Electronic device and method of operation for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty

Abstract

According to various embodiments, an electronic device for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty includes a tool wear sensor module and a processor, wherein the processor may be configured to collect tool wear data of a specific tool through the tool wear sensor module, apply a Kalman filter to the tool wear data to generate noise-removed tool wear data, input the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time. According to various embodiments, a method of operation for an electronic device that predicts the degree of tool wear based on an artificial intelligence model reflecting uncertainty may include: collecting tool wear data of a specific tool through a tool wear sensor module of the electronic device; applying a Kalman filter to the tool wear data to generate noise-removed tool wear data; and inputting the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time. Various other embodiments are also possible.

Description

Technology Field

[0001] The present invention relates to an electronic device and a method of operation for predicting the degree of wear of a tool based on an artificial intelligence model that reflects uncertainty, and more specifically, to an electronic device and a method of operation for predicting an accurate predicted value of tool wear along with a confidence interval through an artificial intelligence model that considers uncertainty. Background Technology

[0003] Titanium alloys are utilized in various industries, including aerospace, biotechnology, and the automotive sector, due to their excellent mechanical properties. In particular, their high strength, corrosion resistance, biocompatibility, and high-temperature resistance serve as a foundation for applications across diverse industries.

[0004] As machining is the primary approach for producing final products for industrial applications, research on predicting tool wear, one of the major problems occurring during machining, is actively being conducted.

[0005] In the case of existing tool wear prediction methods, predictions were made using physical models, but these are insufficient to account for numerous assumptions and the nonlinear characteristics of tool wear. Furthermore, in the case of prediction methods using analytical models, waiting for several hours is required to verify the results calculated through multiple iterations.

[0006] As a result, research on AI-based tool wear prediction has recently been actively underway. Additionally, data is collected using high-precision sensors for AI learning, and the collected data is used to derive the final tool wear results through artificial neural networks, which is a matter directly related to product productivity.

[0007] However, in the case of titanium alloys, high cutting heat is generated during machining due to high strength and poor thermal properties (low thermal conductivity and thermal diffusivity), which is a major factor in increased tool wear, and increased tool wear is directly linked to reduced product quality and reduced productivity.

[0008] Accordingly, while tool replacement should be performed at an accurate time through AI-based tool wear prediction, there is a problem in that sensor data for AI training involves high-precision sensors, resulting in significant initial and maintenance costs.

[0009] Furthermore, due to the non-uniformity of the material and tool wear that varies sensitively with temperature and humidity, it is difficult to be confident in the predicted tool wear results. Prior art literature

[0011] Korean Registered Patent Publication No. 10-2656853 Korean Published Patent Publication No. 10-2023-0063751 Korean Published Patent Publication No. 10-2024-0081717 The problem to be solved

[0012] Accordingly, the technical problem that the present invention aims to solve is created to solve the aforementioned problem, and aims to provide an electronic device and a method of operation thereof for predicting the degree of wear of a tool based on an artificial intelligence model that reflects uncertainty.

[0013] The technical problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art to which the present invention belongs from the description below. means of solving the problem

[0015] An electronic device for predicting the degree of tool wear based on an artificial intelligence model reflecting uncertainty, according to the present invention for solving the above technical problem, comprises a tool wear sensor module and a processor, wherein the processor may be configured to collect tool wear data of a specific tool through the tool wear sensor module, apply a Kalman filter to the tool wear data to generate noise-removed tool wear data, input the noise-removed tool wear data into a tool wear prediction model, and output the degree of wear of the specific tool after a specific time.

[0016] A method of operation for an electronic device for predicting the degree of tool wear based on an artificial intelligence model reflecting uncertainty, according to the present invention, may include: a step of collecting tool wear data of a specific tool through a tool wear sensor module of the electronic device; a step of generating noise-removed tool wear data by applying a Kalman filter to the tool wear data; and a step of inputting the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time. Effects of the invention

[0018] The present invention can predict tool wear occurring during machining while including uncertainty information, thereby reflecting various variables and variability to increase the reliability of the prediction, and can reduce unnecessary tool replacements by predicting the appropriate tool replacement time, thus reducing costs.

[0019] In addition, it helps maintain product quality and, consequently, contributes to increased productivity. Furthermore, through smartphone-based tool wear prediction, it can reduce initial sensing module installation and maintenance costs, and has the advantage of being operable through the installation of a simple application in the future.

[0020] The effects of the present invention are not limited to those described above, and potential effects expected from the technical features of the present invention will be clearly understood from the description below. Brief explanation of the drawing

[0022] FIG. 1 illustrates a block diagram of an electronic device according to various embodiments of the present invention. FIG. 2 is a flowchart illustrating how an electronic device operates according to various embodiments. FIG. 3 is a schematic diagram showing an AI-based tool wear prediction using a smartphone sensor according to various embodiments. FIG. 4 is a diagram showing an experimental environment for collecting processing data based on a smartphone sensor according to various embodiments. FIG. 5 is an example diagram showing the noise removal results of smartphone data based on a Kalman filter according to various embodiments. FIG. 6 is an example diagram showing the results of tool wear prediction based on uncertainty incorporating a smartphone sensor according to various embodiments. Specific details for implementing the invention

[0023] Hereinafter, various embodiments of this document are described with reference to the accompanying drawings. The embodiments and the terms used therein are not intended to limit the technology described in this document to specific embodiments and should be understood to include various modifications, equivalents, and / or substitutions of said embodiments. In relation to the description of the drawings, similar reference numerals may be used for similar components. A singular expression may include a plural expression unless the context clearly indicates otherwise. In this document, expressions such as "A or B" or "at least one of A and / or B" may include all possible combinations of items listed together. Expressions such as "first," "second," "first," or "second" may modify said components regardless of order or importance and are used only to distinguish one component from another and do not limit said components. When it is mentioned that a certain (e.g., 1st) component is "(functionally or telecommunicationally) connected" or "connected" to another (e.g., 2nd) component, said certain component may be directly connected to said other component or connected through another component (e.g., 3rd component).

[0024] In this document, "configured to" may be used interchangeably with, depending on the context, for example, hardware- or software-wise, "suitable for," "capable of," "modified to," "made to," "capable of," or "designed to." In some cases, the expression "device configured to" may mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "processor configured to perform A, B, and C" may mean a dedicated processor for performing the corresponding operations (e.g., an embedded processor), or a general-purpose processor capable of performing the corresponding operations by executing one or more software programs stored in a memory device (e.g., a CPU or application processor).

[0025] An electronic device according to various embodiments of the present document may include, for example, at least one of a smartphone, a tablet PC, a desktop PC, a laptop PC, a netbook computer, a workstation, and a server.

[0026] Referring to FIG. 1, an electronic device (101) within a network environment (100) in various embodiments is described. The electronic device (101) may include a bus (110), a processor (120), a memory (130), an input / output interface (150), a display (160), and a communication interface (170). In some embodiments, the electronic device (101) may omit at least one of the components or additionally include other components. The bus (110) may include a circuit that connects the components (110-170) to each other and transmits communication (e.g., control messages or data) between the components. The processor (120) may include one or more of a central processing unit, an application processor, or a communication processor (CP). The processor (120) may, for example, perform operations or data processing regarding the control and / or communication of at least one other component of the electronic device (101).

[0027] Memory (130) may include volatile and / or non-volatile memory. Memory (130) may store instructions or data related to at least one other component of the electronic device (101), for example. According to one embodiment, memory (130) may store software and / or programs (140). Programs (140) may include, for example, a kernel (141), middleware (143), an application programming interface (API) (145), and / or application programs (or "applications") (147), etc. At least part of the kernel (141), middleware (143), or API (145) may be referred to as an operating system. The kernel (141) can control or manage system resources (e.g., bus (110), processor (120), or memory (130), etc.) used to execute operations or functions implemented in other programs (e.g., middleware (143), API (145), or application program (147)). Additionally, the kernel (141) can provide an interface that controls or manages system resources by accessing individual components of the electronic device (101) from the middleware (143), API (145), or application program (147).

[0028] Middleware (143) may act as an intermediary to enable, for example, an API (145) or an application program (147) to communicate with the kernel (141) to exchange data. Additionally, middleware (143) may process one or more work requests received from the application program (147) according to priority. For example, middleware (143) may process the one or more work requests by assigning a priority to at least one of the application programs (147) to use the system resources of the electronic device (101) (e.g., bus (110), processor (120), or memory (130), etc.). The API (145) is an interface for the application (147) to control functions provided by the kernel (141) or middleware (143), and may include at least one interface or function (e.g., command) for, for example, file control, window control, image processing, or character control. The input / output interface (150) can, for example, transmit commands or data input from a user or other external device to other component(s) of the electronic device (101), or output commands or data received from other component(s) of the electronic device (101) to the user or other external device.

[0029] The display (160) may include, for example, a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a micro-electromechanical system (MEMS) display, or an electronic paper display. The display (160) may display various content (e.g., text, images, videos, icons, and / or symbols, etc.) to a user, for example. The display (160) may include a touch screen and may receive touch, gesture, proximity, or hovering input using, for example, an electronic pen or a part of the user's body. The communication interface (170) may establish communication between, for example, the electronic device (101) and an external device (e.g., a first external electronic device (102), a second external electronic device (104), or a server (106)). For example, the communication interface (170) can be connected to a network (162) via wireless or wired communication to communicate with an external device (e.g., a second external electronic device (104) or a server (106)).

[0030] Wireless communication may include cellular communication using at least one of, for example, LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications). According to one embodiment, wireless communication may include at least one of, for example, WiFi (wireless fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, NFC (near field communication), Magnetic Secure Transmission, Radio Frequency (RF), or Body Area Network (BAN). According to one embodiment, wireless communication may include GNSS. GNSS may be, for example, GPS (Global Positioning System), Glonass (Global Navigation Satellite System), Beidou Navigation Satellite System (hereinafter "Beidou"), or Galileo, the European global satellite-based navigation system. Hereinafter, in this document, "GPS" may be used interchangeably with "GNSS". Wired communication may include at least one of, for example, USB (universal serial bus), HDMI (high definition multimedia interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).The network (162) may include at least one of a telecommunications network, for example, a computer network (e.g., LAN or WAN), the Internet, or a telephone network.

[0031] Each of the first and second external electronic devices (102, 104) may be the same or a different type of device as the electronic device (101). According to various embodiments, all or part of the operations performed on the electronic device (101) may be performed on one or more other electronic devices (e.g., electronic devices (102, 104), or a server (106). According to one embodiment, when the electronic device (101) needs to perform a function or service automatically or upon request, the electronic device (101) may request at least some of the associated functions from another device (e.g., electronic devices (102, 104), or a server (106)) instead of performing the function or service itself or additionally. The other electronic device (e.g., electronic devices (102, 104), or a server (106)) may perform the requested function or additional functions and transmit the result to the electronic device (101). The electronic device (101) may provide the requested function or service by processing the received result as is or additionally. For this purpose, for example, cloud computing, distributed computing, or client-server computing technologies may be used.

[0033] FIG. 2 is a flowchart illustrating how an electronic device operates according to various embodiments.

[0034] FIG. 3 is a schematic diagram showing an AI-based tool wear prediction using a smartphone sensor according to various embodiments.

[0035] FIG. 4 is a diagram showing an experimental environment for collecting processing data based on a smartphone sensor according to various embodiments.

[0036] FIG. 5 is an example diagram showing the noise removal results of smartphone data based on a Kalman filter according to various embodiments.

[0037] FIG. 6 is an example diagram showing the results of tool wear prediction based on uncertainty incorporating a smartphone sensor according to various embodiments.

[0039] Prior to the detailed description of the present invention, the electronic device (100) may include a tool wear sensor module and a processor (120). In the present invention, the electronic device (100) may typically refer to a portable device such as a tablet or a smartphone, but it is understood that it may refer to any device having the function of measuring tool wear data, storing it, and transmitting it to an external device, including the tool wear sensor module described below. The tool wear sensor module may refer to any type of sensor installed inside the electronic device (100) that can measure the wear status of a tool in real time. The processor (120) may be the entity that performs the operations 201 through 205 described below. The components presented above are merely examples, and it is understood that the components may be added or removed as needed.

[0041] In operation 201, according to various embodiments, the processor (120) of the electronic device (100) can collect tool wear data of a specific tool through a tool wear sensor module. For example, referring to FIGS. 3 and 4, tool wear data occurring during machining can be collected in real time after being measured through the electronic device (100, e.g., a smartphone).

[0043] In operation 203, according to various embodiments, the processor (120) of the electronic device (100) can generate noise-removed tool wear data by applying a Kalman filter to the tool wear data. For example, regarding the tool wear sensor module used in the present invention, if we refer to FIG. 5, the tool wear data collected in operation 201 contains a high amount of noise compared to conventional high-precision sensors, so noise suppression must be performed. However, as in the present invention, the turning process does not contain frequency information unlike the milling process, so frequency-related filters cannot be used; therefore, noise removal can be performed through a Kalman filter. Referring to FIG. 5, noise-removed tool wear data with a Kalman filter applied to the tool wear data can be shown.

[0045] In operation 205, according to various embodiments, the processor (120) of the electronic device (100) inputs noise tool wear data into a tool wear prediction model and outputs a specific degree of tool wear after a specific time.

[0046] According to one embodiment, a tool wear prediction model uses the degree of wear of a plurality of tools corresponding to each of a plurality of time points as training data, includes a density output structure and a Bayesian neural network structure, reflects aleatoric uncertainty through the density output structure and reflects epistemic uncertainty through the Bayesian neural network structure, and can output the degree of wear of the specific tool after a specific time point based on noise-removed tool wear data.

[0047] Specifically, referring to FIG. 3, the Density output structure (310) is in the form of a probability density simulation. To account for data-based uncertainty, the model receiving the data predicts the parameters (Gaussian density: average and standard deviation) of the assumed probability distribution. As optimization is performed based on likelihood, the prediction model can naturally account for the uncertainty inherent in the data. The Bayesian neural network structure (320) accounts for the uncertainty inherent in the parameters of the prediction model to account for Episetmic uncertainty, which is the uncertainty of the model itself. In particular, learning can proceed by utilizing variational inference to approximate a variational distribution that simulates the posterior probability distribution followed by each parameter of the prediction model.

[0049] Referring to FIG. 6, tool wear prediction results output through operations 201 to 205 can be shown. Each can represent the same conditions as (a) cutting speed 100 m / min, feed 0.15 mm / rev, (b) cutting speed 80 m / min, feed 0.15 mm / rev, and (c) cutting speed 100 m / min, feed 0.2 mm / rev, axial depth 1 mm.

[0051] According to various embodiments, an electronic device for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty comprises a tool wear sensor module and a processor, wherein the processor is configured to collect tool wear data of a specific tool through the tool wear sensor module, apply a Kalman filter to the tool wear data to generate noise-removed tool wear data, input the noise-removed tool wear data into a tool wear prediction model, and output the degree of wear of the specific tool after a specific time.

[0052] According to various embodiments, the tool wear prediction model may use the degree of wear of a plurality of tools corresponding to each of a plurality of times as training data.

[0053] According to various embodiments, the tool wear prediction model may include a density output structure and a Bayesian neural network structure.

[0054] According to various embodiments, the tool wear prediction model can output the degree of wear of the specific tool after a specific time based on the noise-removed tool wear data by reflecting aleatoric uncertainty through the density output structure and reflecting epistemic uncertainty through the Bayesian neural network structure.

[0055] According to various embodiments, a method of operation of an electronic device for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty may include: a step of collecting tool wear data of a specific tool through a tool wear sensor module of the electronic device; a step of generating noise-removed tool wear data by applying a Kalman filter to the tool wear data; and a step of inputting the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time.

[0057] As used in this document, the terms “module” or “part” include a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. “Module” or “part” may be a component formed integrally or a minimum unit or part thereof that performs one or more functions. “Module” or “part” may be implemented mechanically or electronically and may include, for example, an application-specific integrated circuit (ASIC) chip, field-programmable gate arrays (FPGAs), or programmable logic device known or to be developed that performs certain operations, and may be executed by a processor (120). At least part of the device (e.g., modules or functions thereof) or method (e.g., operations) according to various embodiments may be implemented as instructions stored in a computer-readable storage medium (e.g., memory (130)) in the form of a program module. When the above instruction is executed by a processor (e.g., processor (120)), the processor may perform a function corresponding to the above instruction. Computer-readable recording media may include a hard disk, a floppy disk, a magnetic medium (e.g., magnetic tape), an optical recording medium (e.g., CD-ROM, DVD, magneto-optical medium (e.g., floptical disk), built-in memory, etc. Instructions may include code generated by a compiler or code that can be executed by an interpreter. A module or program module according to various embodiments may include at least one of the aforementioned components, some of which may be omitted, or additionally include other components. Operations performed by a module, program module, or other components according to various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added.

[0058] Furthermore, the embodiments disclosed in this document are presented for the purpose of explaining and understanding the disclosed technical content and are not intended to limit the scope of this disclosure. Accordingly, the scope of this disclosure should be interpreted to include all modifications or various other embodiments based on the technical concept of this disclosure.

Claims

Claim 1 An electronic device for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty, comprising a tool wear sensor module and a processor, wherein the processor collects tool wear data of a specific tool through the tool wear sensor module, applies a Kalman filter to the tool wear data to generate noise-removed tool wear data, inputs the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time, wherein the tool wear prediction model includes a density output structure and a Bayesian neural network structure, reflects aleatoric uncertainty through the density output structure and reflects epistemic uncertainty through the Bayesian neural network structure, and outputs the degree of wear of the specific tool after a specific time based on the noise-removed tool wear data. Claim 2 In claim 1, the tool wear prediction model is an electronic device that uses the degree of wear of a plurality of tools corresponding to each of a plurality of times as learning data. Claim 3 delete Claim 4 delete Claim 5 A method of operation of an electronic device for predicting the degree of tool wear based on an artificial intelligence model that reflects uncertainty, comprising: a step of collecting tool wear data of a specific tool through a tool wear sensor module of the electronic device; a step of generating noise-removed tool wear data by applying a Kalman filter to the tool wear data; and a step of inputting the noise-removed tool wear data into a tool wear prediction model to output the degree of wear of the specific tool after a specific time; wherein the tool wear prediction model includes a density output structure and a Bayesian neural network structure, reflects aleatoric uncertainty through the density output structure and reflects epistemic uncertainty through the Bayesian neural network structure, and outputs the degree of wear of the specific tool after a specific time based on the noise-removed tool wear data.

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