JP2017027449A - Platform and method for optimizing manufacturing efficiency by monitoring and predicting tool state - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a platform and a method for optimizing a manufacturing efficiency by monitoring a tool state and estimating a normality through provision of data acquired from a sensor on a manufacturing apparatus using a service box and by predicting power consumption patterns.SOLUTION: Sensor data is monitored and analyzed continuously. A tool is worn away until it has to be replaced, when the power consumption increases and the vibration also increases to a certain level. A service box is connected to a sensor on a manufacturing apparatus. The service box receives appropriate data from the sensor and transfers the data to a cloud server in real time. If it is determined that the tool needs to be switched, a person in charge will be notified of the necessity and the worn tool will be replaced with a sharp one.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a manufacturing system, and in particular, provides data acquired from sensors on manufacturing equipment using a service box, thereby monitoring tool status, analyzing normality, and predicting power consumption. The present invention relates to a platform and method for optimizing manufacturing efficiency.

  In factories that manufacture products, products are manufactured using various mechanical devices. The performance of the machinery has a direct impact on production costs and profits gained from product sales. In order to improve the performance of machinery, traditional factories have hired many technicians to maintain machinery.

  In many traditional manufacturing facilities, replaceable tool parts such as drill bits, router bits, and cutting tools that contact, cut, mold or form the material to create the product itself or part of the product. A mechanical device equipped with is used.

  When the tool repeatedly contacts the material, the tool begins to wear, and the tool becomes dull as it continues to be used.

  However, conventional manufacturing systems do not have an effective way of determining when a tool change is necessary. In a general factory, a tool is changed after a certain amount of product is manufactured, after a working time, or after a cutting operation. However, the tool change is determined based on the experience of the operator or engineer, set up with a fixed numerical value, and does not reflect the actual state of the tool. Unfortunately, such tool change methods waste material, cost and labor, increase production costs and reduce manufacturing efficiency.

  Therefore, there is a need for an effective method of optimizing manufacturing efficiency by using a platform for acquiring data from manufacturing equipment and utilizing intelligent tool condition monitoring, normality analysis and prediction tools based on the data.

  In order to maintain the conventional advantages and overcome the drawbacks of the conventional methods, the object of the present invention is to monitor the tool status, analyze the normality based on the data obtained from the sensors on the manufacturing equipment using the service box, It is another object of the present invention to provide a manufacturing efficiency optimization platform and method for predicting power consumption.

  The present invention evaluates reliability within the life cycle of a system in order to detect possible failures and reduce risk. Knowing and preventing the failure of a device in advance increases the overall reliability and safety of the product and operation, while saving considerable time and money. External add-on sensors and control signals are used for degradation monitoring and generation of normality information. The level of machine health is generated from a combination of the individual subsystems of critical subsystems and their components.

  The production efficiency optimization platform and method of the present invention includes a service box, an application server, an agent server, and a cloud server.

  The service box includes a hardware box with electronic circuits, firmware and software. The service box is connected to the sensor of the manufacturing equipment. The service box requests and receives appropriate and accurate data from the sensors and transfers the data to the cloud server in real time.

  The present invention provides an efficient and effective way to determine when an exchangeable tool is optimal to change. Tool status monitoring is performed by a service box that acquires sensor data from vibration sensors and power consumption sensors on the machine. Sensor data is constantly monitored and analyzed.

  As the power usage increases and the vibration increases to a predetermined level, the present invention determines that the tool has become dulled or worn to the extent that a tool change is necessary. The appropriate person is then notified and replaced with a sharp tool. The present invention can reduce the waste of materials and labor by automatically specifying the timing when tool change is necessary.

  The application server includes a plurality of analysis tools and management applications that are being developed or completed by application designers and programmers and published on the application server. The agent server includes a plurality of analysis and management tools that are downloaded from the application server and can be used directly on the agent server or downloaded to a cloud server. Analysis and management tools include applications that analyze sensor data and manage manufacturing efficiency to produce effects that maximize the effectiveness of the entire device. Analysis / management tools include, for example, tools such as troubleshooting, production schedule, quality control, normality analysis, usage expansion function and energy monitoring. The cloud server includes a plurality of analysis tools and management tools provided from the agent server. The cloud server uses an analysis tool and a management tool that can be used on the agent server or directly on the cloud server having sensor data received in real time from the service box.

  The manufacturing efficiency optimization platform and method of the present invention further includes a client device. The client device includes a service dashboard for effectively visualizing and displaying various results of the analysis and management tools provided by the cloud server. Client device users communicate with the cloud server via the service dashboard to effectively monitor and manage various aspects of the product.

  According to the present invention, the production process is effectively monitored, analyzed, predicted and managed, the efficiency of machinery and manufacturing is increased for cost reduction and increase in profit, the tool condition is monitored, and the energy consumption is predicted. Can optimize the production.

  For a better understanding of the present invention, the drawings are attached as part of this specification. The drawings illustrate embodiments of the invention and serve to explain the principles of the invention.

It is a figure which shows the manufacturing efficiency optimization platform and tool state monitoring method in the Example of this invention. It is a flowchart which shows the manufacturing efficiency optimization platform and tool state monitoring method in the Example of this invention. It is a flowchart which shows the manufacturing efficiency optimization platform in the Example of this invention, the tool state monitoring method, and the power consumption prediction method. It is a graph which shows a sensor signal. It is a graph which shows a control signal. It is a graph which shows the electric power average after averaging. FIG. 6 is a graph showing selected tool state monitoring features. FIG. It is a graph which shows the normality evaluation value and average power consumption per path. 4 is a graph illustrating a plurality of cloud servers of a manufacturing efficiency optimization platform and method in an embodiment of the present invention. 4 is a graph illustrating a plurality of service boxes of a manufacturing efficiency optimization platform and method in an embodiment of the present invention.

  The above and other objects of the present invention will become more apparent to those skilled in the art from the description of the preferred embodiments detailed below.

  The above-described outline and the details to be described later are representative examples, and are intended to explain the claimed invention in more detail.

  Now, preferred embodiments of the present invention will be described in detail. This embodiment is illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings and the description to refer to the same or like elements.

  Please refer to FIG. The manufacturing efficiency optimization platform and tool condition monitoring method 100 includes an application server 110, an agent server 120, a service box 130, a cloud server 140, and a client device 150.

  The application server 110 is connected to the agent server 120. The agent server 120 is connected to the application server 110 and the cloud server 140. The service box 130 is connected to the cloud server 140 and a plurality of sensors of manufacturing equipment. The client device 150 connects to the cloud server 140. The cloud server 140 is connected to the agent server 120, the service box 130, and the client device 150.

  The connection between the application server 110, the agent server 120, the service box 130, the cloud server 140, and the client device 150 includes a wireless network, a wired network, or a combination of a wireless network and a wired network.

  Application server 110, agent server 120, cloud server 140, and client device 150 include servers, computers, tablets, smartphones, or other electronic devices that can connect to platform 100.

  The application server 110 includes an analysis and management tool application that is under development or has been completed and can be distributed. Developers utilize the application server 110 when creating and programming analysis and management tools. When the analysis / management tool is ready for distribution, the analysis / management tool is published on the application server 110 and notified to the agent server 120.

  The agent server 120 connects to the application server 110 to access and download the published analysis / management tool.

  Analysis and management tools include, for example, data acquisition, health index extraction and selection, health assessment, visualization, performance prediction, quality analysis, planning, inventory, equipment availability, monitoring and manufacturing, troubleshooting, production schedule, quality Tool condition monitoring and analysis tools for management, health analysis, usage expansion function, energy monitoring, knowledge management, data analysis, system management, customer management, remote monitoring, technical documentation, service management, scheduling, or employee management Including.

  Customized tools are requested by the cloud server 140 from the agent server 120 and developed by the application server 110 to meet specific needs from users of the cloud server 140.

  Service box 130 includes a hardware box with a microprocessor, non-transitory memory, electronic circuitry, firmware, software and input / output connections. The service box 130 is connected to a plurality of sensors on the manufacturing equipment. The service box 130 requests and receives appropriate and accurate data from these sensors and forwards these data to the cloud server 140 in real time.

  For example, the programmable logic control devices (PLC), the computer numerical control device (computer numerical control, CNC), the pressure sensor, the power sensor, the vibration sensor, the temperature sensor, the acoustic sensor, and the GPS (global positioning system) sensor. , And integrated resource planning (ERP) / manufacturing execution systems (MES), IT (information technology) systems.

  Service box 130 can be configured to connect to one or more sensors to receive data for the desired sensor.

The cloud server 140 receives sensor data from the service box 130 in real time. The cloud server 140 can also reconfigure a plurality of sensors connected to the service box 130. The cloud server 140 includes a microprocessor, non-transitory memory, and a plurality of analysis and management tools provided from the agent server 120.
The cloud server 140 uses an analysis tool and a management tool that can be used on the agent server 120 or directly on the cloud server 140 having sensor data received from the service box 130 in real time. In the embodiment of the present invention, the analysis / management tool is stored and executed locally on the cloud server 140. In another embodiment, the analysis / management tool is stored and executed on the agent server 120.

  The manufacturing efficiency optimization platform and tool condition monitoring method 100 of the present invention further includes a client device 150. The client device 150 includes a service dashboard 160 for effectively visualizing and displaying various results of analysis and management tools provided from the cloud server 140. A user of the client device 150 communicates with the cloud server 140 via the service dashboard 160 to effectively monitor and manage various aspects of the product.

  Please refer to FIG. 2 which shows the manufacturing efficiency optimization platform and the tool condition monitoring method in the embodiment of the present invention.

  By tracking defect features and using analytical tools to estimate the state of a part, the main factors of machine tool downtime resulting from excessive wear and breakage of the cutting tool during machining operations can be avoided. . As a result, the present invention increases productivity, improves and maintains the quality of machine parts, and reduces consumption associated with automated manufacturing systems.

  The present invention provides an efficient and effective way of determining when it is best to change a replaceable tool. Tool status monitoring is performed by a service box that acquires sensor data from vibration sensors and power consumption sensors on the machine. Sensor data is constantly monitored and analyzed.

  As power usage increases and vibration increases to a predetermined level, the present invention determines that the tool has become dulled or worn to the extent that a tool change is necessary. By automatically identifying when the tool needs to be changed, waste of materials and labor can be reduced.

  Tool wear detection signals such as spindle power and vibration from the machine tool are collected and digitized by the service box. The selected control signal is recorded so that it is properly segmented into the sensor signal. Then, the data stream is transmitted to the cloud server. Thereafter, the segmentation module is activated and the leading and trailing samples that are not important to the actual cutting operation are removed. The remaining data segments are accumulated and processed by a tool condition monitoring module that estimates the state of normality from the test data.

  The manufacturing efficiency optimization platform and tool condition monitoring method 200 in the embodiment shown in FIG. 2 obtains power and vibration data at step 210 from appropriate sensors on the machine or tool by the service box. In addition to the energy and vibration data, other control signals are also acquired from the sensor by the service box. In step 220, the data acquired by the service box is transmitted to the cloud server.

  In step 230, the cutting data of the part where the tool is actually in contact with the manufacturing material and the cutting data of the part which is not in contact with the manufacturing material because the tool is not moving or being reset are analyzed and managed. Are extracted by the tool status monitoring module. In step 240, the cutting data extracted by the tool condition monitoring module is analyzed. In step 250, the tool status monitoring module evaluates the normality of the tool based on the analysis result of the extracted cutting data. In step 260, the normality assessment is analyzed to determine the normality of the tool state. In step 270, if the normality assessment analysis determines that the tool is worn and should be replaced, the tool is replaced and if it is determined that the tool is still usable, the use is Will continue.

  In an embodiment of the present invention, a service box or cloud server notifies a suitable person, such as an engineer, technician or machine operator. After being notified that the blunt tool has been replaced with a sharp tool, production is resumed quickly.

  In the embodiment of the present invention, the service person or the cloud server notifies the person in charge in advance that the tool needs to be replaced. This allows the person in charge to save time by preparing new tools in advance. When the tool needs to be replaced, the person in charge is notified again.

  FIG. 3 shows a flowchart of a manufacturing efficiency optimization platform, a tool state monitoring method, and a power consumption prediction method 300 in the embodiment of the present invention.

  When the tool state monitoring module is started, in step 310, sensor data and control data transmitted from the service box to the cloud server are read out. This data includes, for example, computer numerical control (CNC) data, vibration data, power usage data, current amount data, and data collection (DAQ) data. In step 320, the data is filtered, and in step 330, an averaging process is performed. In step 340, a segment is selected, and in step 350, appropriate features are extracted. In step 360, normality evaluation is performed, and in step 370, a normality evaluation file is created.

  The present invention includes a prediction module that predicts future power consumption. By predicting power consumption, overcharge of energy usage and power limitations can be avoided, production schedules at manufacturing facilities can be more effectively established, and tool manufacturers can improve tools.

  In step 380, the normality evaluation file is compared with the previously created evaluation file. For example, a currently created health assessment file is compared to one or more previously created health assessment files.

  In step 390, the power consumption and current amount are determined, and in step 395, future power consumption trends are predicted.

When the tool condition monitoring module is started, it automatically retrieves one or more appropriate data files. Then, the position of the file path displayed in this file is specified, and the associated file is analyzed. The signal or data obtained from it undergoes a series of processes that extract features from a stable portion of the signal. The stable part is defined as data of a part where the tool is actually in contact with the workpiece.
After the power amount data is subjected to an averaging process, a stable portion of the segment is specified using an average method. The time position of the stable part is used to isolate the equivalent segment in the vibration data. Features are then calculated from the stable portions of the vibration and power signals, and summary statistics such as average values, standard deviation values, minimum values, and maximum values are obtained.

  The selected feature is used for a normality evaluation technique using Euclidean metric.

  Even if the result of the normality evaluation is a high value normalized at first, the normality evaluation value decreases almost monotonously and deteriorates as the cutting tool is continuously used. As a result, when the normality evaluation value falls below a predetermined value such as 0.5, the tool is replaced. Determining when the tool should be replaced based on the normality evaluation value is relatively similar to performing a cutting test under similar machining conditions and parameters.

  When it is determined that the tool has slowed down, an appropriate person is notified through the service box or the cloud server, and the person in charge replaces the blunt tool with a sharp tool.

  The manufacturing efficiency optimization platform and tool condition monitoring and prediction method of the present invention provides real-time monitoring of the tool condition, which allows the manufacturer to easily grasp the condition of the tool. In addition, the prediction module allows manufacturers to improve schedules and avoid power limitations based on power consumption trends.

  As a reference for the contents described above, refer to FIG. 4A, which is a graph showing sensor signals, and FIG. 4B, which is a graph showing control signals. The upper graph in FIG. 4A is vibration data, and the lower graph is power amount data.

  Reference is now made to FIG. 5A, which is a graph showing the average power after averaging. The stable part of the signal 15 refers to the leveling part of the highest or lowest power average. The time position of the stable part is used to isolate the equivalent segment in the vibration data. See also FIG. 5B, which is a graph showing the characteristics of selected tool condition monitoring. The characteristic is calculated from the stable part of both the vibration signal and the power signal.

  See FIG. 6, which is a graph showing normality evaluation values per path and average power consumption. The upper graph shows normality evaluation values, and the lower graph shows power consumption. From this figure, it can be seen that the tool wears as the power consumption increases. A decrease in the normality evaluation value and an increase in power consumption means that the tool is worn. When the normality evaluation value decreases to a predetermined value, the tool is replaced.

The production efficiency optimization platform and tool condition monitoring method of the present invention further includes creation of an analysis and management tool. Application developers use application servers to create and develop analysis and management tools used within the platform.
The analysis / management tool being developed or completed is stored on the application server. When these tools are completed, they are published on the application server, and the agent server is notified that preparation for distribution is complete. These analysis and management tools are stored on the application server during development and release. After the agent server is notified that these analysis / management tools have been released, these analysis / management tools are downloaded from the application server to the agent server. The cloud server is notified of a new or updated version of the analysis / management tool.

  An analysis / management tool on the agent server is provided to the cloud server. In the embodiment of the present invention, the analysis / management tool is automatically downloaded to the cloud server. In another embodiment of the present invention, the analysis / management tool is downloaded as required or desired by the cloud server.

  The service box connects to one or more machine sensors and receives appropriate sensor data from these sensors. The sensor data includes, for example, power consumption, temperature, viscosity, noise level, vibration, material quantity, production quantity, and the like. The service box transmits sensor data to the cloud server in real time, and the transmitted sensor data is received by the cloud server.

The cloud server uses analysis and management tools on sensor data. For example, if the sensor data includes the current temperature of the mold on the production equipment, the analysis and management tool tracks that temperature, creates a temperature record and history, warns if the temperature is too high or too low, Do other useful analyses.
The result obtained from the analysis / management tool on the sensor data is provided from the cloud server to the client device. In an embodiment of the present invention, the result is automatically sent to the client device. In yet another embodiment, results are provided based on requests from client devices. The results are then displayed on the service dashboard on the cloud server.

  The service dashboard on the client device provides a means for the user to access the analysis results and data provided from the cloud server. The service dashboard includes, for example, a display of available tools, reports, graphs, charts, maps, history, logs, schedules, quantities, inventory, documents, orders or plans.

The service dashboard displays icons for available tools and data that is accessible to users of client devices.
Click on one icon to visualize the selected icon. For example, if the user selects the production quantity icon, the service dashboard displays a graph of the current production volume as well as the past production volume history. As a result, the user can easily view various information in real time rather than reading the printed report.

The service dashboard in the example can be configured for individual users and displays only the tools and data appropriate for each user. For example, quality assurance personnel do not view financial, ordering or shipping information. This simplifies platform usage and avoids information overload and confusion.
Also, the service dashboard in the embodiment is configured to display appropriate data in real time on the client device. For example, employees on the manufacturing floor can see equipment performance in real-time graphs on their client devices and are not fooled by unnecessary data.

  Please refer to FIG. The present invention provides flexibility by proposing various configurations of cloud servers and platform services to customers. In the embodiment shown in FIG. 7, a plurality of cloud servers are connected to the agent server 120. Cloud server A 140A connects to service box A 130A, cloud server B connects to service box B 130B, and both cloud server 140A and cloud server 140B connect to the same server agent 120.

Cloud server A 140A is configured as a private cloud server. Private cloud servers contain private data that only customers can access.
The cloud server A 140A connects to the agent server and downloads the analysis / management tool. For example, all data such as sensor data, manufacturing data, analysis data, and management data is stored on the cloud server A 140A and is not disclosed. Private cloud servers, such as cloud server A 140A, provide a high level of security for customers' top secret manufacturing data.

  The cloud server B 140B is configured as a semi-public cloud server, and part or all of the data on the cloud server B 140B can be used by the service agent 120. The service agent 120 provides not only an analysis / management tool but also a cloud data service to the cloud server B 140B. For example, the service agent 120 periodically updates analysis and management tools, provides access to new tools, analyzes manufacturing data, and maintains the cloud server B 140B. A semi-public cloud server such as cloud server B 140B is more economical for small businesses and customers because it does not require a dedicated technical support team.

  In an embodiment of the present invention, the analysis and management tool is based on subscription. Instead of purchasing the tool, the customer can select the necessary analysis and management tool and pay the fee for using the tool. This allows customers to avoid spending money on unnecessary tools and further reduces the cost of installing the platform of the present invention.

  In embodiments of the present invention, analysis and management tools are purchased separately at different prices depending on the complexity of the tools.

  In the embodiment of the present invention, the analysis / management tool is a rental. This makes it possible for the customer to return when the tool is finished or no longer needed. For example, if the tool is an inventory efficiency tool that analyzes inventory efficiency annually, the customer can rent and return the tool in a short time once a year.

  In an embodiment of the present invention, the service box is lent out to a customer. This provides the flexibility that the number of service boxes can be increased or decreased as the number of devices in the manufacturing facility increases or decreases. By renting the service box, the customer can easily control the cost of the platform of the present invention, reducing initial costs compared to purchasing the service box first.

  Please refer to FIG. In the embodiment shown in FIG. 8, multiple service boxes connect to the same cloud server. Service box A 130A is connected to machine A 300A and receives sensor data from sensors A, B, and C of machine A 300A. The service box A 130A transmits the received sensor data to the cloud server 140. Service box D130D is connected to machine D300D and receives sensor data from sensors D and E of machine D300D. The service box D130A transmits sensor data to the cloud server 140.

  The cloud server 140 is connected to a plurality of client devices (client device F150F and client device G150G). Data such as sensor data, analysis data, management data and machine data from both machine A300 and machine D300D is created to be available to client device F150F and client device G150G or based on any access rights The

  It will be apparent to those skilled in the art that various modifications can be made without departing from the scope and spirit of the invention. In view of the above, all the various changes and color changes made without departing from the scope of the present invention are included in the present invention and correspond to the present invention.

15 Signal 100 Manufacturing efficiency optimization platform and tool condition monitoring method 110 Application server 120 Agent server 130 Service box 130A Service box A
130B Service box B
130D Service box D
140 Cloud server 140A Cloud server A
140B Cloud server B
150 Client device 150F Client device F
150G Client device G
300A Machine A
300D Machine D

Claims (20)

Acquiring data from sensors on the machine by means of a service box;
Sending the acquired data to the cloud server;
Extracting the data of the portion where the tool on the machine was in contact with the workpiece from the acquired data;
Analyzing the extracted data; and
Performing a normality assessment of the tool from an analysis of the extracted data;
Analyzing the normality assessment to determine a normality state of the tool;
Replacing the tool if the normality assessment indicates that the tool needs to be replaced;
Continuing production using the tool if the normality assessment indicates that the tool is still usable.
Tool condition monitoring method for manufacturing efficiency optimization platform.   The method of claim 1, wherein the acquired data includes power consumption data and vibration data.   The method of claim 2, wherein the acquired data further includes computer numerical control (CNC) data and data acquisition (DAQ) data.   Further comprising: determining a normality evaluation value of the tool from the normality evaluation; and determining that the tool needs to be replaced when the normality evaluation value reaches a predetermined value. Item 2. A tool state monitoring method for a manufacturing efficiency optimization platform according to Item 1.   The optimal manufacturing efficiency according to claim 1, further comprising: comparing the normality evaluation with a previous normality evaluation; determining a power consumption; and predicting a future power consumption. Tool status monitoring method for a computerized platform.   The tool status monitoring method of the manufacturing efficiency optimization platform according to claim 1, further comprising: notifying a person in charge when the tool needs to be changed.   The tool status monitoring method of the manufacturing efficiency optimization platform according to claim 1, further comprising: notifying a person in charge that the tool needs to be changed in advance. Obtaining sensor data and control data from manufacturing equipment by a service box;
Transmitting the sensor data and the control data to a cloud server by the service box;
Filtering the sensor data and the control data;
Performing an averaging process on the filtered data;
Selecting a segment from the result of the averaging process;
Extracting features from the segments;
Performing a health assessment; and
Determining a normality evaluation value of a tool state on the manufacturing equipment,
Tool condition monitoring method for manufacturing efficiency optimization platform.   The tool state monitoring method of the manufacturing efficiency optimization platform according to claim 8, wherein the sensor data and the control data include vibration data and power consumption data.   The method of claim 9, wherein the sensor data and the control data further include computer numerical control (CNC) data and data acquisition (DAQ) data.   9. The method of claim 8, further comprising determining that the tool needs to be replaced when the normality evaluation value reaches a predetermined value.   9. The optimal manufacturing efficiency according to claim 8, further comprising: comparing the normality evaluation with a previous normality evaluation; determining a power consumption; and predicting a future power consumption. Tool status monitoring method for a computerized platform.   9. The method of claim 8, further comprising: comparing the normality evaluation with a plurality of previous normality evaluations; determining a power consumption; and predicting a trend of future power consumption. Tool status monitoring method for the platform for optimizing manufacturing efficiency.   The tool state monitoring method of the manufacturing efficiency optimization platform according to claim 8, further comprising: replacing the tool when the normality evaluation value reaches a predetermined value.   The manufacturing efficiency optimization platform tool condition monitoring method according to claim 14, further comprising: notifying a person in charge when the tool needs to be replaced.   15. The manufacturing efficiency optimization platform tool condition monitoring method according to claim 14, further comprising: notifying a person in advance that the tool needs to be replaced. Obtaining vibration data, power consumption data and control data from sensors on the manufacturing equipment by the service box;
Transmitting the vibration data, the power consumption data and the control data to the cloud server by the service box;
Filtering the vibration data, the power consumption data and the control data;
Performing an averaging process on the filtered data;
Selecting a segment from the result of the averaging process, wherein the segment includes when a tool on the manufacturing equipment contacts the workpiece;
Extracting features from the segments;
Performing a health assessment; and
Determining a normality evaluation value of the tool state on the manufacturing equipment;
Replacing the tool when the normality evaluation value reaches a predetermined value,
Tool condition monitoring method for manufacturing efficiency optimization platform.   The optimal manufacturing efficiency according to claim 17, further comprising: comparing the normality evaluation with a previous normality evaluation; determining a power consumption; and predicting a future power consumption. Tool status monitoring method for a computerized platform.   The method of claim 17, further comprising: obtaining and transmitting computer numerical control (CNC) data and data collection (DAQ) data by the service box.   18. The manufacturing efficiency optimization platform according to claim 17, wherein when the vibration data indicates an increase in vibration and the power consumption data indicates an increase in power consumption, the normality evaluation value decreases and tool wear is indicated. Tool condition monitoring method.

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