KR20230081136A - Platform system for recommendating cutting tool according usage - Google Patents

Abstract

The present invention is a cutting tool recommendation platform system according to the purpose, wherein the system includes the step of inputting information on tools, workpieces and machine tools, and the input tools, workpieces and machine tools using a learning-based cloud application service as inputs. Thus, it is configured to include a step of determining information on a tool, and a step of matching the determined information on a tool to a machining site.

Description

Cutting tool recommendation platform system according to application {PLATFORM SYSTEM FOR RECOMMENDATING CUTTING TOOL ACCORDING USAGE}

The present invention relates to a cutting tool recommendation platform system according to use.

There are many types of tools for cutting materials depending on the purpose or material, and recently, the use of advanced new materials is increasing.

In order to perform cutting processing precisely and efficiently, it is necessary to understand the physical properties, types, and shapes of machine tools, cutting tools, and workpieces, processing conditions, and the quality of the machined surface. In particular, as the selection of cutting tools and processing conditions influence productivity and processing quality, selection of cutting tools should be considered first in order to establish an optimal processing process.

However, since the types of cutting tools are very diverse and new tools continuously appear over time, it is practically difficult to understand and select all cutting tools. Therefore, depending on the individual experience and knowledge of cutting processing technicians in most manufacturing sites, cutting tools and processing conditions are selected accordingly.

Therefore, in order for a cutting engineer to select a cutting tool, make the most of its advantages, and select the processing conditions accordingly to minimize defects in the process, the selection of cutting conditions, one's own experience, external information, and cutting results It is required to develop a platform for recommending cutting tools that can be judged by considering such factors.

The background description of the invention has been prepared to facilitate understanding of the present invention. It should not be construed as an admission that matters described in the background art of the invention exist as prior art.

Cutting is processing by direct contact between a tool and a workpiece, and the final result (product) by cutting can be greatly influenced by the shape of the tool, the material of the tool, the coating of the tool, and the life of the tool. In addition, characteristic information on mechanical properties and machinability of a workpiece is recognized as a very important factor that can influence the processing quality of a final product along with information on a cutting tool.

However, as the number of difficult-to-cut materials and new materials that are difficult to process with conventional cutting tools increases in parts manufacturing sites in Korea, it is difficult to select a cutting tool suitable for the type of workpiece and processing conditions.

At this time, if a cutting tool is selected and used without considering the conventionality of the workpiece, usage environment, processing shape, etc., it may cause irregular wear and tear on the workpiece, shortening the life of the tool, and thereby shortening the tool replacement time. Increased productivity may be lowered due to a delay in the process and an increase in cost.

Therefore, in order to improve the productivity of products (parts) according to these difficult-to-cut materials and new materials, it is necessary to select an optimal cutting tool. However, as the current selection of cutting tools relies on the data provided by the manufacturer of the cutting tools and the know-how of skilled workers at the manufacturing site, in order to solve problems such as processing precision, processing quality, and tool wear, many trials are required. Mistakes and time consuming can reduce manufacturing competitiveness.

In other words, although processing technology incorporating various types of cutting-edge technologies is continuously evolving to sophistication, a database (database, DB) of the characteristics of new materials and crushing materials to produce them, and the selection of cutting tools and processing processes accordingly Construction is non-existent.

As a result, the inventors of the present invention have developed a platform that includes information on the characteristics of various cutting materials and thus various cutting tools in order to solve the above problems.

Furthermore, the inventors of the present invention tried to expand the use of the platform by collecting more diverse and extensive information from users by sharing and using the developed platform with various users.

The tasks of the present invention are not limited to the tasks mentioned above, and other tasks not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above-described problems, the present invention provides information on tools, workpieces, and machine tools by inputting information on tools, workpieces, and machine tools, using a learning-based cloud application service as inputs, and inputting tools, workpieces, and machine tools. It provides a cutting tool recommendation platform system according to the purpose, including the step of determining, and the step of matching the information on the determined tool to the machining site.

Other embodiment specifics are included in the detailed description and drawings.

The present invention may provide a cutting tool recommendation platform system according to the purpose.

More specifically, the present invention can recommend tools based on its own products by inputting information on processing shapes, workpieces, and product specifications for each tool company.

Therefore, through a public data platform that can provide various tool data and a platform that links actual processing data and machine tools, it is possible to secure competitiveness in the production process of domestic tool manufacturers and various companies that process through tools. there is. Furthermore, the present invention can achieve cost reduction by providing ease in selecting a cutting tool suitable for the field.

Effects according to the present invention are not limited by the contents exemplified above, and more various effects are included in the present invention.

1 is a schematic diagram of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.
2 is a block diagram showing the configuration of a user device according to an embodiment of the present invention.
3 is a block diagram showing the configuration of a user interface providing server according to an embodiment of the present invention.
Figure 4 is a schematic diagram of the function and use of the cutting tool recommendation platform system according to the use according to an embodiment of the present invention.
5 is an exemplary view of a learning model of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.
6 is a flow chart illustrating the utilization of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and will be implemented in various forms different from each other, only these embodiments make the disclosure of the present invention complete, and common knowledge in the art to which the present invention pertains. It is provided to completely inform the person who has the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, like reference numerals may be used for like elements.

In this document, expressions such as "has," "may have," "includes," or "may include" indicate the existence of a corresponding feature (eg, numerical value, function, operation, or component such as a part). , which does not preclude the existence of additional features.

In this document, expressions such as “A or B,” “at least one of A and/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, “A or B,” “at least one of A and B,” or “at least one of A or B” (1) includes at least one A, (2) includes at least one B, Or (3) may refer to all cases including at least one A and at least one B.

Expressions such as “first,” “second,” “first,” or “second,” used in this document may modify various elements, regardless of order and/or importance, and refer to one element as It is used only to distinguish it from other components and does not limit the corresponding components. For example, a first user device and a second user device may represent different user devices regardless of order or importance. For example, without departing from the scope of rights described in this document, a first element may be named a second element, and similarly, the second element may also be renamed to the first element.

A component (e.g., a first component) is "(operatively or communicatively) coupled with/to" another component (e.g., a second component); When referred to as "connected to", it should be understood that the certain component may be directly connected to the other component or connected through another component (eg, a third component). On the other hand, when an element (eg, a first element) is referred to as being “directly connected” or “directly connected” to another element (eg, a second element), the element and the above It may be understood that other components (eg, third components) do not exist between the other components.

As used in this document, the expression "configured to" means "suitable for," "having the capacity to," depending on the circumstances. ," "designed to," "adapted to," "made to," or "capable of." The term "configured (or set) to" may not necessarily mean only "specifically designed to" hardware. Instead, in some contexts, the phrase "device configured to" may mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "a processor configured (or configured) to perform A, B, and C" may include a dedicated processor (e.g., embedded processor) to perform those operations, or by executing one or more software programs stored in a memory device. , may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

Terms used in this document are only used to describe a specific embodiment, and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the art described in this document. Among the terms used in this document, terms defined in a general dictionary may be interpreted as having the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, an ideal or excessively formal meaning. not be interpreted as In some cases, even terms defined in this document cannot be interpreted to exclude the embodiments of this document.

Each feature of the various embodiments of the present invention can be partially or entirely combined or combined with each other, and as those skilled in the art can fully understand, various interlocking and driving operations are possible, and each embodiment can be implemented independently of each other. It may be possible to implement together in an association relationship.

For clarity of interpretation of this specification, terms used in this specification will be defined below.

1 is a schematic diagram of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.

Referring to Figure 1, the cutting tool recommendation platform system according to the purpose inputs product, workpiece, process, and machine tool type information, considering cutting workpiece variables, etc. As a proposed system, the cutting tool recommendation platform system according to the purpose of the present invention inputs information on tools, workpieces, and machine tools, and inputs tools, workpieces, and machine tools using a learning-based cloud application service. , determining information on a tool, and matching the determined information on a tool to a machining site.

More specifically, the present invention selects a cutting tool suitable for the characteristics of various workpieces, sets cutting conditions, monitors and diagnoses the machining process in connection with machining data at the manufacturing site, predicts the lifespan and machining quality of the cutting tool, , It can mean a cloud-based cutting tool platform for the advancement and smartization of cutting processing technology, such as increasing productivity by linking tool information with machine tool applications (interactive programs, tool management programs, machining simulation programs). The present invention is a workpiece DB for deriving the cutting conditions of a new workpiece that is first processed or has no processing data in a short time, a tool DB for tool selection suitable for the characteristics of the workpiece, monitoring and diagnosis of the machining process, recommendation of cutting conditions, optimization of machining, and tools It can be configured as a process DB for life prediction.

At this time, the workpiece DB includes data related to hardness, machinability index, and specific cutting resistance for more than 80 types of workpiece materials based on ISO, and the tool DB includes hardness, friction coefficient, thermal conductivity, and coating material of tool materials for more than 100 types of tools. , structure and tool shape-related data are included, and the process DB may include data collected and processed from actual machine tools at the manufacturing site.

Furthermore, the cutting tool recommendation platform system according to the purpose of the present invention may include a learning-based cloud application service, an intelligence-based service, and a development service.

At this time, the learning-based cloud application service can mean a basic technology for building a smart manufacturing innovation ecosystem to systematically promote innovation and preemptive industrial structure advancement of the production system, which boldly challenges new industries for high added value in the small and medium-sized manufacturing industry.

Furthermore, the development of such a standardized cloud platform may require enterprise-wide application services that manufacturers can easily utilize at optimal maintenance costs through the development of highly reliable analysis models to optimize productivity at industrial sites.

In addition, the intelligence-based service is a system that allows free search and utilization of on-site data related to the quality of process equipment, etc., and can mean a technology that can more easily supply processing technology based on manufacturing data such as big data artificial intelligence to consumers. More specifically, artificial intelligence is a system that collects various field data such as process facility operation and production quality in a standard way at manufacturing sites. , Big data is a big data service that secures the amount of information by utilizing data from various smart factories and provides a way to jointly utilize high-value data.

In addition, the individual service means a platform for visualizing on-site data to check the progress and stability of the process and learning from pre-processed big data and a portal for optimal recommendation of tool processes.

Furthermore, the present invention may further include visualization of on-site information, that is, machining process monitoring and diagnosis technology. More specifically, the present invention, for field application, real-time monitoring technology based on real data sensors of processing equipment and data machine learning model, analysis of processing process status compared to tool recommendation cutting conditions according to materials, optimization linkage technology, and mass production equipment application technology can include

In this case, processing equipment interface for sensor data acquisition/DAQ monitoring visualization, machine learning model, and DB for data acquisition, processing, and analysis may be included for processing monitoring and processing state analysis.

Accordingly, the present invention is an essential intelligent technology in the parts processing industry in connection with IT and smart trends in the manufacturing industry, and is a continuous process at the mass production site of demand companies that can reduce the dependence on experience and know-how of workers and actively respond to multi-product manufacturing sites. optimization can be achieved.

In addition, the cutting tool recommendation platform system according to the purpose can provide an interface to the device possessed by users, and accordingly, actual machine tool application (interactive program) to improve productivity by using the data of the cutting tool recommendation platform. , tool management program, machining simulation program) can be provided, and the user device (management) using the cutting tool recommendation platform system according to the purpose is an electronic device capable of photographing and outputting an image, such as a smartphone, tablet PC, It may include PCs, laptops, and the like.

2 is a block diagram showing the configuration of a user device according to an embodiment of the present invention.

Referring to FIG. 2 , the user device 100 may include a memory interface 110 , one or more processors 120 and a peripheral interface 130 . The various components within user device 100 may be connected by one or more communication buses or signal lines.

The memory interface 110 may be connected to the memory 150 and transfer various data to the processor 120 . Here, the memory 150 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage, cloud , It may include at least one type of storage medium among blockchain databases.

In various embodiments, memory 150 includes operating system 151 , communication module 152 , graphical user interface module (GUI) 153 , sensor processing module 154 , telephony module 155 , and application module 156 . ) At least one or more of them may be stored. Specifically, the operating system 151 may include instructions for processing basic system services and instructions for performing hardware tasks. The communication module 152 may communicate with at least one of one or more other devices, computers, and servers. A graphical user interface module (GUI) 153 may process a graphical user interface. The sensor processing module 154 may process sensor-related functions (eg, process voice input received using one or more microphones 192). The phone module 155 may process phone-related functions. The application module 156 may perform various functions of a user application, such as electronic messaging, web browsing, media processing, navigation, imaging, and other processing functions. In addition, the user device 100 may store one or more software applications 156 - 1 and 156 - 2 (eg, a cutting tool recommendation application) associated with a certain type of service in the memory 150 .

In various embodiments, the memory 150 may store a digital assistant client module 157 (hereinafter referred to as a DA client module), thereby storing instructions and various user data 158 for performing client-side functions of the digital assistant. (eg, user-customized vocabulary data, preference data, and other data such as the user's electronic address book, to-do list, shopping list, etc.).

Meanwhile, the DA client module 157 receives a user's voice input, text input, touch input, and/or gesture input through various user interfaces (eg, the I/O subsystem 140) provided in the user device 100. can be obtained

In addition, the DA client module 157 may output audio-visual and tactile data. For example, the DA client module 157 may output data consisting of a combination of at least two of voice, sound, notification, text message, menu, graphic, video, animation, and vibration. In addition, the DA client module 157 may communicate with a digital assistant server (not shown) using the communication subsystem 180 .

In various embodiments, DA client module 157 may collect additional information about the surrounding environment of user device 100 from various sensors, subsystems, and peripheral devices to construct a context associated with user input. . For example, the DA client module 157 may infer the user's intention by providing context information together with the user's input to the digital assistant server. Here, the situational information that may accompany the user input may include sensor information, eg, lighting, ambient noise, ambient temperature, image of the surrounding environment, video, and the like. For another example, the contextual information may include the physical state of the user device 100 (eg, device orientation, device location, device temperature, power level, speed, acceleration, motion pattern, cellular signal strength, etc.). For another example, the context information is information related to the state of the software of the user device 100 (eg, processes running on the user device 100, installed programs, past and present network activity, background services, error logs, resource usage). etc.) may be included.

In various embodiments, the memory 150 may include added or deleted commands, and the user device 100 may also include additional components other than those shown in FIG. 2 or may exclude some components.

The processor 120 may control the overall operation of the user device 100, and executes various commands for executing applications for the cutting tool recommendation platform system according to the purpose by driving an application or program stored in the memory 150. can do.

The processor 120 may correspond to an arithmetic device such as a central processing unit (CPU) or an application processor (AP). In addition, the processor 120 may be implemented in the form of an integrated chip (IC) such as a System on Chip (SoC) in which various computing devices such as a Neural Processing Unit (NPU) are integrated.

In various embodiments, the processor 120 receives information on tools, workpieces, and machine tools, and uses a learning-based cloud application service to determine information on tools by using the received tools, workpieces, and machine tools as inputs , it can be configured to match the information on the determined tool to the machining site.

The peripheral interface 130 may be connected to various sensors, subsystems, and peripheral devices to provide data so that the user device 100 can perform various functions. Here, that the user device 100 performs a certain function may be understood as being performed by the processor 120 .

Perimeter interface 130 may receive data from motion sensor 160, light sensor (light sensor) 161, and proximity sensor 162, through which user device 100 may receive orientation, light, and proximity. sensing function, etc. For another example, peripheral interface 130 may receive data from other sensors 163 (positioning system-GPS receiver, temperature sensor, biometric sensor), through which the user device 100 may receive data from other sensors. It can perform functions related to (163).

In various embodiments, the user device 100 may include a camera subsystem 170 coupled to the peripheral interface 130 and an optical sensor 171 coupled thereto, through which the user device 100 may take pictures and video Various shooting functions such as clip recording can be performed.

In various embodiments, user device 100 may include a communication subsystem 180 coupled with peripheral interface 130 . The communication subsystem 180 is composed of one or more wired/wireless networks, and may include various communication ports, radio frequency transceivers, and optical transceivers.

In various embodiments, user device 100 includes an audio subsystem 190 coupled to peripheral interface 130, which audio subsystem 190 includes one or more speakers 191 and one or more microphones 192. By including, user device 100 can perform voice-activated functions, such as voice recognition, voice replication, digital recording, and telephony functions.

In various embodiments, user device 100 may include I/O subsystem 140 coupled with peripherals interface 130 . For example, the I/O subsystem 140 may control the touch screen 143 included in the user device 100 through the touch screen controller 141 . As an example, the touch screen controller 141 uses any one of a plurality of touch sensing technologies, such as capacitive, resistive, infrared, surface acoustic wave technology, proximity sensor array, etc. and cessation of movement. For another example, I/O subsystem 140 may control other input/control devices 144 included in user device 100 via other input controller(s) 142 . As an example, other input controller(s) 142 may control one or more buttons, rocker switches, thumb-wheels, infrared ports, USB ports, and pointer devices such as styluses and the like.

3 is a block diagram showing the configuration of a user interface providing server according to an embodiment of the present invention.

Referring to FIG. 3, the cutting tool recommendation platform server 300 may include a communication interface 310, a memory 320, an I/O interface 330, and a processor 340, each of which includes one or more communication elements. They can communicate with each other through a bus or signal line.

The communication interface 310 may be connected to one or more user devices 100, a manager device 200, a monitoring device 500, and a photographing device 600 through a wired/wireless communication network to exchange data. For example, the communication interface 310 transmits video or audio obtained from at least one user device 100 to the manager device 200 as data on cutting tools and workpieces are input from the user device 100 . and can receive various data of the corresponding user device 100 from the manager device 200 .

On the other hand, the communication interface 310 enabling transmission and reception of such data includes a communication pod 311 and a wireless circuit 312, where the wired communication port 311 is one or more wired interfaces, for example, Ethernet, This may include Universal Serial Bus (USB), FireWire, and the like. Also, the wireless circuit 312 may transmit/receive data with an external device through an RF signal or an optical signal. In addition, wireless communication may use at least one of a plurality of communication standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol.

The memory 320 may store various data used in the cutting tool recommendation platform server 300 . For example, the memory 320 may store data on the type of workpiece, tool, etc. input from the user device, and learn to determine information on the tool by taking the input tool, workpiece, and machine tool as inputs. Device model (learning-based cloud application service) can be saved.

In various embodiments, the memory 320 may include a volatile or non-volatile recording medium capable of storing various data, commands, and information. For example, the memory 320 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage , Cloud, and a blockchain database may include at least one type of storage medium.

In various embodiments, the memory 320 may store a configuration of at least one of the operating system 321 , the communication module 322 , the user interface module 323 , and one or more applications 324 .

Operating system 321 (e.g. embedded operating systems such as LINUX, UNIX, MAC OS, WINDOWS, VxWorks, etc.) is a variety of software for controlling and managing general system tasks (e.g. memory management, storage device control, power management, etc.) components and drivers, and may support communication between various hardware, firmware, and software components.

The communication module 323 may support communication with other devices through the communication interface 310 . The communication module 320 may include various software components for processing data received by the wired communication port 311 or the wireless circuit 312 of the communication interface 310 .

The user interface module 323 may receive a user's request or input from a keyboard, touch screen, microphone, etc. through the I/O interface 330 and provide a user interface on a display.

Applications 324 may include programs or modules configured to be executed by one or more processors 330 . Here, an application for recommending and providing a cutting tool according to a purpose may be implemented on a server farm.

[In the case of a server] The I/O interface 330 connects at least one of an input/output device (not shown) of the cutting tool recommendation platform server 300, for example, a display, a keyboard, a touch screen, and a microphone, to the user interface module 323. can The I/O interface 330 may receive user input (eg, voice input, keyboard input, touch input, etc.) together with the user interface module 323 and process a command according to the received input.

[In the case of a computing device] The display 330 may output various interface screens provided by the cutting tool recommendation platform server/device 300 . For example, display 330 may be a touch-sensitive display. A touch-sensitive display may provide an input interface and an output interface between a device and a user. For another example, the display 330 may be a touch screen. A touch screen may include a sensor that accepts a user's input based on haptic and tactile contact. As another example, the display 330 may be a sound display. When the cutting tool recommendation platform server/device 300 includes a sound display, sound may be output without a separate audio device.

The processor 340 is connected to the communication interface 310, the memory 320, and the I/O interface 330 to control the overall operation of the cutting tool recommendation platform server 300 and applications stored in the memory 320. Alternatively, various commands for executing the cutting tool recommendation platform system may be executed through a program.

The processor 340 may correspond to an arithmetic device such as a central processing unit (CPU) or an application processor (AP). In addition, the processor 340 may be implemented in the form of an integrated chip (IC) such as a System on Chip (SoC) in which various computing devices are integrated. Alternatively, the processor 340 may include a module for calculating an artificial neural network model, such as a Neural Processing Unit (NPU).

In various embodiments, the processor 340 receives information on tools, workpieces, and machine tools, and determines information on tools by using the learning-based cloud application service as inputs, using the input tools, workpieces, and machine tools , and information on the determined tool can be matched to the machining site.

Figure 4 is a schematic diagram of the function and use of the cutting tool recommendation platform system according to the use according to an embodiment of the present invention.

The main functions of the cutting tool recommendation platform system according to the purpose of the present invention include cutting condition recommendation, cutting tool recommendation, tool life (machining efficiency) prediction, machining process monitoring diagnosis, on-site machining condition optimization, and productivity improvement through a cutting simulator. can do.

First, the cutting condition recommendation function builds the machinability index and specific cutting coefficient for various workpiece materials, and can derive cutting conditions for new materials that are not installed by loading them on the platform.

More specifically, the quality and productivity of the cutting process depend on the cutting conditions (cutting speed, feed amount, cutting depth), and the cutting conditions are very different depending on the characteristics of the workpiece. It takes a lot of time to derive suitable cutting conditions for cutting. Therefore, as long as there is a reliable machinability rating (MR) and specific cutting factor for these workpieces, suitable cutting conditions can be derived in a relatively short time. conditions can be derived.

Next, the cutting tool recommendation function collects, classifies, and analyzes vast tool data in connection with domestic tool manufacturers, and loads it into the platform to select tools suitable for the characteristics of various work materials.

For example, as the industry diversifies, the types of parts and required functions diversify, so there are many types of machine tools and countless cutting tools. Recently, with the advent of new high-tech materials such as CFRP, the number of types of workpieces is gradually increasing. are doing For this reason, cutting tools are not standardized and various new tools are gradually appearing, making it difficult to select the optimal tool for cutting of a specific workpiece, and the theory or rule for tool selection has not been established. Depending on knowledge, the present invention can select a more efficient cutting tool for a new workpiece by recommending a cutting tool based on big data.

Next, the tool life (machining efficiency) prediction function builds a platform that can predict productivity tool life and abnormal conditions of tools according to the cutting conditions of various workpieces, and can be used for machining monitoring diagnosis and optimization of machining processes at the machining site. .

For example, during cutting, various forms of abrasion occur on the cutting tool due to contact with the workpiece, and abnormal conditions occur in the tool due to various causes. Therefore, it is very important to predict the abnormal condition of the tool and replace the tool at the right time. However, in the current machining site, tools are replaced with the experience and know-how of processing technicians, so there is a problem that the quality of the workpiece and tool life are not uniform. Therefore, through the cutting tool recommendation and prediction function of the present invention, the appropriate cutting tool It is possible to improve processing efficiency by recommending and predicting the lifespan accordingly.

Next, the machining process monitoring and diagnosis function can be used to diagnose and control abnormal conditions in the manufacturing process by establishing a platform capable of monitoring and diagnosing abnormal conditions in the manufacturing process.

For example, recent automation, unmanned intelligence, and autonomy of cutting processing are being promoted. Currently, automation and unmanned automation are applied in many processing sites. In particular, for unmanned operation, it is necessary to monitor the machining process in real time and diagnose and respond immediately when an abnormality occurs, so this problem can be solved through the machining process monitoring diagnosis function of the present invention.

Next, the field processing condition optimization function collects various signals during processing from the production field processing equipment, compares and analyzes the cutting force obtained through experiments, and can be used to derive the optimal processing conditions for the production field processing equipment.

For example, processing conditions at production sites are different for each production site because the type of machine tool used and the working environment are different. In particular, because machine tools have different power sizes and rigidities depending on the type, even if the same workpiece is processed, cutting conditions may vary depending on the production site. Therefore, through the on-site processing condition optimization function of the present invention, suitable cutting conditions can be provided according to the working environment.

Then, the productivity improvement function through the cutting simulator connects and utilizes actual machine tool applications (interactive programs, tool management programs, machining simulation programs) to improve productivity using the developed cutting tool data platform, By building an ISO 13399-based tool information DB that includes tool-related information required in the field, the productivity of the NC program creation and verification process can be increased.

For example, in a manufacturing site using machine tools, cutting tool information needs to be managed throughout the entire cycle, including the process preparation stage, process monitoring stage, and process result analysis stage. However, there is currently no such support program in Korea. In addition, in actual cutting, unexpected things such as collision and interference of tools often occur, and in order to solve this problem, it is necessary to find and respond to problems through virtual machining before actual machining. In addition, cutting simulator software requires not only catalog information of cutting tools but also dimensional shape information, and most of current cutting tools do not provide standardized information, requiring considerable effort for virtual machining, which is a waste factor at the manufacturing site. It's working.

Accordingly, the present invention uses ISO 13399-based tool information, tool shape model information, and cutting condition data to efficiently select tools and select cutting conditions in the NC program generation stage, collision check in the NC program verification stage, and cutting load prediction and program optimization can be realized.

According to the above function, the present invention recommends the most suitable cutting tool and cutting conditions for the workpiece and processing process from the platform server using the Internet at the parts manufacturing site, minimizing operator's judgment error and trial and error, and contributing to productivity and quality improvement. can

Furthermore, the present invention standardizes tool shape variables used under different names for each tool manufacturer according to ISO 13399-based international standards, converts them into a DB, and provides them through a platform, so that accurate machining process design can be possible through CAM.

In addition, the present invention can be used for the development of digital twin technology to which advanced virtual machining technology is applied by providing key physical property information such as hardness elongation and tensile strength required for a workpiece cutting process prediction model.

In addition, the present invention can accurately determine the tool replacement time during machining, thereby enabling tool replacement at the correct time, thereby minimizing tool cost and part reprocessing cost loss due to inaccurate tool replacement and contributing to productivity improvement.

In addition, the present invention can be used in various industrial fields that require cutting processing by adding information on tools and new materials to be developed in the future by establishing a foundation capable of continuously updating cutting tool data and workpiece data.

In addition, the present invention can improve the productivity and competitiveness of the manufacturing industry by building a DB for tools and workpieces and reducing cost, time, and delivery time at the actual parts processing site through the convergence of the machining process and ICT technology.

As a result, the present invention can contribute to strengthening the competitiveness of the domestic manufacturing equipment and parts manufacturing industry by utilizing digital twin technology to which the characteristics of cutting machines such as machining centers and turning centers are applied and used for high value-added parts processing.

5 is an exemplary view of a learning model of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.

The learning model used in the cutting tool recommendation platform system according to the purpose according to the embodiment of the present invention uses parameters such as tool data (cutter type, tool material, etc.) and machining conditions (RPM, Feed, etc.) for machining monitoring-based learning. Based on this, actual machining data (CNC load, sensor acceleration, vibration, current) through actual machining can be acquired and compared with the reference machining load (tool dynamometer cutting force) to train the machining data.

At this time, the data for learning is filtered and preprocessed from unnecessary data in the actual processed data, and extracts only features with high correlation to generate random forest (RF), support vector machine (SVM), convolution neural network (CNN) It can be used for learning through machine learning algorithms, etc., and through this, processing load, tool life, etc. can be estimated.

6 is a flowchart illustrating the use of a cutting tool recommendation platform system according to use according to an embodiment of the present invention.

The cutting tool recommendation platform system according to the purpose according to the embodiment of the present invention selects the processing method and the processing shape, selects the workpiece, selects the tool shape and tool material, and presents the tool candidate Displays the tool life band according to the cutting speed based on the experiment It can optimize machining through comparison, verification, and mechanical learning, and determine (recommend) tool life, diagnosis of machining process monitoring, and provide preliminary verification through a cutting simulator.

More specifically, in the selection of the processing method and the processing shape, the user at the machining site can connect to the cutting tool data platform, select the processing method (turning, milling, etc.), and then select the specific processing shape. For example, in the case of turning, external machining, internal machining, face machining, grooving, threading, etc. can be selected.

Next, the selection of the workpiece is based on the ISO standard for the workpiece to be processed, K cast iron, M stainless steel, P steel, N AI alloy, S heat-resistant alloy (Ti alloy, Ni alloy, etc.), H high hardness material or quenched steel can choose

Next, for the tool shape and tool material selection, the size, dimensions, shape, and type of coating of the tool are selected in the tool shape, and cemented carbide, high toughness cemented carbide, ultrafine particle cemented carbide, coated cemented carbide, cermet, coated cermet, and ceramics are selected from the tool material. , cBN sintered body, coated ceramic, diamond sintered body can be selected, and at this stage, cutting conditions (cutting speed, feed amount, depth of cut) can be changed.

Then, as for tool candidate presentation, a tool candidate list of domestic tool manufacturers suitable for the selected workpiece material and cutting conditions may be presented. For example, tool A of TaeguTec, tool B of YGwon, tool C of OSG Korea, and tool D of Within may be presented.

Then, the tool life band according to the experimental cutting speed can be displayed as a band according to the experimentally based cutting speed when machining the selected workpiece with the selected tool under the set cutting conditions. In other words, changing the cutting speed in the graph of Recommended Machining Conditions can change the tool life.

Then, optimization of machining through comparison, verification, and mechanical learning, diagnosis of machining process monitoring, and determination of tool life are a process of comparison and verification between the experimental-based machining conditions recommended by the above process and data obtained from actual manufacturing sites. , and through mechanical learning, machining optimization, machining process monitoring and diagnosis, and tool life expectancy can be predicted.

Then, the preliminary verification through the cutting simulator is based on ISO 13399 tool information provided by the cutting tool platform, tool shape information, non-cutting coefficient between tools and workpieces, tool life information, etc. Virtual verification of the NC program and cutting force prediction , and based on this, program optimization can be performed.

Although one embodiment of the present invention has been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and may be variously modified and implemented without departing from the technical spirit of the present invention. there is. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be construed according to the claims below, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

100: user device, first user device
110: memory interface 120: processor
130 Peripheral Interface 140 I/O Subsystem
141: touch screen controller 142: other input controller
143: touch screen
144: other input control devices
150: memory 151: operating system
152: communication module 153: GUI module
154: sensor processing module 155: phone module
156: applications
156-1, 156-2: application
157: digital assistant client module
158: user data
160: motion sensor 161: light sensor
162 Proximity sensor 163 Other sensors
170: camera subsystem 171: optical sensor
180: communication subsystem
190: audio subsystem
191: speaker 192: microphone
200: manager device
300: cutting tool recommendation platform server
310: communication interface
311 wired communication port 312 wireless circuit
320: memory
321: operating system 322: communication module
323: user interface module 324: application
330: I/O interface 340: processor
500: monitoring device
600: shooting device

Claims (1)

Entering information on tools, workpieces and machine tools;
Determining information about a tool by using the tool, workpiece, and machine tool input using a learning-based cloud application service, and
A cutting tool recommendation platform system according to the purpose, comprising the step of matching the determined information on the tool to the machining site.

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