
`
`neural
`
`network
`
`an
`
`1a
`
`(hereinafter,
`
`simply referred to as a neural network if not
`
`otherwise necessary).
`
`A.
`
`supervised backpropagation type
`
`neural network can be also used as a neural network in the
`
`embodiment, but
`
`the unsupervised competitive learning neural
`
`network is preferred because it is simpler in configuration
`
`than the supervised back propagation type.
`
`10
`
`As
`
`shown in Fig.
`
`2,
`
`the neural network 1a has
`
`two
`
`layers,
`
`i.e , an input
`
`layer 11 and an output
`
`layer 12, and
`
`is configured such that every neuron N2 of the output
`
`layer
`
`12 is connected to all neurons N1 of the input
`
`layer 11.
`
`In
`
`the embodiment,
`
`the neural network 1a may be executed by an
`
`15
`
`application program running at a sequential processing type
`
`computer, but a dedicated neuro—computer may be used.
`
`The neural network la has
`
`two modes of operations,
`
`i.e.,
`
`a training mode and a checking mode. After learning
`
`through proper
`
`training samples
`
`in the training mode,
`
`an
`
`20
`
`amount of characteristics (check data)
`
`formed of a plurality
`
`of parameters generated from an actual
`
`target
`
`signal
`
`is
`
`classified into a category in the checking mode.
`
`A coupling degree
`
`(weight coefficients)
`
`between.
`
`the
`
`neurons N1 of
`
`the input
`
`layer 11 and the neurons N2 of the
`
`25
`
`output
`
`layer
`
`12
`
`is variable.
`
`In.
`
`the training' mode,
`
`the
`
`neural network 1a is trained by inputting training samples
`
`

`

`to
`
`the
`
`neural
`
`network
`
`1a
`
`so
`
`that
`
`respective weight
`
`coefficients between the neurons N1 of
`
`the input
`
`layer 11
`
`and the neurons N2 of
`
`the output
`
`layer 12 are decided.
`
`other words,
`
`every neuron N2 of
`
`the output
`
`layer
`
`12
`
`In
`
`is
`
`assigned with a weight vector having weight coefficients
`
`associated with all the neurons N1 of the input
`
`layer 11 as
`
`elements of the weight vector. Therefore,
`
`the weight vector
`
`has the same number of elements as the number of neurons N1
`
`in the input
`
`layer 11, and the number of parameters of
`
`the
`
`10
`
`amount of characteristics inputted to the input
`
`layer 11 is
`
`equal to the number of the elements of the weight vector.
`
`Meanwhile,
`
`in the checking mode, when check data whose
`
`category needs to be decided is given to the input
`
`layer 11
`
`of
`
`the neural network 1a,
`
`a neuron having the shortest
`
`15
`
`Euclidean distance between.
`
`the its weight vector and the
`
`check data,
`
`is excited among the neurons N2 of
`
`the output
`
`layer 12.
`
`If categories are assigned to the neurons N2 of
`
`the output
`
`layer 12 in the training mode, a category of the
`
`check data can be recognized by a category of a location of
`
`20
`
`the excited neuron N2.
`
`The neurons N2 of
`
`the output
`
`layer 12 are associated
`
`with zones of
`
`two—dimensional cluster determination unit 1b
`
`having, e.g.,
`
`6
`
`*
`
`6
`
`zones
`
`in one—to—one correspondence.
`
`Therefore,
`
`if
`
`categories
`
`of
`
`the
`
`training samples
`
`are
`
`25
`
`associated with the zones of the cluster determination unit
`
`1b, a category corresponding to a neuron N2 excited by check
`
`

`

`data can be recognized by the cluster determination unit lb.
`
`Thus,
`
`the cluster determination unit
`
`lb can function as an
`
`output unit for outputting a classified result. Here,
`
`the
`
`cluster determination unit
`
`lb may be visualized by using a
`
`map.
`
`When associating categories with each of
`
`the zones of
`
`the cluster determination unit
`
`lb (actually each of
`
`the
`
`neurons N2 of
`
`the output
`
`layer 12),
`
`trained neural network
`
`la is operated in the reverse direction from the output
`
`10
`
`layer 12 to the input
`
`layer 11 to estimate data assigned to
`
`the input
`
`layer 11 for every neuron N2 of
`
`the output
`
`layer
`
`12.
`
`A category of a training sample having the shortest
`
`Euclidean. distance with respect
`
`to the estimated. data is
`
`used. as
`
`a category of
`
`a corresponding' neuron N2
`
`in the
`
`15
`
`output layer 12.
`
`In other word, a category of a training sample having
`
`the shortest Euclidean. distance with respect
`
`to a 'weight
`
`vector of
`
`a neuron N2
`
`is used as
`
`a
`
`category of
`
`-the
`
`corresponding neuron N2 of the output layer 12. As a result,
`
`20
`
`the categories of the training samples are reflected to the
`
`categories of the neurons N2 of the output layer 12.
`
`A large number of
`
`training samples
`
`(for example,
`
`150
`
`samples)
`
`are employed to each of
`
`the categories so that
`
`categories having similar attributes
`
`are
`
`arranged close
`
`25
`
`together
`
`in the cluster determination unit
`
`lb.
`
`In other
`
`words,
`
`the neurons N2, excited among the neurons N2 of
`
`the
`
`

`

`output layer 12 in response to training samples belonging to
`
`a like category,
`
`form a cluster formed of a group of neurons
`
`N2 residing close together in the cluster determination unit
`
`1b.
`
`Cluster determination unit lb is originally the one in
`
`which clusters are formed in association. with. categories
`
`after training, but
`
`in this embodiment even the one before
`
`training is also called a cluster determination unit 1b so
`
`that both of
`
`them are not distinguished in this context.
`
`The
`
`training samples
`
`given
`
`to the neural
`
`network
`
`1a
`
`operating in the training mode are stored in a
`
`training
`
`sample storage 5 and retrieved therefrom to be used in the
`
`neural network la when necessary.
`
`In this embodiment,
`
`the neural network la is used for
`
`detecting' material quality’ of a 'workpiece which.
`
`is being
`
`processed. Therefore,
`
`the neural network la and the cluster
`
`determination unit 1b function as a material inspecting unit
`
`1.
`
`At
`
`least one of vibrations or
`
`sound. waves generated
`
`while the working machine X is processing a workpiece is
`
`used as
`
`information when
`
`the material
`
`inspecting ‘unit
`
`1
`
`detects
`
`the material quality of
`
`the workpiece W.
`
`When
`
`detecting vibrations of
`
`the working machine X,
`
`a Vibration
`
`sensor formed of an acceleration pickup is attached to the
`
`working machine X to be used as a sensor unit 2.
`
`Further,
`
`when detecting sound waves generated while the workpiece is
`
`being processed, a microphone or an acoustic emission sensor
`
`-10-
`
`10
`
`15
`
`20
`
`25
`
`

`

`may be used as
`
`the sensor unit 2.
`
`Here,
`
`the vibration
`
`sensor is assumed to be used as the sensor unit 2.
`
`An electric signal is outputted from the sensor unit 2
`
`are inputted to a signal
`
`input unit 3, and is segmented by
`
`the signal input unit 3 to produce target signals from which
`
`amounts
`
`of
`
`characteristics
`
`are
`
`extracted. An extracted
`
`amount of characteristics varies depending on a position in
`
`the output of the sensor unit
`
`2 on a time axis,
`
`from which
`
`position the
`
`amount of characteristics being extracted.
`
`10
`
`Therefore,
`
`prior
`
`to
`
`the
`
`extraction
`
`of
`
`amounts
`
`of
`
`characteristics,
`
`the signal input unit 3 establishes,
`
`in the
`
`output of
`
`the
`
`sensor unit
`
`2, positions
`
`from which the
`
`amounts of characteristics are extracted in order to extract
`
`the
`
`amounts
`
`of characteristics
`
`under
`
`the
`
`substantially
`
`15
`
`identical condition.
`
`In this embodiment,
`
`the signal
`
`input unit
`
`3 performs
`
`segmentation, which will be described later, on the electric
`
`signal which is the output of
`
`the sensor unit 2,
`
`and the
`
`segmented portions in the electric signal are used as the
`
`target
`
`signals.
`
`The
`
`target
`
`signals are assigned to an
`
`amount of characteristics extracting unit 4 and the amounts
`
`of characteristics each amount
`
`including a plurality of
`
`parameters are extracted from the target signals.
`
`The signal
`
`input unit
`
`3 performs the segmentation of
`
`the electric signal outputted from the sensor unit 2 on the
`
`time axis, e.g., by using a timing signal
`
`(trigger signal)
`
`20
`
`25
`
`_11_
`
`

`

`synchronous with the operation of
`
`the working machine X or
`
`based on wave characteristics of
`
`the electric signal
`
`(for
`
`example, a start point and an end point of an envelop of the
`
`electric signal).
`
`The
`
`signal
`
`input unit
`
`3 has
`
`an A/D converter
`
`for
`
`converting the electric signal produced by the sensor unit 2
`
`into digital signals and a buffer for temporarily storing
`
`the digital signals.
`
`The aforementioned segmentation is
`
`performed. on the signals stored.
`
`in.
`
`the buffer.
`
`Further,
`
`10
`
`limitation of a frequency bandwidth or the like is performed
`in
`order
`to
`reduce
`noises when necessary..
`In
`the
`
`segmentation of the electric signal, only a single segmented
`
`signal need not be outputted from one period of the electric
`
`signal, but a plurality of segmented signals may be made per
`
`15
`
`every proper unit time.
`
`The target signals segmented by the signal
`
`input unit
`
`3 are inputted to the amount of characteristics extracting
`
`unit 4.
`
`The amounts of characteristics extracting unit
`
`4
`
`extracts one set of amount of characteristics including a
`
`20
`
`25
`
`plurality of parameters from one target signal. The amounts
`
`of characteristics can be adaptively extracted according to
`
`characteristics to be considered in the target signals.
`
`For
`
`example,
`
`as
`
`for
`
`the amount of characteristics,
`
`frequency
`
`components of the whole frequency bandwidth detected by the
`
`sensor unit
`
`2
`
`(power at every frequency bandwidth) may be
`
`extracted, or
`
`frequency components of
`
`an envelop of
`
`the
`
`-12-
`
`

`

`electric signal detected by
`
`the
`
`sensor unit
`
`2 may be
`
`extracted.
`
`The amount of characteristics extracting unit 4
`
`may use FFT (Fast Fourier Transform)
`
`in order to extract the
`
`frequency components.
`
`Frequency components
`
`to be used in
`
`the amount of characteristics are properly selected.
`
`The
`
`amount
`
`of
`
`characteristics
`
`extracting unit
`
`4
`
`extracts amounts of characteristics,
`
`each amount having a
`
`plurality of parameters
`
`(frequency bandwidths),
`
`and the
`
`neural
`
`network
`
`la
`
`of
`
`the material
`
`inspecting unit
`
`1
`
`10
`
`classifies
`
`the
`
`amounts of characteristics.
`
`The neural
`
`network 1a classifies the amounts of characteristics to
`
`determine whether a material of the workpiece W is basically
`
`normal
`
`in quality or not.
`
`In the present embodiment,
`
`the
`
`workpiece is assumed to be made of wood,
`
`and thus both of
`
`15
`
`the early wood portions and the late wood portions forming
`
`growth rings are treated as normal quality materials.
`
`The early wood portions are light in density due to a
`
`high plant growth rate, while the late wood portions are
`
`dense in density due to a low plant growth rate. That
`
`is,
`
`20
`
`the
`
`late wood portions are harder
`
`than the early wood
`
`portions. Therefore, when the workpiece is cut by the saw
`
`blade S moving across wood grain G
`
`(i.e.,
`
`annual
`
`rings)
`
`formed in the cross section E of the wood of the workpiece W
`
`as
`
`shown in Fig.
`
`3,
`
`a cut
`
`resistance of
`
`the late wood
`
`25
`
`portions G1 is larger than that of
`
`the early wood portions
`
`G2.
`
`Such wood working corresponds to a case of dividing a
`
`_l3_
`
`

`

`piece of wood, i.e., a workpiece,
`
`in a shape of a board into
`
`desired sizes.
`
`The cut resistance of a knot
`
`is greater
`
`than that «of
`
`the late wood portions G1, while the cut
`
`resistance of a knot hole is less than that of
`
`the early
`
`wood portions G2. Further,
`
`if a foreign substance such as a
`
`piece of stone is stuck into a workpiece,
`
`the cut resistance
`
`becomes extremely large.
`
`In this embodiment,
`
`the late wood portions G1 and the
`
`early" wood portions G2 are determined as normal quality
`
`10
`
`materials, and a knot, a knot hole and a piece of stone are
`
`determined. as abnormal quality’ materials.
`
`Therefore,
`
`the
`
`neural network la needs to be trained by using amounts of
`
`characteristics obtained. while performing normal cutting,
`
`i e., while cutting the late wood portions G1 and the early
`
`15
`
`wood portions G2.
`
`Therefore,
`
`amounts of characteristics obtained. while
`
`cutting the workpiece, which have been determined to be of a
`
`normal quality material by the naked eye are stored in the
`
`training sample storage 5 stores as training samples. That
`
`is,
`
`the amounts of characteristics obtained by the amount of
`
`characteristics extracting unit
`
`4 are stored and collected
`
`in the training sample storage 5 as training samples prior
`
`to performing the training mode.
`
`It is preferable that
`
`the
`
`training samples are assigned with a category for the late
`
`wood portions G1 and that for the early wood portions G2.
`
`If the wood grain is not well developed in the wood material,
`
`20
`
`25
`
`-14-
`
`

`

`only a normal category can be assigned instead of categories
`
`for the late wood portions G1 and the early wood portions G2.
`
`After
`
`a
`
`specific number
`
`,of
`
`training samples
`
`(for
`
`example, 150 samples) are collected with respect
`
`to each of
`
`the categories in the training data storage 5,
`
`the neural
`
`network la in the training Inode
`
`is trained. by ‘using the
`
`training samples stored in the training sample storage 5.
`
`The data stored in the training sample storage 5 may
`
`be called data sets. As can be seen from the description
`
`provided above,
`
`the training sample storage 5 stores a data
`
`set
`
`to which the category of
`
`the late wood. portions
`
`is
`
`assigned and a data set
`
`to which the category of
`
`the early
`
`wood portions is assigned.
`
`The number of data constituting
`
`each data set can be arbitrarily decided within a capacity
`
`of the training sample storage 5. However, it is preferable
`
`that about 150 of data per each category are used to train
`
`the neural network la as described above.
`
`If the neural network 1a is trained as aforementioned,
`
`every neuron N2
`
`in the output
`
`layer 12 is assigned with a
`
`weight vector having the weight coefficients associated with
`
`all the neurons N1 of the input
`
`layer 11 as elements of the
`
`weight vector. Therefore,
`
`if a training sample belonging to
`
`a category is assigned.
`
`to the neural network 1a
`
`in the
`
`checking mode,
`
`a neuron N2 associated with the category is
`
`excited. Since, however,
`
`the training samples are different
`
`from each other,
`
`training samples
`
`(a data set)
`
`included in a
`
`-15_
`
`10
`
`15
`
`20
`
`25
`
`

`

`single category excite more than one neuron N2, which form a
`
`cluster.
`
`Therefore,
`
`in the cluster determination unit
`
`lb,
`
`the
`
`zones
`
`corresponding to the
`
`neurons
`
`N2
`
`excited by
`
`the
`
`training samples included in the respective categories are
`
`the
`
`zones
`
`representing normal quality of material with
`
`respect to the corresponding categories.
`
`In other words,
`
`if
`
`a neuron N2 belonging to a normal cluster is excited during
`
`the cutting operation of a wood material, it indicates that
`
`10
`
`the quality of
`
`the wood material
`
`is normal.
`
`On the other
`
`hands, when a neuron N2 which does not belong to the normal
`
`clusters is excited,
`
`it can be determined that
`
`the wood
`
`material contains a foreign substance,
`
`a knot, or a knot
`
`hole.
`
`15
`
`When
`
`the excited neuron. N2 does not belong to any
`
`clusters for the late wood portions G1 and the early wood
`
`portions G2,
`
`the saw blade S is most likely in contact with
`
`a foreign substance, a knot, or a knot hole,
`
`so that
`
`there
`
`is a possible that
`
`the saw blade S can be damage, or
`
`the
`
`20
`
`cutting may not be carried out properly. Therefore, cluster
`
`determination unit
`
`lb issues an instruction to a machining
`
`terminating unit
`
`6a provided at
`
`an output unit
`
`6
`
`to
`
`terminate the machining operation of the working machine X.
`
`As a result,
`
`the saw blade S is prevented from being damaged
`
`25
`
`and, moreover,
`
`the deterioration of machining precision due
`
`to a knot or a knot hole can be prevented.
`
`It may' be
`
`—16—
`
`

`

`preferable that an operator is notified of an existence of a
`
`foreign substance, a knot, or a knot hole when the material
`
`inspecting unit 1 detects it,
`
`so that proper maintenance can
`
`be made by the operator.
`
`As aforementioned,
`
`there exist
`
`the late wood portions
`
`G1 and the early wood portions G2 having different hardness
`
`in the wood grain (the annual
`
`rings) G.
`
`Therefore,
`
`it is
`
`preferable to vary amounts of contact per unit time between
`
`the saw teeth of the saw blade S and the wood portions while
`
`10
`
`cutting is performed.
`
`It has been known that cutting the
`
`hard portions
`
`can be carried out easily by raising the
`
`amount of contact.
`
`Therefore,
`
`a speed control unit 6b is
`
`provided in the output unit
`
`6
`
`to control
`
`the amount of
`
`contact
`
`to be varied according to whether an excited neuron
`
`N2 belongs to the category for the late wood portions G1 or
`
`the category for
`
`to the early wood portions G2
`
`in the
`
`cluster determination unit
`
`lb.
`
`The speed control unit 6b
`
`controls the amount of contact
`
`to be great (i.e.,
`
`increasing
`
`a cutting speed,
`
`i.e., moving speed of
`
`the blade teeth, or
`
`decreasing a feed speed of
`
`the workpiece W)
`
`in case of
`
`the
`
`hard late wood portions G1, while it controls the amount of
`
`contact
`
`to be small (i.e., decreasing the cutting speed or
`
`increasing in the feed speed of the workpiece W)
`
`in case of
`
`the soft early wood portions G2.
`
`As
`
`a
`
`result,
`
`the cutting can be
`
`facilitated.
`
`The
`
`amounts of characteristics are extracted from the electric
`
`15
`
`20
`
`25
`
`-17-
`
`

`

`signal
`
`generated
`
`from the
`
`vibration
`
`sensor
`
`in
`
`this
`
`embodiment. However,
`
`the amounts of characteristics may be
`
`extracted from the signals representing vibrations of
`
`the
`
`working machine X, e.g., a load current of a motor provided
`
`at the driving device. However,
`
`if the load current is used
`
`in extracting the amount of characteristics,
`
`the amount of
`
`characteristics may include a lot of unnecessary information.
`
`On
`
`the other hand,
`
`the
`
`amounts of characteristics
`
`are
`
`extracted from the vibrations in this embodiment,
`
`so that it
`
`is easy to acquire target information required by the neural
`
`network la and it is simple to obtain information on quality
`
`of material of the workpiece W.
`
`While the invention has been shown and described with
`
`respect
`
`to the embodiments,
`
`it will be understood by those
`
`skilled in the art
`
`that various changes and modifications
`
`may
`
`be made without departing from the
`
`scope
`
`of
`
`the
`
`invention as defined in the following claims.
`
`10
`
`15
`
`—l8—
`
`

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