TWM651765U - Intelligent management integration system for machine tools - Google Patents

Abstract

An intelligent management integration system for machine tools, including a controller, a human-machine interface and an analysis module. The human-machine interface displays a workpiece model of an object to be processed and processing parameters corresponding to the features/areas of the workpiece model. The controller is based on Complete the set processing parameters on the human-machine interface, control the movement of the machine tool, and the analysis module collects, filters, aggregates and analyzes the processing process information of the machine tool during operation to generate monitoring information and production capacity information related to the processing process information. In this way, it is possible to achieve intuitive and graphical control of the processing process, reuse of processing experience (due to continuous recording of historical parameters, it is more efficient than the technical inheritance of the traditional master-apprentice system), optimized machine operation plans and quick troubleshooting of abnormalities ( Component/machine life management, maintenance and replacement warning) functions.

Description

Intelligent management integration system for machine tools

This creation involves the technology of machine tool machining behavior analysis and job scheduling management, especially an "intelligent management integration system for machine tools" that can provide intuitive and graphical control of the machining process, and can also analyze historical machining and maintenance experience.

With the development of the smart machinery industry, how to improve the readability of real-time monitoring and feedback by improving the human-machine interface for existing processing operations, thereby improving user experience and processing quality, and how to be oriented from production and manufacturing services , realizing real-time monitoring and troubleshooting anomalies, and assisting decision-makers in predicting the cost, production capacity and delivery date of processing operations, are indeed topics that relevant technical personnel need to think about.

In an embodiment of the invention, the invention proposes an intelligent management integration system for machine tools for managing at least one machine tool. The system includes: a controller coupled to the machine tool; a person coupled to the controller The human-machine interface provides a parameter setting interface, which displays a workpiece geometric model of an object to be processed and multiple processing parameters corresponding to multiple features or areas of the workpiece geometric model. The controller executes a processing formula according to it and After completing the set processing parameters on the human-machine interface, the machine tool can be controlled to process the object to be processed; an analysis module connected to the controller information can continuously receive and analyze a data generated by the controller when the current machine tool is performing processing operations. Processing process information is used to generate a monitoring information and a production capacity information related to the processing process information, whereby the processing parameters of the machine tool can be intuitively and graphically controlled, and the processing efficiency can be reproduced (the historical processing parameters can be continuously recorded due to the Integrated into actual processing time history), optimization of machine operation plans (with the continuous collection and analysis of data, the intelligent analysis of the analysis module is expected to become more and more accurate) and rapid elimination of abnormalities (component life management, component It is recommended to replace the warning).

In an embodiment of the present invention, the above-mentioned intelligent management integration system further includes a terminal, which includes a processor, a communication module and the analysis module. The communication module can use a first communication protocol (such as Modbus, RS485, Wi-Fi) establishes a wired or wireless communication connection with the controller, and the analysis module built in the terminal can generate monitoring information and production capacity information based on a machine learning algorithm.

In one embodiment, the above-mentioned intelligent management integration system further includes a cloud server, which can use a second communication protocol to establish a communication connection with the terminal to collect and store processing process information, monitoring information, and production capacity information from the terminal. One or a combination thereof.

In one embodiment, the cloud server of the above-mentioned intelligent management integration system can use the second communication protocol to establish a communication connection with another terminal to collect and store another processing process received by the other terminal from another controller. information, another monitoring information and another production capacity information, or a combination thereof.

In an embodiment of this invention, the cloud server of the above-mentioned intelligent management integration system can analyze multiple processing process information collected by it to optimize the machine learning algorithm and deploy it to the terminal through the second communication protocol to enhance the analysis module Accuracy in generating monitoring information, capacity information, or a combination thereof.

In an embodiment of the present invention, the above-mentioned intelligent management integration system further includes a machine control box equipped with a human-machine interface. The human-machine interface can provide a processing status interface to view monitoring information and production capacity information related to the current machine tool. Or a combination thereof, and the machine control box is equipped with an alarm unit to output an abnormality elimination prompt based on a warning instruction generated by the controller when an abnormal feature appears in the monitoring information.

Based on the above-mentioned intelligent management integration system of machine tools, it can be seen that this creation, through the optimized human-machine interface, can not only achieve the beneficial effects of intuitive and graphical control of the machining process, but also provide monitoring information and production capacity information during the machining process. By waiting for real-time feedback, coupled with network-connected terminals and big data applications, traditional machine tools can be used as smart network-connected machine tools.

This creation also proposes an intelligent management and integration method for machine tools, computer-readable recording media and computer program products.

The following is a detailed description of the preferred embodiments of the present invention based on multiple drawings. Those skilled in the art should be able to clearly understand the purpose of the present invention after reading.

Please refer to the block diagram of FIG. 1. An intelligent management integration system 10 for a machine tool is used to manage at least one machine tool M. The system 10 may include: a machine tool M, a controller P, a human-machine interface I, and an analysis module. Set A and a storage module S, the controller P can be coupled to the machine tool M, and if there are multiple machine tools M, there are corresponding multiple controllers P that are respectively connected to each machine tool M for communication, and the human-machine interface I is coupled to the controller P, and the analysis module A and the storage module S are information-connected to the controller P respectively.

Please refer to Figures 1 and 2. The human-machine interface I provides a parameter setting interface I_S that can be displayed on a display module D. The parameter setting interface I_S can display a workpiece geometric model O corresponding to an object to be processed and the corresponding workpiece geometric model. With multiple processing parameters M_P of multiple characteristics of O, the controller P can control the electronic components of the machine tool M to operate according to a processing formula executed by it and after completing the set processing parameters M_P on the human-machine interface I. The object to be processed is processed. The processing process information W_R generated by the controller P when the machine tool M is currently operating is continuously filtered and compared by the analysis module A to generate a monitoring information W_M associated with the processing process information W_R. One production capacity information W_P. Among them, one or a combination of the processing process information W_R, the monitoring information W_M and the production capacity information W_P can be presented on a processing status interface I_V provided by the human-machine interface I through the display module D.

In a feasible embodiment, the processing process information W_R continuously generated by the analysis module A is continuously input into a training data set of historical processing process information to improve the accuracy of the analysis module A in calculating the monitoring information W_M and the production capacity information W_P. .

As shown in Figure 3 and Figure 4, the processing history information W_R that can be displayed on the display module D can be the processing mode, feed amount, safety point, X/Y axis starting point, Z axis end point, total grinding amount, and total rough grinding One or a combination of quantity, total amount of fine grinding, number of dry grinding, operating time, processing time, Y/Z axis load, spindle speed, dressing timing, feed amount, reversing position, station changing time, and processing time history .

Following the above, in a feasible embodiment, the analysis module A can be based on a machine learning algorithm (such as decision tree, cluster analysis, reinforcement learning, Bayesian network and other decision-making algorithms, or a deep learning algorithm, which is not based on This is limited to this), based on a historical machining process information of machine tools M of various specifications and same/similar years stored in the storage module S, or a combination of the historical machining process information and monitoring information W_M (which can be packaged It is a pre-trained historical processing experience model) and performs intelligent analysis to calculate monitoring information W_M and production capacity information W_P.

As shown in Figure 5, Figure 6 and Figure 7, the monitoring information W_M that can be displayed on the display module D is machine status, predicted processing time, actual processing time, spindle operating time, spindle speed monitoring, spindle load monitoring, servo Load monitoring, hydraulic operating time, oil usage time, current total working time, predicted cycle time, residual processing time, current processing parameters, component/machine life, machine I/O status, daily operation rate, power consumption One or a combination of statistics (kWh), phase voltage, average phase voltage, line voltage, average line voltage.

As shown in Figure 8, the production capacity information W_P that can be displayed on the display module D can be the estimated total production, total utilization rate, actual total production, actual operating hours, actual idle hours, actual failure hours, and machine schedule One or a combination of plans.

Among them, as an example, the above-mentioned monitoring information W_M related to the life of components/machine can be, for example, proximity switches, oil pressure start/stop, lock buttons, pressure switches, speed switches, indicator lights/warning lights, buzzers, relays and other components (Users can adjust and define the type and quantity of components according to the specifications of the machine) or the number of uses of input signals (such as hydraulic/spindle/electric clamp start signals, inverter fault signals, inching signals, forward and backward continuous signals, etc.) Count, signal occurrence/generation count or remaining life count.

As an example, the machine tool M can be, for example, a multi-axis machine tool, a lathe, a milling machine, a welding machine, a robot arm module, etc., but is not limited thereto.

Among them, as an example, the controller P can be provided on the machine tool M, and the controller P can be, for example, a PLC controller (Programmable Logic Controller; PLC) or a CNC controller (Computer Numerical Control; CNC). is limited.

As an example, the sensor shown in FIG. 1 can be disposed on the main shaft or key components of the machine tool M, or outside the machine tool M, or in the environment of the machine tool M. The sensor can also be A proximity sensor, a photoelectric sensor, a laser displacement meter, an area sensor, a pressure sensor, a visual sensor, a gas flow meter, a temperature sensor, or one of them Combinations are not limited to this.

Among them, as an example, the above historical processing history information can be a historical processing time history, which in addition to the data type of the processing history information W_R, can also include historical customers, historical product names, order numbers, work order numbers, and batches. One or a combination of number, change number, historical material number, but not limited to this, and the historical processing process information can also be presented in the processing status interface I_V of the human-machine interface I.

As an example, the human-machine interface I may be an industrial-grade human-machine interface, and its full name may be, for example, Human/Man Machine interface (HMI/MMI) or Operator Interface (OI), but is not limited to this.

Please refer to Figure 9, which is a method flow chart of the STEP method for intelligent management integration of machine tools, and please refer to Figure 1 together, including the following steps.

In step S1 (providing a graphical and intuitive parameter interface on the human-machine interface), the human-machine interface I coupled to a controller P provides a parameter setting interface I_S to display a workpiece geometric model of an object to be processed. O and multiple processing parameters M_P corresponding to multiple features or areas of the workpiece geometric model O.

In step S2 (collecting, filtering and integrating data generated during the processing process), the controller P coupled to a machine tool M completes the processing parameters M_P set on the human-machine interface I according to a processing formula executed by it and the human-machine interface I. The machine tool M is controlled to process the object to be processed, and an analysis module A connected to the controller P continuously receives and analyzes a processing process information W_R generated by the controller P when the machine tool M is operating.

In step S3 (continuous calculation of monitoring information and production capacity information related to the machine status and processing process), the analysis module A continuously updates a monitoring information W_M and a production capacity information W_P at different time points associated with the processing process information W_R, and Optionally stored in storage module S.

Please refer to Figure 10. In one embodiment, the intelligent management integration system 10 of the machine tool may further include a machine control box B configured with a human-machine interface I. In another feasible embodiment, there are multiple machine tools at the factory. M and a plurality of machine control boxes B respectively coupled to the controller P and the human-machine interface I. When each machine tool M performs processing operations according to the processing method, the user can take care of multiple machine control boxes B to At the end of a processing cycle, let the user set the processing parameters M_P in the corresponding machine control box B (or directly import the historical processing parameters of the processing time history) and place another to-be-processed required for the next processing cycle. Object, execute the processing procedure again, and continue automatic processing. In addition, FIG. 9 also reveals that the human-machine interface I of this embodiment is not limited to the area where the display module D is located.

Please continue to refer to Figure 10. In a possible embodiment, the above-mentioned machine control box B can be further configured with an alarm unit (not shown in the figure), which can detect when an abnormal feature occurs in the monitoring information W_M (such as oil pressure overload). ), and outputs an abnormality elimination prompt based on a warning instruction generated by the controller P. The abnormality elimination prompt can be, for example, the machine I/O status (or the I/O status point of the PLC) shown in Figure 11 bit table, and the machine I/O status can be part of the monitoring information W_M (as mentioned above), which can help factory-side users to instantly find the component causing the abnormality on site. In addition, the warning unit can also output an early warning prompt according to a warning instruction generated by the controller P when the monitoring information W_M has an early warning feature. The early warning prompt can be, for example, a component life warning of the machine tool M, One or a combination of machine life warning, bottleneck equipment warning, machine maintenance timing and parts maintenance timing, which can help factory managers diagnose and arrange machine maintenance schedules and parts replacement schedules in advance , reduce the loss of maintenance and production costs caused by unexpected failures and shutdowns, reduce the frequency of unexpected shutdowns and maintenance, and at the same time help improve and stabilize the efficiency of production line scheduling.

Please refer to Figure 12. In another embodiment of the invention, the intelligent management integration system 10 of the machine tool may further include a terminal SB and a cloud server CS. The terminal SB includes a processor and a communication module N and analysis module A. The terminal SB (Smart Box) in this embodiment can be used as an edge node (Edge Node) on the factory side to collect, filter, and analyze the processing process information W_R nearby to reduce the cost based on edge computing. The waiting time for data to travel from the communication module N of the edge node to and from the cloud server CS can also be reduced while reducing network bandwidth costs.

Following the above, the communication module N in this embodiment can use a first communication protocol P1 to establish a communication connection with the controller P, and the cloud server CS in this embodiment can use a second communication protocol P2 (different from the first communication protocol The industrial area network used by P1) establishes a communication connection with the communication module N of the terminal SB to collect and store the processing process information W_R and monitoring information W_M from the controller P and associated with the machine tool M from the SB terminal. and production capacity information W_P, or a combination thereof.

Following the above, in a possible embodiment, the cloud server CS can use the second communication protocol P2 to establish a communication connection with another terminal (not shown in the figure) to collect and store data from another terminal from another controller. (Not shown in the figure) One or a combination of another processing process information, another monitoring information, another production capacity information received.

Following the above, in a feasible embodiment, the cloud server CS can analyze the multiple processing process information W_R it collects (in addition to the controller P of this creation, it can also come from external data sources) to optimize the machine learning algorithm, And through the second communication protocol P2, the optimized machine learning algorithm is deployed to the terminal SB regularly or irregularly to improve the accuracy of the monitoring information W_M and production capacity information W_P generated by the analysis module A of the terminal SB.

In a feasible embodiment, after step S3 of the above-mentioned intelligent management integration method STEP of the machine tool is completed, the following steps can be continued, and please refer to Figure 1 and Figure 12 together: the terminal SB uses the first communication protocol P1 and controls The controller P establishes a communication connection, so that the analysis module A configured in the terminal SB obtains the processing process information W_R generated by the controller P when the machine tool M operates; and the cloud server CS uses the second communication protocol P2 to establish a communication connection with the terminal SB Communication connection to collect and store one or a combination of the processing process information W_R, monitoring information W_M, and production capacity information W_P from the controller P and associated with the machine tool M from the terminal SB, and deploy an optimized machine learning algorithm to the terminal SB, thereby improving the accuracy of the monitoring information W_M and production capacity information W_P generated by the analysis module A.

In a possible embodiment, the terminal SB can use a first communication protocol P1 to establish communication connections with other terminals (not shown in the figure), so that the terminal SB can receive data obtained by other terminals SB from other controllers P. The processing history information W_R is combined with the monitoring information W_M and production capacity information W_P calculated by other terminals SB (other analysis modules A).

In a possible embodiment, the analysis module A can also respond to a reverse query request from an external device (not shown in the figure; such as a smartphone, tablet, personal computer, notebook computer) to retrieve and retrieve Remotely monitor the processing process information W_R, monitoring information W_M and production capacity information W_P of the machine tool M in the operating state.

In a feasible embodiment, the terminal SB can be connected through the communication module N to management systems used at the factory or enterprise such as ERP (Enterprise resource planning), SAP, MES (Manufacturing Execution System), WMS (Warehouse Management System), etc. system.

In addition, regarding the processing process information W_R, monitoring information W_M and production capacity information W_P mentioned in the intelligent management integration method STEP of machine tools, the functions and aspects of these technical features have been explained above and will not be repeated here.

Please refer to Figure 1. In one embodiment, the present invention further provides a non-transitory computer-readable recording medium associated with at least one instruction to define the aforementioned intelligent management integration method STEP of the machine tool. The relevant descriptions of each step have been This is described in detail above and will not be repeated here.

Please refer to Figure 1. In one embodiment, the present invention further provides a computer-readable recording medium associated with at least one instruction to define the aforementioned intelligent management integration method STEP for machine tools. Relevant descriptions of each step have been detailed in above, no further details will be given here.

Please refer to Figure 1. In one embodiment, the present invention further provides a computer program product. When the computer system loads multiple instructions of the computer program product, it can at least complete the intelligent management integration method STEP of the machine tool as mentioned above. , the relevant instructions for each step have been detailed above and will not be repeated here.

Among them, as an example, the cloud server CS of this creation can be one or more independent server computers that provide connection services, or a server running in the form of a virtual machine (Virtual Machine), or a virtual dedicated host (Virtual Machine). A server running in the form of Private Server), or a public cloud, or a private cloud, etc., but is not limited to this.

Among them, as an example, the storage module S of this invention can be eMMC (embedded MultiMedia Card) flash memory, UFS (Universal Flash StoR_Age) flash memory, NVMe (NVM Express) flash memory, solid state drive (solid -state drive/disk), LPDDR (Low Power R_Andom Access Memory) memory, dynamic random access memory (DR_AM) or static random access memory (SR_AM), if used as a non-transient (non-tR_Ansitory) computer If the medium is readable, the storage module S can further store at least one instruction associated with the intelligent management integration method STEP of the aforementioned machine tool, and the at least one instruction can be accessed and executed by the processor.

Among them, as an example, the first communication protocol P1 of this creation can be EIA-485/RS485, CAN/CAN Bus (Controller Area Network) communication protocol; WIA-PA, HaLow Wi-Fi (IEEE 802.11) based on Wi-Fi architecture ah), WiGig (IEEE 802.11ad) wireless communication protocols; wireless communication protocols based on IEEE 802.15.4 standards, such as 6LoWPAN, WirelessHART, and ZigBee; low-power Bluetooth (BLE) wireless communication protocols; LoRA (Long Range Wide Area Network Regulation) Variable) communication protocol; LoRa, NB-IoT, 6TiSCH communication protocol based on Sub-GHz solutions, one of the above-listed wireless communication protocols or any combination thereof, but not limited to this.

In summary, after this creation is implemented, through the introduction of optimized human-machine interface and analysis modules (which can also be extended to big data analysis in cloud applications), at least intuitive and graphical control of processing parameters, Reproduction of processing efficiency (because historical processing parameters are continuously recorded and can be integrated into a processing time history), optimization of machine operation plans (with the continuous collection and analysis of data, the intelligent analysis of the analysis module can become more and more accurate ) and rapid abnormality elimination (component life management, component replacement recommendation warning).

Although the present invention has been disclosed as above in the form of embodiments, it is not intended to limit the invention. Anyone with ordinary knowledge in the technical field may make some modifications and modifications without departing from the spirit and scope of the invention. Therefore, The scope of protection of this creation shall be determined by the attached patent application scope.

To sum up, this creation has the patent requirements of "industrial applicability", "novelty" and "progressivity"; the applicant has filed a new patent application with the Jun Bureau in accordance with the provisions of the Patent Law.

10:Intelligent management integration system for machine tools M: Machine tool P:Controller I: Human-computer interface I_S: Parameter setting interface I_V: Processing status interface D:Display module M_P: Processing parameters O: Workpiece geometric model A:Analysis module W_R: Processing process information W_M: Monitoring information W_P:Production capacity information B:Machine control box SB: terminal N: Communication module CS: cloud server P1: First communication protocol P2: Second communication protocol S:storage module STEP: Intelligent management integration method for machine tools S1: Provide a graphical and intuitive parameter interface on the human-machine interface S2: Collect, filter and aggregate data generated during processing S3: Continuously calculate monitoring information and production capacity information about machine status and processing process

Figure 1 is a block diagram of an embodiment of the present invention.

Figure 2 is a schematic diagram of a human-machine interface according to an embodiment of the present invention.

Figure 3 is a schematic diagram (1) of a situation of processing process information according to an embodiment of this invention.

Figure 4 is a schematic diagram (2) of the processing process information of one embodiment of this invention.

Figure 5 is a schematic diagram (1) of a situation of monitoring information according to an embodiment of this invention.

Figure 6 is a schematic diagram (2) of the monitoring information situation according to an embodiment of this invention.

Figure 7 is a schematic diagram (3) of the monitoring information situation according to an embodiment of this invention.

Figure 8 is a schematic diagram of a scenario of production capacity information according to an embodiment of this invention.

Figure 9 is a method flow chart according to an embodiment of the present invention.

Figure 10 is a schematic diagram of a machine control box according to an embodiment of the present invention.

Figure 11 is a schematic diagram (4) of the monitoring information situation according to an embodiment of this invention.

Figure 12 is another embodiment of this invention.

10:Intelligent management integration system for machine tools

M: Machine tool

P:Controller

I: Human-computer interface

I_S: Parameter setting interface

I_V: Processing status interface

D:Display module

M_P: Processing parameters

O: Workpiece geometric model

A:Analysis module

S:storage module

Claims (8)

An intelligent management integration system for machine tools for managing a machine tool, including: a controller coupled to the machine tool; A human-machine interface is coupled to the controller. The human-machine interface provides a parameter setting interface that displays a workpiece geometric model of an object to be processed and multiple processing parameters corresponding to multiple features of the workpiece geometric model. The controller controls the machine tool to process the object to be processed according to a processing formula executed by it and the processing parameters set on the human-machine interface; and An analysis module is connected to the controller information. The analysis module receives a processing process information generated by the controller when the machine tool is operating, so as to generate a monitoring information and a production capacity information associated with the processing process information; wherein The human-machine interface provides a processing status interface to present the processing process information, the monitoring information, the production capacity information or a combination thereof. For example, the intelligent management integration system of claim 1 further includes a terminal, which includes a processor, a communication module and the analysis module. The communication module uses a first communication protocol to establish a connection with the controller. The communication connection enables the analysis module of the terminal to collect and store the processing process information generated by the controller. For example, in claim 2, the intelligent management integration system for machine tools is provided, wherein the analysis module of the terminal generates the monitoring information and the production capacity information based on a machine learning algorithm. For example, the intelligent management integration system of claim 3 further includes a cloud server that uses a second communication protocol to establish a communication connection with the communication module of the terminal to collect and store the processing from the terminal. Process information. Such as the intelligent management integration system of claim 4, wherein the cloud server uses the second communication protocol to establish a communication connection with another terminal to collect and store the information received by the other terminal from another controller. One or a combination of another processing process information, another monitoring information, another production capacity information. For example, the intelligent management integration system of machine tools in claim 5, wherein the cloud server analyzes a plurality of the processing process information collected by it to optimize the machine learning algorithm and deploy it to the terminal through the second communication protocol, Improve the accuracy of the analysis module in generating the monitoring information and the production capacity information. For example, the intelligent management integration system for machine tools of claim 1 further includes a machine control box equipped with the human-machine interface. For example, the intelligent management integrated system of claim 7 for machine tools, wherein the machine control box is equipped with a warning unit for outputting a warning command based on a warning command generated by the controller when an abnormal feature appears in the monitoring information. Exception troubleshooting tips.