TW201713456A - Manufacturing efficiency optimization platform and tool condition monitoring and prediction method - Google Patents

Abstract

A platform and method for optimization of manufacturing efficiency by utilizing a service box to provide data obtained from sensors on production machines in order to perform tool condition monitoring and health assessment and predict power consumption trends. The sensor data is continuously monitored and analyzed. When power usage increases and vibration increases to a predetermined level the tool has become dull or worn to the point that the tool needs to be changed. The service box is coupled to sensors on a production machine. The service box receives appropriate data from the sensors and transfers the data to a cloud server in real-time. When it is determined that the tool needs to be replaced, notification is made and personnel replace the worn tool with a sharp tool.

Description

Manufacturing efficiency optimization platform and tool condition monitoring and prediction method

The present invention relates to a production system, and more particularly to a platform and method for optimizing the use of a service box to obtain data from sensors on a production facility to perform cutting tool condition monitoring, health analysis, and power consumption prediction.

According to the manufacturing factory, a large number of machines are used to produce products. The efficiency of the equipment directly affects the cost of the product and the profitability of the product. In order to increase the efficiency of the equipment, the traditional factory employs a large number of technicians to maintain the operation of the equipment.

Many conventional production facilities have variable tools such as drills, boring tools or other cutting tools that come into contact with the material and are cut, shaped or materialed into all or part of the product.

When the tool touches the material several times, it will start to wear. If it continues to be used, the tool will wear and eventually become unusable due to breakage or chipping and need to be replaced.

However, traditional production systems do not have an effective way to determine when a tool should be replaced. In general, the factory will change the tool based on the number of workpieces produced, working hours or cutting area, but based on the experience and number of operators and experts. The settings, which are static, do not reflect the real situation. Unfortunately, the method of replacing scrap materials, material costs, and labor costs with such tools increases production costs and reduces production efficiency.

Therefore, it is necessary to propose a manufacturing efficiency optimization platform and tool condition monitoring and forecasting method, use a platform to obtain the data of production equipment, and use smart tool condition monitoring, health analysis and forecasting tools in the data, the specific structure and its implementation. The way will be detailed below:

The main object of the present invention is to provide a manufacturing efficiency optimization platform and a tool condition monitoring and prediction method, which use a service box to provide information obtained by sensors on a production device to increase production efficiency for performing tool condition monitoring, Health analysis and power consumption prediction.

The present invention assesses the reliability of a system over its life cycle to proactively detect any impending failures and reduce risk, knowing that certain equipment is faulty and preventing significant time and money savings, while improving overall reliability of products and operations. Sex and safety. Externally attached sensors and controller signals are used to monitor and analyze the health messages generated. The health assessment of a device is generated by the health values of its key subsystems and their components.

The manufacturing efficiency optimization platform and the tool state monitoring and prediction method of the present invention comprise a service box, an application server, a proxy server and a cloud server.

The service box includes a hardware box including an electronic circuit, a firmware and a software. The service box is coupled with a plurality of sensors on the production device, and the service box requests and receives appropriate and accurate data from the sensor, and immediately records the data. Transfer to the cloud server.

The present invention provides an efficient and efficient method of determining when a changeable tool should be replaced by a sensor box provided by sensor data acquired by the vibration sensor and the power consumption sensor on the device. Sensor data is continuously monitored and analyzed.

When the power usage is increased and the vibration is increased to a predetermined level, the present invention determines the point at which the tool that has become blunt or worn by the tool has reached a point where it must be replaced. When the appropriate person is notified and the tool is replaced with a sharp tool, the present invention automatically identifies the time at which the tool needs to be replaced to reduce wasted material and labor.

Application servers include complex analysis tools and management applications that are in development or have been completed and published by application designers and programmers. A proxy server includes a plurality of analysis tools and management tools, which are downloaded from an application server, and can be used directly by the user on the proxy server or downloaded to the cloud server. Analysis tools and management tools include applications for analyzing sensor data and production management productivity, maximizing the effectiveness of overall equipment efficiency. Analysis and management tools include, for example, troubleshooting, production scheduling, quality control, health diagnostics, utilization management, and energy monitoring tools. The cloud server includes a complex analysis and management tool provided by the proxy server, and the cloud server uses the analysis and management tool to immediately receive the sensor data transmitted by the service box on the proxy server or directly on the cloud server.

The invention further includes a client device that includes a service dashboard to display an effective visualization of various results of the analysis tools and management tools provided by the cloud server. Users of the client device can effectively monitor and manage all aspects of production through the service dashboard and communicate with the cloud server.

Therefore, the present invention effectively monitors, analyzes, predicts, and manages the manufacturing process, and optimizes the manufacturing process by increasing the production efficiency of the mechanical equipment, monitoring the tool state, and predicting power consumption to reduce costs and increase profits.

The purpose, technical content, features and effects achieved by the present invention will be more readily understood by the detailed description of the embodiments.

The invention provides a manufacturing efficiency optimization platform and a tool state monitoring and forecasting method. Please refer to FIG. 1 , including a manufacturing optimization platform and a tool monitoring method 100 , which is an application server 110 , a proxy server 120 , and a service The box 130, the cloud server 140, and a client device 150 are formed.

The application server 110 is connected to the proxy server 120, and the proxy server 120 is connected to the application server 110 and the cloud server 140, and the complex sensing of the service box 130 and the cloud server 140 and a production machine The client device 150 is connected to the cloud server 140, and the cloud server is connected to the proxy server 120, the service box 130, and the client device 150.

The application server 110, the proxy server 120, the service box 130, the cloud server 140, and the client device 150 also include a wireless or wired network, or even a wireless and wired network. combination.

The application server 110, the proxy server 120, the cloud server 140, and the client device 150 include a plurality of servers, computers, tablets, smart phones, or other electronic devices that can interface with the platform 100.

The application server 110 includes a plurality of analysis and management tool applications that are still in development before, or have been developed and dispatched for use. Developers will use application server 110 to build and plan these analysis and management tools. Once these analysis and management tools are ready to be dispatched, these analysis and management tools will be published in application server 110 and will notify the proxy server. 120.

The proxy server 120 interfaces with the application server 110 to access and download these published analysis and management tools.

These analysis and management tools include several tools such as data monitoring and analysis, data acquisition, health factor extraction and selection, health assessment, visualization, and performance prediction. ), quality analysis, design, inventory, equipment effectiveness, detection and production, troubleshooting, production scheduling, quality control, health diagnostics, utilization Utilization management, energy monitoring, knowledge management, data analysis, system management, customer management, remote monitoring, technical documentation, service management, scheduling, and employee management.

The various customization tools developed by the cloud server 140 from the proxy server 120 and developed by the application server 110 can meet the various special requirements of the users of the cloud server 140.

The service box 130 includes a hardware box with a microprocessor, a non-transitory memory, a complex electronic circuit, a firmware, a software, and a plurality of input/output connectors. Service box 130 is coupled to a plurality of sensors on a production machine. The service box 130 requests and receives appropriate and accurate data from the sensor while transmitting the data to the cloud server 140 in real time.

These sensors include several types of sensors, such as programmable logic controllers (PLCs), computer numerical control (CNC) controllers, pressure sensors, and power sensing. Power sensors, vibration sensors, temperature sensors, acoustic sensors, global positioning system (GPS) sensors, enterprise resource planning (enterprise resource planning) , ERP), manufacturing execution systems (MES) and information technology (IT) systems.

The service box 130 can be configured to connect a desired single or multiple sensors and receive the desired sensor data.

The cloud server 140 will immediately receive the sensor data from the service box 130. The cloud server 140 can also reconfigure the complex sensors that interface with the service box 130. The cloud server 140 includes a microprocessor, a non-transitory memory, and a set of analysis and management tools provided by the proxy server 120. The cloud server 140 utilizes these analysis and management tools that can be used by the proxy server 120, or can be used directly with the cloud server 140, while receiving sensor data that is immediately transmitted from the service box 130. In one embodiment of the invention, these analysis and management tools will be stored in the local machine and executed on the cloud server 140. In yet another embodiment, these analysis and management tools will be stored and executed on the proxy server 120.

The platform and method of manufacturing optimization 100 of the present invention even includes client device 150. The client device 150 includes a dashboard 160 for effectively visualizing the results of various results of the analysis and management tools provided by the cloud server 140.

Please refer to FIG. 2, which is a flow chart of an embodiment of a manufacturing efficiency optimization platform and a tool state monitoring method according to the present invention.

By tracking failure characteristics and using analytical tools to estimate the state of a component, a major source of mechanical downtime can avoid damage to the tool due to excessive wear and cutting during machining operations. Thus, the present invention improves production efficiency, enables better quality of manufactured and maintained workpieces, and reduces expenses associated with automated manufacturing systems.

The present invention provides an efficient and effective method for determining when a replaceable tool is the optimal replacement time. The service box takes sensor data from the vibration sensor and power consumption sensor on the device and provides tool status monitoring. The detector data is continuously monitored and analyzed.

When the power usage is increased and the vibration is increased to a predetermined level, the present invention determines that the tool that has become blunt or worn by the tool reaches a point in time when it must be replaced, and automatically recognizes the timing at which the tool needs to be replaced to reduce wasted material and labor.

The service box collects wear-sensitive signals from mechanical equipment and tools, such as spindle power and vibration, and digitizes the selected controller signals to record the segments in the correct sensor signal, and then streams the two data streams to the cloud server. . Then use a segment module to remove the beginning and end samples without obvious cutting action, and the remaining data segments are stored, and the tool state monitoring module is used to process a given test data. Generate a health status estimate.

In the embodiment illustrated in FIG. 2, the manufacturing efficiency optimization platform and tool condition monitoring method 200 of the present invention includes, in step 210, a service box for obtaining power and vibration data from a suitable sensor on the device or tool. In addition to power and vibration data, other control signals are taken from the sensor using the service box. In step 220, the service box transmits the obtained data to the cloud server.

In step 230, the tool state monitoring module in the analysis and management tool extracts the cutting data, wherein the tool actually contacts the production material, and cuts from the idle or reset non-contact material production data of the tool; step 240, the tool The condition monitoring module analyzes the extracted cutting data. In step 250, the tool state monitoring module performs one of the tool health evaluations according to the analysis result of the proposed cutting data, and analyzes the health evaluation to determine the tool in step 260. The state of health. In step 270, if the analysis result of the health assessment is that the tool has worn out and needs to be replaced, the tool is replaced, and if the analysis result of the health assessment is that the tool can continue to be used, the tool is continuously used.

In one embodiment of the invention, the service box or cloud server notifies an appropriate person, such as an engineer, technician, or machine operator, to replace the worn tool with a sharp tool to quickly resume production.

In one embodiment of the invention, the service box or cloud server will only notify the appropriate personnel when the tool needs to be replaced, which allows the person to get new tools ahead of time to save time. The person will be notified again when the tool needs to be replaced.

Please refer to FIG. 3, which is a flow chart of an embodiment of a manufacturing efficiency optimization platform, a tool state monitoring, and a power consumption prediction method according to the present invention.

When the tool state monitoring module is triggered, the sensor data and the control data transmitted from the service box to the cloud server are read. As described in step 310, the data includes computer numerical control (CNC) data. The vibration data, the power usage data, the current data, and the data acquisition (DAQ) data; the data is filtered in step 320, and an averaging process is performed in step 330, and a step is selected in step 340. 350 takes appropriate complex features, performs a health assessment in step 360, and writes a health assessment profile in step 370.

The invention further includes a prediction module for predicting future power consumption. By predicting power consumption, energy consumption over-capacity and power limitation can be avoided, manufacturing equipment can be more efficiently arranged, and tool manufacturers can improve tool manufacturing quality.

In step 380, the health assessment file is compared to a previously written assessment file, for example, the currently written health assessment profile is compared to a previously written assessment profile, or with a plurality of previously written profiles. Compared to the assessment file.

Next, as described in step 390, power consumption and current are determined, and at step 395, future power consumption is predicted.

When the tool status monitoring module is triggered, it will automatically search for at least one data file, the file path to the file will be located and the related files will be parsed, and the obtained signal or data will be processed in series. , wherein the feature is drawn from a stable portion of the signal, and a stable portion is defined as the duration of time during which the cutting tool is actually joined to the workpiece. After the power data has passed an averaging procedure, the stable portion of the segment is identified using a device method. The time position of the stabilizing portion is used to isolate the equivalent section of the vibration data, and then the vibration and power signals from the stabilizing portion are calculated, and in general, the mean, standard deviation, minimum and maximum values are derived.

The selected plural feature is then imported into a health assessment technique that uses an Euclidean metric.

The health assessment results include a standard health assessment value that starts at a high rate, and when the cutting tool is used continuously, the decline performance almost always reduces the health value. Finally, when the health rating reaches a value that is only below the preset value, for example 0.5, the tool needs to be replaced. When the tool has to be replaced, the health assessment value will be simulated with the cutting test performed under similar equipment status and parameters.

When the tool is judged to be dull, the service box or cloud server will notify the appropriate person to replace it with a sharp tool.

The manufacturing efficiency optimization platform, tool condition monitoring and prediction method of the present invention provides instant monitoring of tool status and allows manufacturers to easily understand their tool status, while predictive modules allow manufacturers to use power consumption trends to improve scheduling. Avoid power limitations.

Referring to the above description, FIG. 4A is a schematic diagram of the sensor signal, and FIG. 4B is a schematic diagram of the controller signal. In Figure 4A, the vibration data is displayed above and the power data is displayed below.

Please also refer to Figure 5A, which is a graph of the average post-power value, with the stable portion of signal 15 being the highest or lowest mean plateau portion. The time position of the stable portion is used to isolate the equivalent section of the vibration data. Please refer to Figure 5B, which is a graph for selecting the tool state monitoring feature to calculate the complex characteristics of the vibration signal and the power signal of the stability portion.

Please refer to Figure 6, which is a graph of the results of the health assessment and the average power consumption per lane. The upper is the health assessment and the lower is the power consumption. As shown, the power consumed when the tool is worn out increases, the health rating decreases and the increase in power consumption represents tool wear. When the health rating drops to a preset point, the tool has been replaced.

The manufacturing efficiency optimization platform and the tool state monitoring and forecasting method of the present invention further comprise generating analysis and management tools, and the application developer uses the application server to create and develop analysis and management tools used on the platform, and the analysis and management The tools are stored in the application server during development or development. When the tool is completed, it will be exposed on the application server and notify the proxy server that the analysis and management tools are ready for sale. The analysis and management tools are stored in the application server during the development and disclosure period. When the notification proxy server has disclosed the analysis and management tools, the analysis and management tools are downloaded from the application server to the proxy server, and the cloud server is downloaded. Will be notified of new analysis and management tools that will update the version.

The proxy server provides the analysis and management tool to the cloud server. In an embodiment of the present invention, the analysis and management tool is automatically downloaded to the cloud server; in another embodiment of the present invention, the analysis and management The tool will be downloaded according to the requirements of the cloud server.

The service box is coupled to at least one mechanical sensor to receive appropriate sensor data, including power consumption, temperature, viscosity, noise level, vibration, material quantity or volume, product count, etc., the service box will feel The test data is instantly transmitted to the cloud server, and the transmitted data is received by the cloud server.

The cloud server uses the analysis and management tools on the sensor data. For example, when the sensor data includes the current temperature of the machine, the analysis and management tool tracks the temperature and produces a temperature record or history if the temperature is too high. Or too low will generate an alarm, as well as other useful analysis. The cloud server provides the results of the analysis and management tools on the sensor data to the client device. In one embodiment of the invention, the results are automatically transmitted to the client device, while another implementation of the present invention In the example, these results are displayed on one of the client devices' service dashboards.

The service dashboard on the client device provides analysis results accessed by the user and data provided by the cloud server. The service dashboard includes displays of available tools, reports, charts, charts, maps, history, logs, schedules, quantities, Inventory, document, order, or projection.

The service dashboard displays the icon of the available tool and the icon accessed by the user device of the client device. Clicking on one of the icons will bring up the visualization of the selection icon. For example, if the user selects a production quality icon on the service dashboard It will display a graph of current production and past production history, so that users can see quantitative information instantly and easily, and better than reading printed reports.

In an embodiment, the service dashboard is individually configured for different users, and only the appropriate tools and materials are displayed for each user. For example, the quality assurance personnel do not look at the financial, subscription, or delivery information. Using the platform of the present invention can avoid information overload and confusion. In one embodiment, the service dashboard is configured to instantly display the appropriate data on the client device, for example, an operator at the production site will see an immediate graph of mechanical performance from their client device, not Confused by unwanted information.

Referring to FIG. 7, the present invention provides a configuration of a plurality of cloud servers and platform services, which is quite flexible for the client. In FIG. 7, the plurality of cloud servers are connected to the proxy server 120, and the cloud server A 140A and The service box A 130A is connected, the cloud server B 140B is connected to the service box B 130B, and the cloud servers 140A and 140B are connected to the same proxy server 120.

The Cloud Server A 140A is configured as a private cloud server, including private data for client-only access. The cloud server A 140A is connected to the proxy server to download analysis and management tools, all data such as sensors. Data, product information, analysis data, and management data will remain in the cloud server A 140A and will not be disclosed. Private cloud servers, such as the Cloud Server A 140A, provide high-level security for sensitive manufacturing data to the client.

The cloud server B 140B is configured as a semi-public cloud server, some of the data or all the data can be accessed by the proxy server 120, and the proxy server 120 provides the cloud data service and the analysis and management tool management service to the cloud server. B 140B. For example, the proxy server 120 periodically updates the analysis and management tools, provides access to new tools, performs analysis on the product data, and maintains the cloud server B 140B. Semi-public cloud servers, such as the Cloud Server B 140B, are economically viable for smaller companies or clients without the need for a technical support team.

In an embodiment of the present invention, the analysis and management tool is a paid use, and the client can select the required analysis and management tools and pay for the use, instead of buying the tool, to prevent the client from buying the unnecessary tools. The cost of erecting the platform of the present invention is further reduced.

In one embodiment of the invention, the analysis and management tools are purchased separately, and the price is determined based on the complexity of the tool.

In an embodiment of the present invention, the analysis and management tool is used for renting, and when the client finishes using or no longer needs the tool, it can be returned. For example, if the tool is an inventory efficiency tool, it is analyzed annually. Once, the client only needs to rent one year or short-term loan, and return the tool after use.

In an embodiment of the present invention, the service box is leased to the client, which provides flexibility for increasing or decreasing the number of service boxes when the service box is added to or removed from the production device; and as such, the present invention The cost of the platform can be easily controlled by the client, and the initial cost is lower than the cost of purchasing the service box at the beginning.

Please refer to Figure 8, which is a schematic diagram of a plurality of service boxes connected to the same cloud server. The service box A 130A is connected to the machine A 300A and receives the sensor data of the sensor A, the sensor B and the sensor C thereon, and the service box A 130A transmits the received sensor data to the Cloud server 140. The service box D 130D is connected to the machine D 300D and receives the sensor data of the sensor D and the sensor E thereon, and the service box D 130D transmits the received sensor data to the cloud server 140.

The cloud server 140 is connected to a plurality of client devices (client device F 150F and client device G 150G), and the data may include sensor data, analysis data, management data, and machine data of the machine A 300A and the machine D 300D, and the client The end device F 150F and the client device G 150G have the right to access data simultaneously or separately on the cloud server 140.

The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Therefore, any changes or modifications of the features and spirits of the present invention should be included in the scope of the present invention.

100‧‧‧Manufacture of optimized platforms
110‧‧‧Application Server
120‧‧‧Proxy 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
160‧‧‧Service Dashboard
300A‧‧‧ Machine A
300D‧‧‧ Machine D

Figure 1 is a block diagram of the manufacturing efficiency optimization platform and tool condition monitoring of the present invention. 2 is a flow chart of an embodiment of a manufacturing efficiency optimization platform and a tool state monitoring method according to the present invention. 3 is a flow chart of an embodiment of a manufacturing efficiency optimization platform, a tool state monitoring, and a power consumption prediction method according to the present invention. Figure 4A is a schematic diagram of the sensor signal. Figure 4B is a schematic diagram of the controller signal. Figure 5A is a graph of the average post power value. Figure 5B is a graph of the selection of tool condition monitoring features. Figure 6 is a graph of the results of the health assessment and the average power consumption per channel. FIG. 7 is a flow chart of an embodiment of a plurality of cloud servers in the optimization platform and method of the present invention. Figure 8 is a flow chart of an embodiment of a plurality of service boxes in an optimized platform and method of the present invention.

Claims (20)

A method of manufacturing an efficiency optimization platform with tool condition monitoring, comprising the steps of: obtaining data from a plurality of sensors on a machine device using a service box; transmitting the acquired data to a cloud server; The tool on the device uses the acquired data to contact a workpiece and retrieves the data; analyzes the captured data; and performs a health assessment based on the result of analyzing the captured data; if the health assessment represents the tool must Replace, replace the tool; and if the health assessment indicates that the tool can continue to be used, continue to use the tool for production. The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 1, wherein the obtained data includes power consumption data and vibration data. The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 2, wherein the obtained data further comprises computer numerical control (CNC) data and data acquisition (DAQ) data. . The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 1, further comprising: determining a health assessment value of the tool from the health assessment; and when the health assessment value reaches a preset value, Determine if the tool needs to be replaced. The method of manufacturing an efficiency optimization platform having tool condition monitoring according to claim 1, further comprising: comparing the health assessment with a prior health assessment; determining power consumption; and predicting future power consumption. A method of manufacturing an efficiency optimization platform having tool condition monitoring as described in claim 1, further comprising notifying all personnel when the tool must be replaced. The method of manufacturing an efficiency optimization platform having tool condition monitoring as described in claim 1 further includes notifying all personnel when the tool is about to be replaced. A method of manufacturing an efficiency optimization platform with tool condition monitoring, comprising the steps of: obtaining a plurality of sensor data and control data from a production machine using a service box; using the service box to use the sensor data and the Controlling data transmission to a cloud server; filtering the sensor data and the control data; performing an averaging process on the filtered data; selecting a segment from the result of the average program; A plurality of features are captured in the segment; a health assessment is performed; and a health assessment value of a tool state on the production machine is determined. The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 8, wherein the sensor data and the control data comprise power consumption data and vibration data. The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 9, wherein the sensor data and the control data further comprise computer numerical control (CNC) data and data acquisition (data) Acquisition, DAQ) information. The method of manufacturing an efficiency optimization platform for tool condition monitoring according to claim 8, further comprising determining whether the tool needs to be replaced when the health assessment value reaches a predetermined value. The method of manufacturing an efficiency optimization platform having tool condition monitoring according to claim 8, further comprising: comparing the health assessment with a prior health assessment; determining power consumption; and predicting future power consumption. The method of manufacturing an efficiency optimization platform having tool condition monitoring according to claim 8, further comprising: comparing the health assessment with a plurality of prior health assessments; determining power consumption; and predicting future power consumption. The method of manufacturing an efficiency optimization platform having tool condition monitoring as described in claim 8, further comprising replacing the tool when the health assessment value reaches a predetermined value. A method of manufacturing an efficiency optimization platform having tool condition monitoring as described in claim 14, further comprising notifying all personnel when the tool must be replaced. The method of manufacturing an efficiency optimization platform having tool condition monitoring as described in claim 14, further comprising replacing the tool when the health assessment value reaches the preset value. A method of manufacturing an efficiency optimization platform with tool condition monitoring, comprising the steps of: obtaining vibration data, power consumption data, and control data from a plurality of sensors of a production machine using a service box; The vibration data, the power consumption data and the control data are transmitted to a cloud server; the vibration data, the power consumption data and the control data are filtered; an averaging process is performed on the filtered data; Selecting a segment from the result, the segment including when a tool on the production machine is associated with a workpiece; extracting a plurality of features from the segment; performing a health assessment; determining a state of a tool on the production machine a health assessment value; and when the health assessment value reaches a predetermined value, the tool is replaced. The method of manufacturing an efficiency optimization platform having tool condition monitoring according to claim 17, further comprising: comparing the health assessment with a prior health assessment; determining power consumption; and predicting future power consumption. The method for manufacturing an efficiency optimization platform with tool state monitoring according to claim 17, further comprising acquiring and transmitting computer numerical control (CNC) data and data acquisition (DAQ) using the service box. )data. A method of manufacturing an efficiency optimization platform having tool condition monitoring as claimed in claim 17, wherein the vibration data represents an increase in vibration, the power consumption data representing an increase in power consumption, the health assessment value being reduced and representing that the tool has been worn. .