KR102193189B1 - Method for manufacturing cutting tool - Google Patents

Abstract

Disclosed is a method of manufacturing a cutting tool. According to one embodiment of the present invention, the method of manufacturing the cutting tool, in a method of manufacturing the cutting tool based on an artificial intelligence, comprises: a step of primarily cut raw materials by controlling a first processing process facility based on a design with a control apparatus inputted; a step of photographing an image of the raw materials cut primarily by rotating by 360 degrees as the control apparatus controls a camera in the control apparatus; a step of generating a first input signal based on the results of photographing by the control apparatus; a step where the control apparatus inputs a first input signal into a pre-learned convolution neural network in an embedded computer of the control apparatus; a step where the control apparatus inputs an output value of the convolution neural network into one of a first, second, and third neural networks which are pre-learned in the embedded computer; a step where the control apparatus acquires a first output signal based on the input results for the neural networks; a step where the control apparatus secondarily cuts the raw materials by controlling a second processing process facility based on the first output signal; a step where the control apparatus performs thermal treatment by controlling a thermal treatment machine for the cutting tool product generated as a result of the secondary cutting process; a step where the control apparatus finally grinds the cutting tool product by controlling a grinder; a step where the control apparatus coats a surface of the cutting tool product by controlling a coating machine; and a step where the control apparatus stores a feedback of the manufacturer on the first input signal, the first output signal, and the processing results in a blockchain network. The present invention aims to provide the method of manufacturing the cutting tool, which is able to increase accuracy and efficiency of manufacturing the cutting tool.

Description

Cutting tool manufacturing method {METHOD FOR MANUFACTURING CUTTING TOOL}

The following embodiments relate to a method of manufacturing a cutting tool.

In the manufacture of cutting tools including hole saws, the manufacturing accuracy of cutting tools is very important. Particularly, the part including the cutting edge of the cutting tool requires a very sophisticated process. Currently, manufacturing of cutting tools is carried out through mechanization and automation processes, and in the process of manufacturing, defective products are generated due to the problem of implementing precision. In order to prevent this, research on technology that can reduce the defect rate in manufacturing cutting tools is required through artificial intelligence designed to enable high precision implementation.

KR100625218 KR101401318 KR101898989 KR1998070951

The embodiments intend to increase the accuracy and efficiency of manufacturing a cutting tool by applying a deep learning technology to a cutting tool manufacturing method.

The embodiments are intended to optimize cutting tool manufacturing by applying a selectable cutting machine to a cutting tool manufacturing method.

The embodiments aim to convert information into big data through a blockchain network and use enhanced security.

In the method of manufacturing a cutting tool according to an embodiment, in a method of manufacturing a cutting tool based on artificial intelligence, based on a design drawing input by a control device, a first processing process facility-the first processing process facility may be rotated. Controlling a cylindrical raw material connected to a control rod with a 2 μm-sized diamond tip embedded in a cylindrical rotary cutting machine to perform the primary cutting process to form a basic shape-controlling the primary cutting process of the raw material; Capturing a 360° rotational image of the raw material processed by the control device by controlling the camera in the control device; Generating, by the control device, a first input signal based on a result of the photographing; Inputting, by the control device, a first input signal to a pre-learned convolutional neural network in an embedded computer of the control device; Inputting, by the control device, an output value of the convolutional neural network into one of the first, second, and third neural networks previously learned in the embedded computer; Obtaining, by the control device, a first output signal based on input results for the neural networks; Information that the first output signal includes the first cut-processed raw material connected to a control rod capable of being rotated by the control device based on the first output signal; Controlling a cutting machine selected on the basis of the equipment to perform secondary cutting processing to create an elaborate shape-and performing secondary cutting processing of raw materials; Controlling, by the control device, a heat treatment unit on the cutting tool product generated as a result of the secondary cutting processing to perform heat treatment; Controlling, by the control device, a polishing machine for final polishing of the cutting tool product; Coating the surface of the cutting tool product by the control device by controlling the coating machine; And storing, by the control device, the first input signal, the first output signal, and the manufacturer's feedback on the result of the processing in a blockchain network.

According to an embodiment, the convolutional neural network receives a first input signal that encodes the 360° rotational image of the raw material processed by the primary cutting, and extracts features of the convolutional neural network based on the first input signal. Each output value is output to 30 output nodes based on the connectivity, polishing degree, size accuracy, and shape accuracy of the raw material first cut through the neural network and the classification neural network, and the first, second and third neural networks are Each of 10 of the output values of the 30 output nodes is input, the first neural network determines the shape of the secondary cutting machine, and the second neural network is the size of the secondary cutting machine. And the third neural network determines the size of the diamond tip of the second cutting machine, and the embedded computer generates a first output signal including all outputs of the first, second and third neural networks. Can be generated.

According to an embodiment, the block chain network includes blocks including a manufacturer's feedback on the first input signal, the first output signal, and the result of the processing; Chains connecting each block in chronological order; And a private blockchain network including the network storage devices for storing the respective block chains, wherein the network storage devices include a first network storage device including a primary producer producing raw materials and cutting machines for the cutting tool; A second network storage device including a manufacturer that manufactures the cutting tool; A third network storage device including participants who wish to participate in the blockchain network; And a high-speed Internet connection network connecting each of the network storage devices.

The apparatus according to the embodiment may be controlled by a computer program stored in a medium in order to execute the method of any one of the above-described methods in combination with hardware.

The embodiments may increase the accuracy and efficiency of cutting tool manufacturing by applying deep learning technology to a cutting tool manufacturing method.

Embodiments can optimize cutting tool manufacturing by applying a selectable cutting machine to a cutting tool manufacturing method.

The embodiments can convert information into big data and use enhanced security through a blockchain network.

1 is a flow chart illustrating a method of manufacturing a cutting tool according to an embodiment.
2 is a diagram for explaining deep learning for a method of manufacturing a cutting tool according to an embodiment.
3 is a diagram illustrating a block chain network according to an embodiment.
4 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the rights of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be changed in various forms and implemented. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea.

Although terms such as first or second may be used to describe various components, these terms should be interpreted only for the purpose of distinguishing one component from other components. For example, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component.

When a component is referred to as being "connected" to another component, it is to be understood that it may be directly connected or connected to the other component, but other components may exist in the middle.

The terms used in the examples are used for illustrative purposes only and should not be interpreted as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

The embodiments may be implemented in various types of products such as a personal computer, a laptop computer, a tablet computer, a smart phone, a television, a smart home appliance, an intelligent vehicle, a kiosk, and a wearable device.

1 is a flow chart illustrating a method of manufacturing a cutting tool according to an embodiment.

According to an embodiment, a control device for a method of manufacturing a cutting tool (hereinafter, a control device) is a method of manufacturing a cutting tool based on artificial intelligence, by controlling the first processing equipment based on the input design drawing The primary cutting process can be performed (101). The control device is a device that controls a cutting tool manufacturing process, and may be implemented as, for example, a software module, a hardware module, or a combination thereof.

The control device according to an embodiment may be an electronic device including a communication function. For example, electronic devices include smart phones, tablet personal computers (PCs), mobile phones, video phones, e-book readers, desktop personal computers (desktop personal computers), and laptops. Laptop personal computer (PC), netbook computer, personal digital assistant (PDA), portable multimedia player (PMP), MP3 player, mobile medical device, camera, or wearable device (e.g.: Including at least one of a head-mounted-device (HMD) such as an electronic glasses, an electronic clothing, an electronic bracelet, an electronic necklace, an electronic appcessory, an electronic tattoo, a smart car, or a smartwatch. I can.

According to an embodiment, the electronic device may be a smart home appliance having a communication function. Smart home appliances, for example, include televisions, digital video disk (DVD) players, audio, refrigerators, air conditioners, vacuum cleaners, ovens, microwave ovens, washing machines, air purifiers, set-top boxes, and TVs. It may include at least one of a box (eg, Samsung HomeSyncTM, Apple TVTM, or Google TVTM), game consoles, electronic dictionary, electronic key, camcorder, or electronic frame.

According to an embodiment, the electronic device includes various medical devices (e.g., magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), computed tomography (CT)), an imager, an ultrasonic device, etc.), a navigation device, a GPS receiver ( global positioning system receiver), EDR (event data recorder), FDR (flight data recorder), automobile infotainment device, marine electronic equipment (e.g. marine navigation equipment and gyro compass, etc.), avionics, security It may include at least one of a device, a vehicle head unit, an industrial or domestic robot, an automatic teller's machine (ATM) of a financial institution, or a point of sales (POS) of a store.

According to one embodiment, the electronic device is a piece of furniture or building/structure including a communication function, an electronic board, an electronic signature receiving device, a projector, or various measurement devices. It may include at least one of devices (eg, water, electricity, gas, or radio wave measurement devices). The electronic device according to an embodiment may be a combination of one or more of the aforementioned various devices. In addition, the electronic device according to an embodiment may be a flexible device. In addition, it is obvious to those skilled in the art that the electronic device according to the embodiment is not limited to the above-described devices. The term user used in various embodiments may refer to a person using an electronic device or a device (eg, an artificial intelligence electronic device) using an electronic device.

An electronic device according to an embodiment includes a processor, a memory, a user interface, and a communication interface, and may be connected to other electronic devices through a network. The communication interface may transmit and receive data to and from other electronic devices within a predetermined distance through wired, wireless network, or wired serial communication. The network enables wired and wireless communication between an electronic device and various entities according to an embodiment. The electronic device can communicate with various entities through the network, and the network can use standard communication technologies and/or protocols. In this case, the network includes, but is not limited to, the Internet, a local area network (LAN), a wireless local area network (LAN), a wide area network (WAN), a personal area network (PAN), and the like, It can be understood by those of ordinary skill in the field of communication technology that it may be another type of network capable of transmitting and receiving information.

The design drawing according to an embodiment is a design drawing of a target cutting tool provided by a manufacturer of the target cutting tool, and may be a design drawing in 3D, and a process facility control process to facilitate the operation of the first and second machining process facilities May be included. The manufacturer can also input basic information about the physical properties of raw materials in the blueprint, and accordingly, the operation and strength of the first processing facility may change.

The first processing equipment according to an embodiment is an equipment capable of performing primary cutting processing to form a basic shape by controlling a cylindrical rotary cutting machine with a 2 μm size diamond tip embedded in a cylindrical raw material connected to a rotating control rod. I can. The control rod of the first processing facility may be a device capable of fixing cylindrical raw materials and rotating up to 360° according to the design. The cutting machine is a device capable of cutting raw materials by rotation and can perform cutting to obtain a basic shape. While the cutting machine rotates and cuts the raw material, the control rod can guide the raw material to be cut into the shape of the target cutting tool through a movement motion in the direction of rotation. The cutting machine of the first processing equipment may limit the processing of raw materials depending on the size of the cutting machine and the size of the diamond tip. Therefore, the first processing equipment is the first cutting process to create the basic shape of the target cutting tool from the raw material. Can be done.

According to an embodiment, the control device may control a camera in the control device to take a 360° rotational image of the raw material (102).

A camera according to an embodiment is a photography camera capable of automatically adjusting shutter speed, exposure, ISO, and magnification, and may have a resolution of 12 million pixels or more. The camera rotates the raw material processed by the primary cutting by 360° to take an image. This 360° rotation can be based on three axes of a three-dimensional space as well as one axis. The raw material may be photographed while being connected to the control rod for convenience, and in this case, an additional operation of removing the control rod may be required when generating the first input signal.

According to an embodiment, the control device may generate a first input signal based on the result of the photographing (103).

According to an embodiment, the control device may generate the first input signal by using image information captured by the camera, and the image may be encoded in a frame-by-frame standardized picture format to be appropriate for input to the convolutional neural network. I can. In the process of encoding, a picture may be converted into numerical data including RGB information, contrast information, saturation information, etc. for each pixel, and may be encoded as an input signal in a matrix form including each value. In the case of recording including the control rod during the video recording process, the operation of removing the control rod by recognizing the shape of the control rod during the encoding process may be performed, and the final modification of the encoded value may be performed while the control rod is removed. .

According to an embodiment, the control device may input the first input signal to a pre-learned convolutional neural network in the embedded computer of the control device (104).

The convolutional neural network according to an embodiment may check various surface area characteristics of a raw material that has been first cut based on a contrast with a database through a frame-by-frame picture according to an image capturing result of a camera. The convolutional neural network can extract surface area features extracted from a frame-by-frame picture of an input captured image and output them through 30 output nodes. For values output through the output node, the top 10 outputs can be inputs of the first neural network, the middle 10 outputs can be the inputs of the second neural network, and the lower 10 outputs are the inputs of the third neural network. Can be input. A detailed description of the convolutional neural network will be described later with reference to FIG. 2.

According to an embodiment, the control device may input an output value of the convolutional neural network to one of the first, second, and third neural networks learned in advance in the embedded computer (105). The control device may obtain a first output signal based on input results for neural networks (106).

According to an embodiment, of the 30 outputs of the convolutional neural network, the top 10 outputs become the inputs of the first neural network, the middle 10 outputs become the inputs of the second neural network, and the bottom 10 outputs are the third neural network. Can be input. Each neural network may determine the shape, size and size of a diamond tip of a cutting machine for secondary cutting processing of a target cutting tool based on the input value. The shape, size and size of the diamond tip of the cutting machine, which are the outputs of each neural network, can be integrated into a first thrust signal bound together by an embedded computer. A detailed description of the neural network will be described later with reference to FIG. 2.

The control device according to an embodiment may increase the accuracy and efficiency of manufacturing a cutting tool by applying deep learning technology to a method of manufacturing a cutting tool.

According to an embodiment, the control device may perform secondary cutting of the raw material by controlling the second processing equipment based on the first output signal (107).

The second machining process facility according to an embodiment performs secondary cutting processing to create an elaborate shape by controlling a cutting machine selected based on the information included in the first output signal for the raw material that has been first cut connected to the rotatable control rod. It may be a facility that can be performed. The cutting machine may be selected as a cutting machine optimized for secondary cutting processing by output values of the first, second, and third neural networks included in the first output signal. In the process of the secondary cutting process, the control device can extract information about the state of the cutting machine from the time and amount of cutting of the cutting machine, and accordingly, can automatically replace the cutting machine with an inadequate condition with a cutting machine with a normal cutting edge. . The cutting machine determined to be inadequate may be disposed of, and each time the cutting machine is replaced, the control device can include this record in the first output signal and store it in the blockchain network in the future.

According to an embodiment, the control device may perform heat treatment by controlling a heat treatment machine on the cutting tool product generated as a result of the secondary cutting process (108).

According to an embodiment, the control device may put the generated cutting tool product into a heat treatment unit and undergo a heat treatment process on the surface. The surface heat treatment process of the cutting tool product may have the purpose of heat treatment to deform the surface of the metal, removing carbon from the surface, and removing an oxide layer. The surface of the heat-treated cutting tool product may have black soot, and the soot produced can be removed through a final polishing operation with a grinder.

According to an embodiment, the control device may control the polishing machine to perform final polishing of the cutting tool product (109).

According to an embodiment, the control device may position the cutting tool product inside the polishing machine, and when the position detection sensor recognizes that the cutting tool product is located therein, the polishing operation may be automatically performed. The polishing operation inside the polishing machine can be performed in the order of acetone cleaning, watering a liquid containing the abrasive at high speed using air pressure, followed by acetone cleaning. The injection water using the abrasive may be an operation for physically removing soot from the surface by spraying the abrasive in the form of small particles having a size of 2 to 10 nm. The acetone washing operation may be an operation to remove carbon and oxide layers so that they do not form again.

According to an embodiment, the control device may coat the surface of the cutting tool product by controlling the coater (110).

The coating machine according to an embodiment is a device for evenly spraying the coating agent onto the surface of the cutting tool product, and by spraying the coating agent over a wide spray range at a distance of at least 1 m for a long time, it is possible to coat the surface of the cutting tool product. Coatings used in cutting tool products may be mainly used for the purpose of preventing oxidation and corrosion, but in some cases, they may be used for gloss, strength enhancement, and aesthetic purposes.

According to an embodiment, the control device may store the first input signal, the first output signal, and the manufacturer's feedback on the result of processing in the blockchain network (111).

According to an embodiment, the control device may store information about the accuracy of convolutional neural networks and neural networks, accuracy of a processing process, and appropriateness of materials for processing, and store various signals to provide feedback based on the information. Accordingly, the control device may store the first input signal and the first output signal in the blockchain network, and the first output signal may additionally add and store a cutting machine replacement problem that occurred when using the cutting machine. The control device may also store the manufacturer's feedback on the processing result in the blockchain network, which may be to facilitate learning of convolutional neural networks and neural networks by reflecting the final manufacturer's evaluation of the manufacturing process.

The block chain network according to an embodiment may be composed of blocks storing a manufacturer's feedback on a first input signal, a first output signal, and a processing result, and a chain connecting them in chronological order. Stored information is managed as big data, and blockchain networks can help keep this stored information secure and make it available only to limited users. A description of the blockchain network will be described later with reference to FIG. 3.

2 is a view for explaining deep learning for a method of manufacturing a cutting tool according to an embodiment.

Referring to FIG. 2, a process of deep learning for a cutting tool manufacturing method may include a convolutional neural network 340, a first neural network 310, a second neural network 320, and a third neural network 330. .

The convolutional neural network 340 according to an embodiment may receive, as an input, a first input signal 300 that encodes a 360° rotational image of a raw material processed by the primary cutting process. The first input signal 300 is generated by a process of extracting frames for each frame from the 360° rotational image captured by the control device, and encoding RGB information, contrast information, and saturation information in pixel units into a numerical matrix form. Can be.

The convolutional neural network 340 according to an embodiment includes a feature extraction neural network 301 and a classification neural network 302, and the feature extraction neural network 301 sequentially stacks an input signal with a convolutional layer and a pooling layer. Based on the first input signal 300, the convolutional neural network 340 is based on the connectivity, polishing degree, size accuracy, and shape accuracy of the raw material processed first through the feature extraction neural network 301 and the classification neural network 302. Each evaluation information can be output to 30 output nodes. In the feature extraction neural network 301 of the convolutional neural network 340, the convolutional layer includes a convolutional operation, a convolutional filter, and an activation function. The calculation of the convolution filter is adjusted according to the size of the matrix of the target input, but generally a 9X9 matrix is used. The activation function generally uses a ReLU function, a sigmoid function, and a tanh function, but is not limited thereto. The pooling layer is a layer that reduces the size of the input matrix, and uses a method of extracting a representative value by grouping pixels in a specific area. In general, an average value or a maximum value is often used for the operation of the pooling layer, but the present invention is not limited thereto. This operation is performed using a square matrix, typically a 9X9 matrix. The convolutional layer and the pooling layer are alternately repeated until the corresponding input is small enough while maintaining the difference.

According to one embodiment, the classification neural network 302 has a hidden layer and an output layer. In the convolutional neural network 340 for a method of manufacturing a cutting tool, there are generally three or more hidden layers, and 100 nodes of each hidden layer are designated, but more or less may be determined in some cases. The activation function of the hidden layer uses a ReLU function, a sigmoid function, and a tanh function, but is not limited thereto. The number of output layer nodes of the convolutional neural network 340 may be 30. Among the 30 output nodes, the top 10 nodes can be used as inputs of the first neural network 310, the middle 10 nodes can be used as inputs of the second neural network 320, and the bottom 10 nodes are the third. It can be used as an input to the neural network 330.

According to an embodiment, 30 nodes of the convolutional neural network 340 may be used as inputs to the first neural network 310, the second neural network 320, and the third neural network 330, respectively.

Neural networks have an input side, a hidden layer and an output layer. Neural networks for a method of manufacturing a cutting tool may basically have an input layer each having 10 input nodes, but are not limited thereto. There are usually 5 or more hidden layers of neural networks, and 30 nodes are designated for each hidden layer, but more or less can be set in some cases. The activation function of the hidden layer uses a ReLU function, a sigmoid function, and a tanh function, but is not limited thereto. Each of the output layer nodes of the neural networks may be 5, and each of the output layer nodes may contain characteristic information for selecting a cutting machine.

According to an embodiment, the first neural network 310 may use an output value from the top 10 nodes among 30 nodes of the convolutional neural network 340 as input, and this input value is the curvature of the raw material processed by the first cutting process. , It may contain information on the shape of the surface, the size of the gap, and the shape of the seam, but is not limited thereto. The first neural network 310 may output each value to five output nodes in order to determine the shape of the cutting machine through the input value. The output value output to the output node may determine the shape of the base, the shape of the edge, the shape and angle of the edge of the cutting machine, but is not limited thereto.

According to an embodiment, the second neural network 320 may use an output value from the middle 10 nodes among 30 nodes of the convolutional neural network 340 as an input, and such an input value is the curvature of the first cut raw material. , The information on the surface shape, the size of the gap, the shape of the seam, and the minimum area of the secondary processing area may be included, but is not limited thereto. The second neural network 320 may output each value to five output nodes in order to determine the size of the cutting machine through the input value. The output value output to the output node may determine the top and bottom area, side area, volume, and inclined surface area of the cutting machine, but is not limited thereto.

According to an embodiment, the third neural network 330 may use an output value from the lower 10 nodes among 30 nodes of the convolutional neural network 340 as an input, and such an input value is the surface of the raw material processed by the primary cutting. It may contain information on the degree of polishing and connectivity, but is not limited thereto. The third neural network 330 may output each value to five output nodes in order to determine the size of the diamond tip through the input value. The output value output to the output node may determine the crystal size, crystal structure, crystal shape, and strength of the cutting machine diamond tip, but is not limited thereto.

According to an embodiment, outputs of neural networks may be integrated by an embedded computer in the control device, and through this process, the control device may generate the first output signal 350. The generated first output signal 350 may be used for secondary cutting processing through a second processing facility. The control device can optimize cutting tool manufacturing by applying selectable cutting machines to the cutting tool manufacturing method.

According to an embodiment, the learning signal for relearning the convolutional neural network 340 and the neural networks is the first input signal 300, the first output signal 350 stored in the blockchain network, and the manufacturer's feedback on the processing result. On the basis of, it can be generated when the manufacturer proceeds to relearn as necessary. The learning signal is created based on the correct answer according to the manufacturer's feedback and the error of the output value output by the convolutional neural network 340 and neural networks at the time, and in some cases, the SGD using delta, the batch method, or the method following the backpropagation algorithm may be used. I can. By this learning signal, the convolutional neural network 340 and the neural networks perform learning by modifying existing weights, and in some cases, momentum may be used. The cost function can be used to calculate the error, and the cross entropy function can be used as the cost function.

According to an embodiment, the control device may provide feedback through a continuous learning signal using an error for a determination error of the convolutional neural network 340 and the neural networks.

3 is a diagram illustrating a block chain network according to an embodiment.

Referring to FIG. 3, blocks 401 including a first input signal, a first output signal, and a manufacturer's feedback on a result of processing; Chains 402 connecting each block 401 in chronological order; And a first 410, a second 420, and a third network storage device 430 for storing each block chain.

According to an embodiment, the block 401 of the blockchain network may include a first input signal, a first output signal, and a manufacturer's feedback on a result of processing. Each block 401 is generally connected in chronological order, and accordingly, new blocks 401 may be produced every 10 minutes. The contents of the already generated block 401 are stored in all network storage devices, and have a structure that cannot be changed unless a majority of the contents are changed within time. For example, if a block chain network with a total of 3 network storage devices has one block 401 for each network storage device, if the contents of two or more blocks 401 cannot be changed within a limited time, Each block 401 may change the value of the block 401 having contents different from the majority through verification to be the same as the majority. Accordingly, high security can be maintained, and since the number of network storage devices actually participating in the blockchain network can reach tens to hundreds of thousands of devices, higher security can be exhibited.

Chains 402 according to an embodiment may be configured with a hash value. The chains 402 allow the blocks 401 to be continuous in chronological order, and in this case, each block 401 may be connected using a hash value. The block 401 may store a database, a hash value of the database, a previous header, and a header of the current block. Here, since the header of the current block functions as the previous header in the next block, each block 401 can be organically connected. In addition, if the content of the block changes even a little, the hash value is transformed into a completely different form, which effectively prevents attempts to change the content of the database. Since the header of the current block becomes the hash value of the total sum including the database, the hash value of the database, and the previous header, any attempt to compromise security by effectively modifying the database and the hash value becomes difficult. This is because the moment the database and the hash value are modified, the contents of the header also change, and accordingly, the previous header that enters the next block also changes, and accordingly, the header of the block also changes and the previous header of the next block may be changed again. That is, all subsequent blocks 401 must be hacked. Therefore, it is possible to increase the security of the blockchain with the chain 402 through the hash value. The control device can convert information into big data through a blockchain network and use enhanced security.

According to an embodiment, the network storage devices include: a first network storage device 410 including a primary producer producing raw materials and cutting machines for cutting tools; A second network storage device 420 including a manufacturer for manufacturing a cutting tool; A third network storage device 430 including participants who wish to participate in the blockchain network; And a high-speed Internet connection network 403 connecting each of the network storage devices. The number of network storage devices classified as first, second, and third may be determined according to the number of practitioners, the number of users, and the number of storage devices included.

According to an embodiment, the first network storage device 410 may include a primary producer who produces raw materials and cutting machines for cutting tools, and the primary producer may be one or more. The number of first network storage devices 410 may be determined according to the number of storage devices used by each company. The first network storage device 410 may make an effort to improve the physical properties and performance of raw materials and cutting machines through information recorded in the blockchain network, and facilitate manufacturer management through inventory identification.

According to an embodiment, the second network storage device 420 may include a manufacturer that manufactures a cutting tool. The number of the second network storage device 420 may be determined according to the number of companies that actually manufacture cutting tools and their users. The second network storage device 420 facilitates learning of convolutional neural networks and neural networks by manually inputting manufacturing errors when a cutting tool is manufactured, thereby increasing manufacturing efficiency.

According to an embodiment, the third network storage device 430 may include participants who wish to participate in the blockchain network. Participants of the third network storage device 430 can receive approval as a user of the third network storage device 430 by request to perform the function as a storage device, and perform a task for coin mining of the corresponding blockchain network. can do.

The high-speed Internet connection network 403 according to an embodiment generally refers to an Internet connection network exhibiting a speed of 10 Mb/s or more, and is a connection network including wired, wireless, optical cable technology, etc., and is a local area network (LAN) or wireless LAN (Wireless LAN). Local Area Network), Wide Area Network (WAN), Personal Area Network (PAN), and the like, but are not limited thereto.

4 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

The device 1301 according to an embodiment includes a processor 1302 and a memory 1303. The device 1301 according to an embodiment may be the above-described server or terminal. The processor may include at least one of the devices described above with reference to FIGS. 1 to 3, or may perform at least one of the methods described above with reference to FIGS. 1 to 3. The memory 1303 may store information related to the above-described method or a program in which the above-described method is implemented. The memory 1303 may be a volatile memory or a nonvolatile memory.

According to an embodiment, the processor 1302 may execute a program and control the device 1301. The code of a program executed by the processor 1302 may be stored in the memory 1303. The device 1301 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown), and can exchange data.

The embodiments described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices, methods, and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments and claims and equivalents fall within the scope of the following claims.

Claims (3)

In the method of manufacturing a cutting tool based on artificial intelligence,
The control device, based on the blueprint, controls the first machining process facility-the first machining process facility controls a cylindrical rotary cutting machine with a 2 μm diamond tip embedded in a cylindrical raw material connected to a rotatable control rod to create a basic shape Controlling the equipment capable of performing primary cutting processing to perform primary cutting processing of raw materials;
Capturing a 360° rotational image of the raw material processed by the control device by controlling the camera in the control device;
Generating, by the control device, a first input signal based on a result of the photographing;
Inputting, by the control device, a first input signal to a pre-learned convolutional neural network in an embedded computer of the control device;
Inputting, by the control device, an output value of the convolutional neural network into one of the first, second and third neural networks previously learned in the embedded computer;
Obtaining, by the control device, a first output signal based on input results for the neural networks;
Information that the first output signal includes the first cut-processed raw material connected to a control rod capable of being rotated by the control device based on the first output signal; Controlling a cutting machine selected on the basis of the equipment to perform secondary cutting processing to create an elaborate shape-and performing secondary cutting processing of raw materials;
Performing, by the control device, controlling a heat treatment machine on the cutting tool product generated as a result of the secondary cutting processing;
Controlling, by the control device, a polishing machine for final polishing of the cutting tool product;
Controlling the surface of the cutting tool product by the control device to coat the surface of the cutting tool product; And
Storing, by the control device, the first input signal, the first output signal, and the manufacturer's feedback on the result of the processing in a blockchain network
Including
How to make a cutting tool.


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