Application No.: l l/987,44l
`Examiner: Chang, Sunray
`Art Unit: 2121
`
`AMENDMENT TO THE CLAIMS
`
`1. (Currently Amended) A working process monitoring device, comprising:
`
`a sensor unit for detecting at least one of vibrations and sound waves produced
`
`while processing a workpiece by a working machine;
`
`a signal input unit for performing segmentation on an electric sigpal outputted
`
`from the sensor unit and extracting target signals using segmented portions in the electric
`
`s_ign_2d—fren+an—eleetri&sig&a+eutputted—freflkthesensefir&itg
`
`an amount of characteristics extracting unit
`
`for extracting amounts of
`
`characteristics including a plurality of parameters From the target signals;
`
`a training sample storage unit for storing amounts of characteristics extracted
`
`from target signals obtained while processing a workpiece made of a normal quality
`
`material; and
`
`a material inspecting unit for detecting whether a material of a portion of the
`
`workpiece currently being processed is normal in quality or not, wherein the material
`
`inspecting unit employs a neural network trained by using the amounts of characteristics
`
`stored in the training sample storage unit,
`
`wherein the workpiece is wood, and the working machine is a cutting device
`
`having a saw blade to cut the workpiece across wood grains thereof,
`
`wherein early wood portions, [[and]] late wood portions and portions of the
`
`workpiece having insufficiently develop ed wood grain are treated as material portions of
`
`normal quality among portions being machined,
`
`wherein the amounts of characteristics stored in the training sample storage unit
`
`are categorized into a normal group= a late wood group and an early wood group
`
`corresponding respectively to the insufficiently developed wood grain portions, the late
`
`wood portions and the early wood portions,
`
`wherein the working process monitoring device fiarther comprises a speed
`
`control unit which controls a cutting speed so that an amount of contact per unit time that
`
`
`Examiner: Chang, Sunray
`Art Unit: 2121
`
`AMENDMENT TO THE CLAIMS
`
`1. (Currently Amended) A working process monitoring device, comprising:
`
`a sensor unit for detecting at least one of vibrations and sound waves produced
`
`while processing a workpiece by a working machine;
`
`a signal input unit for performing segmentation on an electric sigpal outputted
`
`from the sensor unit and extracting target signals using segmented portions in the electric
`
`s_ign_2d—fren+an—eleetri&sig&a+eutputted—freflkthesensefir&itg
`
`an amount of characteristics extracting unit
`
`for extracting amounts of
`
`characteristics including a plurality of parameters From the target signals;
`
`a training sample storage unit for storing amounts of characteristics extracted
`
`from target signals obtained while processing a workpiece made of a normal quality
`
`material; and
`
`a material inspecting unit for detecting whether a material of a portion of the
`
`workpiece currently being processed is normal in quality or not, wherein the material
`
`inspecting unit employs a neural network trained by using the amounts of characteristics
`
`stored in the training sample storage unit,
`
`wherein the workpiece is wood, and the working machine is a cutting device
`
`having a saw blade to cut the workpiece across wood grains thereof,
`
`wherein early wood portions, [[and]] late wood portions and portions of the
`
`workpiece having insufficiently develop ed wood grain are treated as material portions of
`
`normal quality among portions being machined,
`
`wherein the amounts of characteristics stored in the training sample storage unit
`
`are categorized into a normal group= a late wood group and an early wood group
`
`corresponding respectively to the insufficiently developed wood grain portions, the late
`
`wood portions and the early wood portions,
`
`wherein the working process monitoring device fiarther comprises a speed
`
`control unit which controls a cutting speed so that an amount of contact per unit time that
`
`
`
`Application No.: l l/987,44l
`Examiner: Chang, Sunray
`Art Unit: 2121
`
`saw teeth of the saw blade are in contact with the workpiece while cutting the late wood
`
`portions is greater than that while cutting the early wood portions, and
`
`wherein the early wood portions and the late wood portions have different cut
`
`resistances in wood grains, and the cut resistance of the late wood portions is larger than
`
`that of the early wood portions.
`
`2. (Previously Presented) The working process monitoring device of claim 1, wherein
`
`the working process monitoring device fiirther comprises a machining 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 of normal quality.
`
`3. (Canceled).
`
`4. (Original) The working process monitoring device of claim 1, wherein the neural
`
`network is a competitive learning neural network.
`
`5. (Currently Amended) A working process monitoring device, comprising:
`
`a sensor unit for detecting at least one of vibrations and sound waves produced
`
`while processing a workpiece by a working machine;
`
`a signal input unit for performing segmentation on an electric signal outputted
`
`from the sensor unit and extracting target signals using segm ented portions in the electric
`
`iigLM—fiem—afleleetfiesignakeutpmted—frem—tlfienseeunfi;
`
`an amount of characteristics extracting unit
`
`for extracting amounts of
`
`characteristics including a plurality of parameters from the target signals;
`
`a training sample storage unit for storing amounts of characteristics extracted
`
`from target signals obtained while processing a workpiece made of a normal quality
`
`material; and
`
`a material inspecting unit for detecting whether a material of a portion of the
`
`workpiece currently being processed is normal in quality or not, wherein the material
`
`-3-
`
`
`
`Application No.: l l/987,44l
`Examiner: Chang, Sunray
`Art Unit: 2121
`
`inspecting unit employs a neural network trained by using amounts of characteristics
`
`stored in the training sample storage unit,
`
`wherein the workpiece is wood, and the working machine is a cutting device
`
`having a saw blade to cut the workpiece across wood grains thereof,
`
`wherein early wood portions, [[and]] late wood portions and portions of the
`
`workpiece having insufficiently developed wood grain are treated as material portions of
`
`normal quality among portions being machined,
`
`wherein the amounts of characteristics stored in the training sample storage unit
`
`are categorized into a normal group, a late wood group and an early wood group
`
`corresponding respectively to the insufficiently developed wood ggain portions, the late
`
`wood portions and the early wood portions,
`
`wherein the working process monitoring device further comprises a speed
`
`control unit which controls a relative feed speed between the workpiece and the saw
`
`blade so that an amount of contact per unit time that saw teeth of the saw blade are in
`
`contact with the workpiece while cutting the late wood portions is greater than that while
`
`cutting the early wood portions, and
`
`wherein the early wood portions and the late wood portions have different cut
`
`resistances in wood grains, and the cut resistance of the late wood portions is larger than
`
`that of the early wood portions.
`
`6. (Previously Presented) The working process monitoring device of claim 5, wherein
`
`the working process monitoring device further comprises a machining 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 of normal quality.
`
`7. (Previously Presented) The working process monitoring device of claim 5, wherein
`
`the neural network is a competitive learning neural network.
`
`
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