System and method for performing virtual orchestration to manage and use processing equipment

Abstract

Systems (100) and methods for performing virtual orchestration to manage and use processing equipment (105) are disclosed herein. The method includes receiving, by a processing unit, a request from a user interface (165) for performing a virtual orchestration of a processing device (105). Further, upon receiving the request, at least one virtual copy of the spindle bearing assembly suitable for the machining apparatus (105) is selected from a plurality of virtual copies stored in the digital library within the virtual layer. Furthermore, a digital copy of at least the spindle bearing assembly of the machining apparatus (105) is adapted within the virtual layer by updating the selected virtual copy based on the input data. One or more workflow parameters associated with the spindle bearing assembly are calculated based on operation of the at least one simulation using the configured digital copy. Further, at least one action is performed based on the one or more workflow parameters to generate a result.

Description

[0001] This invention relates to processing equipment, and more particularly to systems and methods for performing virtual orchestration to manage and use processing equipment.

[0002] Traditionally, consumers purchase processing equipment from original equipment manufacturers (OEMs) by paying an upfront price. After purchase, consumers assume responsibility for the processing equipment throughout its entire lifecycle. Therefore, consumers also incur ongoing costs for repairs, maintenance, and spare parts throughout the equipment's lifespan. Alternatively, consumers may be permitted to obtain processing equipment from OEMs based on a free maintenance package, whereby consumers can pay usage fees associated with the processing equipment based on attributes such as performance, availability, and uptime. This mechanism provides consumers with the flexibility to pay based on the nature of the processing equipment's use.

[0003] Therefore, a mechanism is needed to predict such attributes associated with processing equipment to support charging consumers based on the use of processing equipment.

[0004] This document discloses a system and method for performing virtual orchestration to manage and use machining equipment. The method includes: receiving a request from a user interface by a processing unit for performing virtual orchestration of the machining equipment. The request includes input data indicating one or more requirements associated with performing virtual orchestration of the machining equipment.

[0005] The method further includes, upon receiving the request, selecting at least one virtual copy from a plurality of virtual copies stored in a digital library within the virtual layer. Each of the virtual copies corresponds to a static mathematical representation of a spindle bearing assembly suitable for a machining equipment. In one embodiment, selecting at least one virtual copy from the plurality of virtual copies includes: identifying metadata from input data indicating one or more requirements for performing virtual orchestration within the virtual layer. Furthermore, search logic is generated based on the identified metadata. Additionally, a search is performed in the digital library comprising the plurality of virtual copies based on the generated search logic to select the virtual copy. In one embodiment, the virtual copy includes an integrated dynamic thermal model comprising at least one one-dimensional dynamic model operatively coupled to a one-dimensional thermal model of the spindle bearing assembly. In another embodiment, the virtual copy also includes a motion simulation model operatively coupled to the integrated dynamic thermal model, wherein at least one motion simulation model is configured to dynamically calculate contact forces in the spindle bearing assembly.

[0006] The method also includes configuring a digital copy of at least one spindle bearing assembly suitable for the machining equipment within the virtual layer by updating the selected virtual copy based on input data. The digital copy is a dynamic mathematical representation of the spindle bearing assembly corresponding to the selected virtual copy in a digital library.

[0007] The method also includes calculating one or more workflow parameters associated with the spindle bearing assembly based on at least one run of a simulation using a configured digital copy. In one embodiment, calculating one or more workflow parameters includes running one or more simulation instances of the configured digital copy in a simulation environment to generate one or more simulation results indicative of the one or more workflow parameters.

[0008] The method further includes performing at least one action based on one or more workflow parameters to generate a result. In one embodiment, the result of performing at least one action is an optimal configuration of the spindle bearing assembly, wherein performing at least one action includes: determining whether one or more workflow parameters meet predetermined criteria. If at least one of the workflow parameters does not meet the predetermined criteria, one or more other virtual copies are selected from a digital library. Furthermore, the one or more workflow parameters are recalculated based on digital copies configured based on each of the one or more other virtual copies until the one or more workflow parameters meet the predetermined criteria. In one embodiment, if one or more workflow parameters meet the predetermined criteria, the configuration of the spindle bearing assembly corresponding to the selected virtual copy is identified.

[0009] In another embodiment, the result of performing at least one action is optimal operating conditions for the spindle bearing assembly, wherein the optimal operating conditions are associated with one of autonomous and manual operation of the machining equipment, and wherein performing at least one action includes: using an optimization algorithm to calculate the optimal operating conditions for the spindle bearing assembly based on one or more workflow parameters.

[0010] In yet another embodiment, the result of performing at least one action is the deviation between at least one of the workflow parameters and a corresponding measurement parameter, and wherein performing at least one action includes: calculating the deviation between at least one of the workflow parameters and a corresponding measurement parameter.

[0011] In another embodiment, the result of performing at least one action is control parameters for controlling the operation of the processing equipment, and wherein performing at least one action includes: calculating control parameters based on the calculated deviation if the deviation is greater than a predefined value. The calculated control parameters are adapted to reduce the deviation between at least one workflow parameter and a measurement parameter when applied to the processing equipment. In one embodiment, the deviation is associated with the production time of the processing equipment.

[0012] The method further includes: running a secure wallet adapted based on the result of performing at least one action. The secure wallet is a piece of code executable on the network for controlling transactions between consumer nodes and manufacturer nodes, where consumer nodes are nodes associated with consumers of the processing equipment, and manufacturer nodes are associated with manufacturers of the processing equipment. In one embodiment, running the result-based configured secure wallet further includes: identifying a template based on one or more requests received with the request. Furthermore, the secure wallet is configured using a template selected based on the result of performing at least one action for virtual orchestration within a virtual layer of the processing equipment.

[0013] In one embodiment, the method further includes generating a notification on the user interface indicating the result of performing at least one action.

[0014] This document also discloses a computer system arranged and configured to perform the steps of a computer implementation of the method according to any one of the foregoing method steps.

[0015] This document also discloses a computer-readable medium on which a program code segment of a computer program is stored, the program code segment being loadable into and / or executable by a processing unit, the processing unit performing the methods described above when the program code segment is executed.

[0016] The advantages of this invention, implemented through a computer program product and / or a non-transient computer-readable storage medium, are that by installing the computer program, the computer system can be easily adopted to operate as proposed in this invention.

[0017] The computer program product may be, for example, a computer program or include another element besides a computer program. The other element may be hardware, such as a memory device on which the computer program is stored, a hardware key for using the computer program, etc., and / or may be software, such as documentation or a software key for using the computer program.

[0018] The above-described properties, features, and advantages of the invention, as well as the ways in which they are realized, will become more apparent and understandable from the following description of embodiments of the invention in conjunction with the accompanying drawings. The described embodiments are intended to illustrate, not limit, the invention.

[0019] The invention will be further described below with reference to the embodiments illustrated in the accompanying drawings, in which: Figure 1A The figure illustrates a block diagram of a system for performing virtual orchestration to manage and use at least one processing device according to an embodiment of the present invention; Figure 1BThe diagram illustrates a block diagram of an apparatus for performing virtual orchestration to manage and use at least one processing device according to an embodiment of the present invention; Figure 2 The illustration shows a spindle bearing assembly associated with a machining apparatus according to an embodiment of the present invention; Figure 3A -B illustrates a block diagram of a digital copy of a spindle bearing assembly associated with a machining apparatus according to an embodiment of the present invention; Figure 4 A flowchart illustrating an exemplary method for performing virtual orchestration to manage and use processing equipment according to an embodiment of the present invention is shown; Figure 5 The illustration depicts an exemplary method for calculating and maintaining optimal operating conditions for a spindle bearing assembly according to an embodiment of the present invention; and Figure 6 A flowchart illustrating an exemplary method for predicting production time associated with processing equipment according to an embodiment of the present invention is shown.

[0020] Embodiments for carrying out the invention are described in detail below. Various embodiments are described with reference to the accompanying drawings, wherein similar reference numerals are used throughout to refer to similar elements. In the following description, numerous specific details are set forth for purposes of explanation, to provide a thorough understanding of one or more embodiments. It will be apparent that such embodiments can be practiced without these specific details.

[0021] Figure 1A The illustration shows a block diagram of a system 100 according to an embodiment of the present invention, the system 100 for performing virtual orchestration to manage and use at least one processing device 105. More specifically, the system 100 manages the utilization of the processing device 105. The system 100 includes a device 110 communicatively coupled to a controller 115 associated with the processing device 105 via a network 120. In this embodiment, the device 110 is an edge computing device. Those skilled in the art will understand that the device 110 can be communicatively coupled to multiple controllers in a similar manner. It will be understood that each of the multiple controllers can be associated with one or more processing devices. Those skilled in the art will understand that the device 110 can combine the functionality of an edge computing device and the controller 115. In an alternative embodiment, the device 110 is a cloud platform. For example, the cloud platform can be communicatively coupled to the controller 115 via an edge computing device.

[0022] The controller 115 enables the operator of the machining equipment 105 to define operating conditions for performing machining operations. As those skilled in the art will understand, operating conditions can be defined by the operator before starting a machining operation or during operation of the machining equipment 105. For example, operating conditions correspond to the type of machining operation, cutting tool type, tool settings, automatic tool changer settings, feed rate, cutting speed, NC code, and material test data associated with the workpiece mounted on the machining equipment 105.

[0023] The controller 115 may also be communicatively coupled to one or more sensing units 125 associated with the machining equipment 105. The one or more sensing units 125 include at least one sensor, such as an accelerometer, rotary encoder, force gauge, current transformer, thermistor, etc., configured to measure operating parameters associated with the machining equipment 105. The accelerometer is configured to measure vibrations at one or more locations on the machining equipment 105. In this embodiment, the accelerometer is mounted on the structure of the machining equipment 105. For example, the accelerometer may be attached to the bed or column of the machining equipment 105. The current transformer is configured to measure the current associated with a servo mechanism controlling the spindle movement of the machining equipment 105. The thermistor is configured to measure the temperature at one or more locations on the machining equipment 105. The outputs from each of these sensors will be collectively referred to as sensor data.

[0024] The controller 115 includes a transceiver 130, one or more processors 135, and a memory 140. The transceiver 130 is configured to connect the controller 115 to a network interface 145 associated with network 120. The controller 115 transmits real-time operational data to the device 110 via the network interface 145. The real-time operational data includes operating conditions set on the controller 115 and sensor data received from one or more sensing units 125.

[0025] Device 110 may be a (personal) computer, workstation, virtual machine running on host hardware, microcontroller, or integrated circuit. Alternatively, device 110 may be a real or virtual computer group (the technical term for a real computer group is "cluster," and the technical term for a virtual computer group is "cloud").

[0026] Device 110 includes a communication unit 150, one or more processing units 155, a display 160, a user interface 165, and a memory unit 170 that are communicatively coupled to each other, such as Figure 1BAs shown in the diagram. In one embodiment, communication unit 150 includes a transmitter (not shown), a receiver (not shown), and a gigabit Ethernet port (not shown). Memory unit 170 may include two gigabytes of random access memory (RAM) stacked in a Package on Package (PoP) configuration, as well as flash memory. One or more processing units 155 are configured to execute computer program instructions defined in the module. Furthermore, one or more processing units 155 are also configured to simultaneously execute instructions in memory unit 170. Display 160 includes a High-Definition Multimedia Interface (HDMI) display and a cooling fan (not shown). Additionally, an operator can access device 110 via user interface 165. In one embodiment, user interface 165 may be associated with a remote device communicatively coupled to device 110. For example, the remote device may provide access to the device via a web-based interface, a web-based downloadable application interface, etc.

[0027] As used herein, the term 'processing unit' refers to any type of computing circuitry, such as, but not limited to, microprocessors, microcontrollers, complex instruction set computing microprocessors, reduced instruction set computing microprocessors, very long instruction word microprocessors, explicit parallel instruction computing microprocessors, graphics processors, digital signal processors, or any other type of processing circuitry. Processing unit 155 may also include embedded controllers, such as general-purpose or programmable logic devices or arrays, application-specific integrated circuits, single-chip computers, etc. Typically, processing unit 155 may include hardware and software elements. Processing unit 155 can be configured for multithreading, meaning that processing unit 155 can simultaneously manage different computational processes, thereby executing any one in parallel or switching between active and passive computational processes.

[0028] Memory unit 170 can be volatile or non-volatile memory. Memory unit 170 can be coupled for communication with processing unit 155. Processing unit 155 can execute instructions and / or code stored in memory unit 170. Various computer-readable storage media can be stored in and accessed from memory unit 170. Memory unit 170 can include any suitable elements for storing data and machine-readable instructions, such as read-only memory, random access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, hard disk drive, removable media drive for handling compact disks, digital video disks, floppy disks, magnetic tape cassettes, memory cards, etc.

[0029] The memory unit 170 also includes a virtual orchestration module 175 in the form of machine-readable instructions on any of the aforementioned storage media, and is communicable with and executed by the processing unit 155. The virtual orchestration module 175 also includes a request processing module 177, a virtual copy selection module 180, a digital copy configuration module 182, an emulation module 185, an action module 187, a notification module 190, and a secure wallet module 192.

[0030] The device 110 may also include a storage unit 198. The storage unit 198 may include a database, which includes a digital library. The digital library includes data corresponding to multiple virtual copies associated with one or more types of spindle bearing assemblies suitable for machining equipment 105 or similar other machining equipment. The following description explains the function of the module when the processed unit 155 is in operation.

[0031] The request processing module 177 is configured to receive requests from the user interface to perform virtual orchestration of the machining equipment 105. As used herein, the term 'virtual orchestration' refers to at least one of testing, verification, and validation for enabling the management and use of the machining equipment 105 by using a digital copy of at least a spindle bearing assembly applicable to the machining equipment 105.

[0032] The request includes one or more input data indicating a type of virtual orchestration, and relevant information for performing the virtual orchestration. The virtual copy selection module 180 is configured to select at least one virtual copy from a plurality of virtual copies stored in a digital library within the virtual layer upon receiving the request. As used herein, the term 'virtual layer' refers to a logical abstraction of physical resources, such as computing, networking, and storage, that enables a single hardware resource to support multiple concurrent instances of device 110, or multiple hardware resources to support a single instance of device 110. In this document, concurrent instances of device 110 refer to multiple occurrences of the same device 110, typically running simultaneously on different machines or threads. Each concurrent instance of device 110 operates independently of other instances and can perform tasks concurrently. This allows multiple users or operators of processing equipment 105 to simultaneously access device 110 and perform virtual orchestration concurrently without interfering with each other's tasks.

[0033] In this document, each of the virtual copies corresponds to a spindle bearing assembly suitable for machining equipment 105 (e.g., Figure 2The digital copy configuration module 182 configures a digital copy of at least the spindle bearing assembly suitable for the machining equipment 105 within the virtual layer by updating the selected virtual copy using input data. Here, the digital copy is a dynamic mathematical representation of at least the spindle bearing assembly corresponding to the selected virtual copy. The digital copy configuration module 182 calibrates the digital copy to replicate a substantially similar response to the spindle bearing assembly. In other words, the digital copy is calibrated to ensure a certain degree of fidelity to the spindle bearing assembly.

[0034] Simulation module 185 is configured to calculate one or more workflow parameters associated with the spindle bearing assembly based on at least one run of simulation using a configured digital copy. Action module 187 is configured to perform at least one action based on one or more workflow parameters to generate a result. Notification module 190 is configured to generate a notification on display 160 associated with the result of performing at least one action.

[0035] The secure wallet module 192 is configured to identify a secure wallet template based on the result of at least one action of the virtual orchestration of the machining equipment 105. In this document, the term 'the result of at least one action' may include at least one deterministic and probabilistic value indicating an operational attribute of the spindle bearing assembly. Non-limiting examples of operational attributes include: optimal operating conditions, energy-saving operating conditions, optimal configuration, predicted machining time, availability, performance, runtime, etc. Furthermore, the secure wallet module 192 also configures the secure wallet based on the selected secure wallet template using one or more workflow parameters, wherein the secure wallet is a piece of code that can run on a decentralized network (not shown) to control financial transactions between the original equipment manufacturer (OEM) and consumers of the machining equipment 105.

[0036] Those skilled in the art will understand that Figure 1A and Figure 1B The hardware depicted may vary depending on the implementation. For example, additional peripherals such as optical disc drives and the like, local area network (LAN) / wide area network (WAN) / wireless (e.g., Wi-Fi) adapters, graphics adapters, disk controllers, input / output (I / O) adapters, and network connectivity devices may be used in addition to or in place of the depicted hardware. The examples depicted are provided for illustrative purposes only and are not intended to imply any architectural limitations with respect to this disclosure.

[0037] In this embodiment, for ease of explanation, the machining equipment 105 is considered to be a CNC lathe. A CNC lathe is a three-axis machine that includes a spindle attached to a tool holder. The tool holder holds the cutting tools required for machining the workpiece. The spindle is supported by an angular contact ball bearing (ACBB) assembly mounted on the spindle shaft.

[0038] Figure 2 The illustration shows an ACBB assembly 200 of a spindle associated with a machining apparatus 105 according to an embodiment of the present invention. The ACBB assembly 200 (hereinafter referred to as spindle bearing assembly 200) includes a housing 205 that houses an outer ring 210, an inner ring 215, and a plurality of uniformly sized balls 220 disposed between the outer ring 210 and the inner ring 215.

[0039] Figure 3A and 3B A digital copy 300 associated with a spindle bearing assembly 200 according to an embodiment of the present invention is illustrated. Herein, the digital copy 300 is based on a virtual copy of a one-dimensional (1D) dynamic model 305 including the spindle bearing assembly 200. This 1D dynamic model 305 is constructed using 1D simulation software that uses equations of motion and code to solve mathematical problems. Specifically, the 1D dynamic model 305 is a 5-degree-of-freedom (DOF) model of the spindle bearing assembly 200. This 5-DOF model includes two planar DOFs of the inner ring 215, two planar DOFs of the outer ring 210, and one planar DOF of the housing 205. Furthermore, inputs to the 1D dynamic model 305 include product specifications such as geometric parameters 310 associated with the spindle bearing assembly 200 (e.g., mass, diameter, connection stiffness, damping value), material properties 315, applied preload 320, radial load, and operating speed. Another input to the 1D dynamic model 305 includes the contact force 330 between each ball 220 in the spindle bearing assembly 200 and the inner ring 215. The output from the 1D dynamic model 305 includes performance metrics of the spindle bearing assembly 200, such as component displacements 335, velocity, and acceleration curves 340 of the housing 205, outer ring 210, and inner ring 215.

[0040] This virtual copy also includes a 1D thermal model 350 of the spindle bearing assembly 200. For example... Figure 3BAs shown, the 1D dynamic model and the 1D thermal model are operatively coupled to form an integrated dynamic-thermal model. In one example, this integrated dynamic-thermal model can provide a mathematical relationship between the dynamic and thermal characteristics of the spindle bearing assembly. Inputs to the 1D thermal model 350 include contact force 330 calculated by the motion simulation model 345. Outputs from the 1D thermal model 350 include heat generated in the spindle bearing assembly 200 or bearing temperature 360. In this document, the term 'bearing temperature' may refer to the temperature of the outer ring 210 and / or the inner ring 215. The bearing temperature 360 ​​is also used to calibrate the values ​​of lubrication parameters 355 and material properties or geometric parameters 310 of the spindle bearing assembly 310 using predetermined mathematical equations. Based on the calibrated material properties or geometric parameters 310, an induced thermal preload can be calculated. This induced thermal preload is also used to calibrate the applied preload 320.

[0041] like Figure 3B The motion simulation model 345, operatively coupled to the integrated dynamic thermal model, is configured to dynamically calculate the contact force 330 in the spindle bearing assembly 200. The motion simulation model 345 calculates the contact force 330 based on the applied preload 320, radial load, and operating speed 325 of the shaft on which the spindle bearing assembly 200 is mounted. In one embodiment, the motion simulation model 345 includes empirical equations solvable using 1D simulation software. In another embodiment, the motion simulation model 345 includes a 3D motion simulation model constructed using three-dimensional (3D) CAD software. In this 3D motion simulation model, the 3D contact between the ball 220 and the inner and outer rings 215 is defined based on stiffness and damping values ​​calculated using Hertzian contact theory. Inputs to the 3D motion simulation model include the bearing component displacement 335 of the shaft, the applied preload 320, the radial load, and the operating speed 325.

[0042] In one embodiment, such as Figure 3B As shown, the integrated dynamic thermal model and the three-dimensional motion simulation model 345 are used for joint simulation. Here, the 1D simulation software acts as the main program, and the 3D CAD software acts as the slave program. The 1D simulation software also generates major outputs such as component displacement 335, velocity and acceleration curves 340, heat generated in the bearing, bearing temperature 360, induced thermal preload, lubrication parameters 355, etc.

[0043] Advantageously, 3D CAD software allows for the modeling of faults, defects, and wear in the spindle bearing assembly 200, enabling precise calculation of the contact force 330, which is impossible using only 1D simulation software. For example, in the 3D motion simulation model 345, a fault in the spindle bearing assembly 200 can be modeled as a localized rectangular indentation in the inner ring 215 or the outer ring 210. Furthermore, the 3D motion simulation model 345 can be transformed into a finite element mesh to identify the contact stress at the fault location. Specifically, the workflow parameters required to evaluate the true state of the spindle bearing assembly 200 include velocity and acceleration curves 340, calibrated preload 320, contact force 330, and bearing temperature 360.

[0044] Figure 4 A flowchart depicts an exemplary method 400 for virtual orchestration to manage and use processing equipment 105 according to an embodiment of the present invention.

[0045] In step 405, a request to perform virtual orchestration of the machining equipment 105 is received by the processing unit 155 from the user interface 165. This request includes input data indicating one or more requirements associated with the virtual orchestration. For example, the input data may include a type of virtual debugging to be performed and information related to performing the virtual orchestration. The type of virtual debugging may be, for example, one of virtual testing and virtual design of the spindle bearing assembly associated with the machining equipment 105. Furthermore, virtual testing may be associated with identifying the optimal configuration of the spindle bearing assembly of the machining equipment 105, identifying the optimal operating conditions of the bearing assembly, predicting the machining time of the test input, etc.

[0046] Input data may include, for example, initial values ​​of operating conditions, bearing assembly configuration, expected production time, expected energy efficiency, and operating data, used to predict the behavior of the bearing assembly in a hypothetical scenario. Operating data can be obtained from, for example, real-time or historical data associated with machining equipment 105 or other similar machining equipment, simulated operating data, or manually entered by a human operator. In another embodiment, device 110 may also be configured to acquire real-time operating data from, for example, sensing units 125, controllers 115, etc., associated with machining equipment 105 or other similar machining equipment. For example, operating data may be associated with acceleration, thermal profile, operating axis speed, NC program, etc.

[0047] In one embodiment, the user interface of device 110 can be dynamically configured to receive requests from a human operator based on a type of virtual orchestration selected by the human operator from a set of virtual orchestration options. For example, if the human operator selects a virtual test to determine the optimal operating conditions for a bearing assembly, the user interface can be dynamically configured to receive input data, such as initial values ​​for the operating conditions.

[0048] In step 410, upon receiving a request, at least one virtual copy is selected from a plurality of virtual copies stored in a digital library within the virtual layer. Each of the virtual copies corresponds to a static mathematical representation of the spindle bearing assembly suitable for the machining equipment 105. In this embodiment, the virtual copy includes, for example,... Figure 3B The integrated dynamic thermal model is shown. In one embodiment, each of the plurality of virtual copies corresponds to a different type of spindle bearing assembly suitable for machining equipment 105. For example, different types of spindle bearing assemblies may include angular contact ball bearings, deep groove ball bearings, self-aligning ball bearings, etc.

[0049] In one embodiment, selecting at least one virtual copy from a plurality of virtual copies stored in a digital library includes: identifying metadata from input data that indicates one or more requirements for performing virtual orchestration within the virtual layer. For example, the metadata may indicate the type of virtual orchestration to be performed. Furthermore, the metadata may indicate that the spindle bearing assembly is an angular contact ball bearing, and the type of virtual orchestration may correspond to determining the optimal configuration of the spindle bearing assembly.

[0050] Furthermore, search logic is generated based on the identified metadata. For example, search logic can be generated by embedding metadata into a predefined search string. In one example, the search logic could be an updated search string. In another example, the search logic could be a sequence of selections within different categories of virtual copies. Additionally, based on the generated search logic, a search is performed in a digital library comprising multiple virtual copies to select a virtual copy. After performing the search, a model file containing model data associated with the selected virtual copy is retrieved. For example, the model file can be in formats such as XML, STL, OBJ, FBX, and DAE.

[0051] In a preferred embodiment, the digital library can be stored on a cloud platform. Furthermore, after selection based on search logic, a downgraded model of the selected virtual copy can be deployed on device 110 to support faster on-premises simulation.

[0052] In step 415, a digital copy of at least one spindle bearing assembly suitable for machining equipment 105 is configured within the virtual layer by updating the selected virtual copy based on input data. This digital copy is a dynamic mathematical representation of at least one spindle bearing assembly corresponding to the selected virtual copy. In one embodiment, updating the digital copy includes updating the selected virtual copy based on input data (e.g., simulation or real-time operational data) to generate one or more simulation instances. In this example, operational data is provided as input to an integrated dynamic thermal model (i.e., the digital copy) to generate one or more simulation instances corresponding to the spindle bearing assembly.

[0053] In step 420, based on at least one run of simulation using the configured digital copy, one or more workflow parameters associated with the spindle bearing assembly are calculated. In one embodiment, calculating one or more workflow parameters based on at least one run of simulation includes running one or more simulation instances of the configured digital copy in a simulation environment to generate one or more simulation results indicative of the one or more workflow parameters. For example, refer to... Figure 3B Vibration and temperature responses at one or more locations on the spindle bearing assembly can be virtually sensed by defining 'virtual sensors' at corresponding positions on a 3D motion simulation model of the spindle bearing assembly. The one or more locations where virtual sensors are defined can be spatial locations on the spindle bearing assembly where parameters cannot be directly measured using sensors. The output of such virtual sensors constitutes the workflow parameters. In the case of the spindle bearing assembly, workflow parameters may include, for example, parameters such as vibration, stiffness, preload, contact force, temperature, and stress at one or more locations on the spindle bearing assembly.

[0054] In step 425, at least one action is performed based on one or more workflow parameters to generate a result. This at least one action is determined based on one or more requirements indicated by the input data.

[0055] In one embodiment, the result of performing at least one action is the identification of the optimal configuration of the spindle bearing assembly. In this embodiment, performing at least one action based on one or more workflow parameters includes determining whether one or more workflow parameters meet predetermined criteria. For example, a workflow parameter could be the temperature at a location inside the housing of the spindle bearing assembly. Here, the predetermined criteria could include the maximum permissible temperature of a given spindle bearing assembly. If the calculated temperature (workflow parameter) is higher than the maximum permissible temperature, it damages the spindle bearing assembly. Therefore, it may be necessary to select a spindle bearing assembly that can withstand the calculated temperature. Alternatively, it may be necessary to select a spindle bearing assembly that generates less heat. In other words, it is necessary to identify the configuration of the spindle bearing assembly most suitable for the machining equipment 105. In one embodiment, if one or more workflow parameters do not meet the predetermined criteria, one or more other virtual copies of the spindle bearing assembly are selected from a digital library. Furthermore, steps 415 to 425 are repeated for each of the one or more other virtual copies until the predetermined criteria are met. In other words, one or more workflow parameters are calculated based on input data using multiple virtual copies until the predetermined criteria are met.

[0056] Alternatively, if one or more workflow parameters meet predetermined criteria, the spindle bearing assembly configuration corresponding to the selected virtual copy is identified from the selected virtual copy. For example, model data corresponding to the virtual copy could indicate that the configuration is an angular contact ball bearing. The configuration thus identified represents the optimal configuration of the spindle bearing assembly.

[0057] In another embodiment, one or more requirements may be associated with determining the optimal operating conditions for the spindle bearing assembly. In this case, the result of performing at least one action is the optimal operating conditions for the spindle bearing assembly. The optimal operating conditions are associated with one of the autonomous and manual operations of the machining equipment 105. The optimal operating conditions may be indicated by a set of operating parameters and / or control parameters to be used for the operation of the machining equipment 105. In this embodiment, performing at least one action based on one or more workflow parameters includes: using an optimization algorithm to calculate the optimal operating conditions for the spindle bearing assembly based on one or more workflow parameters. Figure 5 An exemplary method for calculating and maintaining optimal operating conditions for a spindle bearing assembly is illustrated according to an embodiment of the present invention.

[0058] In another embodiment, the result of performing one or more actions includes control parameters for controlling the operation of the machining equipment 105. In this embodiment, performing at least one action includes calculating the deviation between at least one of the workflow parameters and a corresponding measured parameter. In one example, the machining equipment 105 is in use, and the measured parameter is measured using one or more sensing units 125. In another example, the measured parameter may be measured manually by a human operator. Furthermore, the measured parameter may be provided to the device 110 as part of the input data. In one embodiment, the measured parameter is the actual production time associated with the machining equipment 105 or other machining equipment similar to the machining equipment 105, and the workflow parameter is the production time predicted based on a simulation performed using at least a digital copy of the spindle bearing assembly. In one example, the term 'deviation' may refer to the standard deviation between the average value of the measured parameter and the workflow parameter. Furthermore, if the calculated deviation is greater than a predefined value, a control parameter is calculated based on the calculated deviation. The calculated control parameter is adapted to reduce the deviation between at least one workflow parameter and the measured parameter when applied to the machining equipment 105. In one embodiment, this deviation is associated with the production time of the machining equipment 105. More specifically, the deviation can be calculated based on the processing time predicted by the simulation performed in step 420 and the measured processing time. Figure 6 An example of a workflow for predicting the production time of processing equipment is shown in the figure.

[0059] In step 430, a secure wallet configured based on the result of performing at least one action is run. A secure wallet is a piece of code that can run on a decentralized network to control transactions between consumer nodes and manufacturer nodes, where consumer nodes are nodes associated with consumers of processing equipment 105, and manufacturer nodes are nodes associated with manufacturers of processing equipment 105. As used herein, the term "node" refers to a processing device, such as a computer, participating in a decentralized network, configured to run protocol software for verifying transactions on the decentralized network. In one embodiment, running a secure wallet includes: first, identifying a secure wallet template based on one or more requirements indicated by input data. In one example, a first secure wallet template may be associated with a category determining the optimal configuration of the spindle bearing assembly. Once run, the corresponding secure wallet can initiate financial transactions between the manufacturer and consumer nodes to make payments to the manufacturer to utilize the optimal configuration of the spindle bearing assembly of processing equipment 105. These payments may be based, for example, on a pay-per-use basis, enabling the manufacturer to offer processing equipment 105 as a service to consumers.

[0060] In another example, the second secure wallet template may be associated with a category determining the deviation between workflow parameters and measurement parameters. Furthermore, based on the nature of the parameters and predetermined causes of deviation, the secure wallet is run to allow the manufacturer (or consumer) to penalize or reward the consumer (or manufacturer) for the deviation. In one embodiment, the cause of deviation may be determined based on a predefined algorithm (e.g., based on root cause analysis). For example, if the workflow parameter is availability, and if the actual availability of processing equipment 105 when used by a consumer is lower than the predicted availability, the consumer may be penalized if the low availability is attributable to improper use of the processing equipment by the consumer. Alternatively, the manufacturer may be penalized if the low availability is attributable to a manufacturing defect.

[0061] In one example, the decentralized network could be a private blockchain associated with a financial service provider, enabling the quantification of operational expenses associated with processing equipment 105 using a secure wallet that operates based on the results of at least one action. In one embodiment, the secure wallet can be updated by modifying code within it based on the results of at least one action performed. For example, calculated workflow parameters could indicate the predicted performance or availability associated with processing equipment 105 for a lease term (e.g., 20 years).

[0062] In another embodiment, method 400 further includes generating a notification on user interface 160 indicating the result of performing at least one action. For example, user interface 160 may display the values ​​of workflow parameters in different formats, including but not limited to text, graphics, augmented reality, virtual reality, etc.

[0063] Figure 5 A flowchart illustrating an exemplary workflow 500 for calculating and maintaining optimal operating conditions for a spindle bearing assembly associated with a machining equipment, according to an embodiment of the present invention.

[0064] In step 505, a digital copy 502 of the spindle bearing assembly is used to simulate the behavior of the spindle bearing assembly against a given input dataset. Based on this simulation, operational parameters associated with the spindle bearing assembly, such as vibration, temperature, stiffness, preload, contact force, and stress, are predicted, for example, in the form of time-series data.

[0065] In step 510, the workflow parameters are preprocessed. Preprocessing may include transforming the workflow parameters in time-series format to a spectrum. For example, feature extraction and feature selection may be performed on the workflow parameters in frequency domain format. Feature extraction may be performed using statistical analysis methods, such as, but not limited to, mean, standard deviation, root mean square, skewness, kurtosis, maximum, minimum, and peak factor. Feature selection may be performed using neural network-based techniques, such as, but not limited to, improved distance discrimination techniques, distance evaluation techniques, and discrete wavelets. It must be understood that feature selection is used to preprocess the data to improve the accuracy of the response prediction in step 515. In this document, the term 'response' may refer to at least one statistical parameter indicating the vibration, temperature, or remaining service life of one or more bearing assemblies in the spindle bearing assembly.

[0066] In step 515, based on the preprocessed data obtained in step 510, an artificial intelligence-based technique, such as a support vector machine, is used to predict the response associated with the spindle bearing assembly. In an alternative embodiment, other fuzzy logic-based techniques may also be used to predict the response. In a preferred embodiment, an optimization algorithm combining artificial intelligence techniques and fuzzy logic is used to predict the response with the desired level of accuracy. For example, neurofuzzy logic can be used to process vibration data (i.e., vibration spectra) in the frequency domain to predict the response of the spindle bearing assembly. After optimization, a set of workflow parameters associated with the spindle bearing assembly is generated. These workflow parameters include values ​​for vibration, temperature, preload, stiffness, etc., which indicate the characteristic response of the spindle bearing assembly at one or more locations or points of interest.

[0067] In step 520, machine learning and artificial intelligence-based search methods, such as gradient descent, genetic algorithms, and particle swarm optimization, are used to analyze workflow parameters to identify optimal operating conditions for the spindle bearing assembly. Optimal operating conditions may include specific values ​​for load, speed, preload, etc. In one embodiment, optimal operating conditions are associated with autonomous or unmanned operation of the machining equipment. In another embodiment, optimal operating conditions are associated with manual operation of the machining equipment.

[0068] In step 525, the optimal operating conditions for the spindle bearing assembly are also provided as input to the controller associated with the machining equipment. The controller also calculates control parameters corresponding to the optimal operating conditions. The calculated control parameters are further used to generate control commands for the machining equipment. After the control commands are implemented, the operating conditions of the spindle bearing assembly are modified to match the optimal operating conditions calculated in step 530.

[0069] In step 530, after the control command is issued, the operating parameters of the machining equipment are also used as feedback to reconfigure the digital copy 502. Steps 505 to 530 are repeated further to ensure that the spindle bearing assembly continues to operate under optimal conditions.

[0070] Figure 6 A flowchart of an exemplary method 600 for predicting production time associated with processing equipment is shown according to an embodiment of the present invention.

[0071] In step 605, a request is received for calculating the production time associated with the machining equipment. This request includes input data indicating requirements such as load, workpiece-related information, and spindle bearing assembly type.

[0072] In step 610, a virtual copy corresponding to the spindle bearing assembly of the machining equipment is selected from the digital library. This virtual copy can be a previously referenced... Figure 3A and Figure 3B An integrated dynamic thermal model is explained. Furthermore, this virtual copy is updated based on the input data to configure a digital copy of the spindle bearing assembly.

[0073] In step 615, a digital copy is run in the simulation environment used to run the simulation. After running the simulation, simulation results are generated, indicating changes in temperature, applied preload, and stiffness relative to at least one location on the spindle bearing assembly. Furthermore, the machining time of the machining equipment is calculated based on the simulation results, for example using a predetermined mathematical model.

[0074] In step 620, the calculated processing time is compared with the measured processing time corresponding to the processing equipment to determine the deviation. The measured processing time represents the actual processing time measured by the operator when the same set of requirements is applied to the actual processing equipment. If the determined deviation is greater than a predefined value, the virtual copy is recalibrated based on this deviation, and steps 610 to 620 are repeated. The recalibrated virtual copy can also be updated in the digital library. If the deviation is less than the predefined value, step 625 is executed.

[0075] In step 625, the calculated processing time is used to calculate the production time of the processing equipment.

[0076] Advantageously, this invention enables manufacturers of machining equipment to offer the equipment as a service to potential consumers, thereby eliminating the need for consumers to purchase the equipment by paying a price upfront. More specifically, this invention supports virtual orchestration of machining equipment to predict attributes (outcomes) associated with the equipment, and thus supports the quantification of operational expenses associated with the equipment via a secure wallet. Furthermore, the use of a secure wallet supports the automatic penalty or reward of consumers or manufacturers based on the performance of the machining equipment, depending on the factors influencing that performance. The use of digital copies facilitates accurate modeling of the real-time conditions of the spindle bearing assembly for simulation purposes, replacing simulation models that rely on ideal conditions. Therefore, errors associated with the prediction of performance metrics associated with the spindle bearing assembly are minimized. Moreover, this invention enables consumers to perform virtual orchestration of machining equipment without physical access to the equipment. More specifically, this invention enables consumers to make quick decisions regarding the selection of machining equipment or one or more components thereof suitable for a specific application without physical testing. Furthermore, consumers can perform virtual orchestration of machining equipment to predict its operational behavior to determine the optimal operating conditions for any given scenario.

[0077] This invention is not limited to a specific computer system platform, processing unit, operating system, or network. One or more aspects of this invention can be distributed across one or more computer systems, such as servers configured to provide one or more services to one or more client computers or to perform a complete task in a distributed system. For example, one or more aspects of this invention can be implemented on a client-server system comprising components distributed across one or more server systems performing multiple functions according to various embodiments. These components include, for example, executable code, intermediate code, or interpreted code that communicate over a network using communication protocols. This invention is not limited to being executable on any particular system or group of systems, nor is it limited to any particular distributed architecture, network, or communication protocol.

[0078] Although the invention has been illustrated and described in detail with the aid of preferred embodiments, the invention is not limited to the disclosed examples. Other modifications can be deduced by those skilled in the art without departing from the scope of the claimed invention.

Claims

1. A computer implementation method, comprising: a) The processing unit (155) receives a request from the user interface (165) for performing virtual orchestration of the processing equipment (105), wherein the request includes input data indicating one or more requirements associated with virtual operation of the mechanical equipment; b) Upon receiving the request, select at least one virtual copy from a plurality of virtual copies stored in a digital library within the virtual layer, wherein each of the virtual copies corresponds to a static mathematical representation of a spindle bearing assembly suitable for the machining equipment (105); c) By updating the selected virtual copy based on the input data, configure a digital copy of at least the spindle bearing assembly suitable for the machining equipment (105) within the virtual layer, wherein the digital copy is a dynamic mathematical representation of at least the spindle bearing assembly corresponding to the selected virtual copy; d) Calculate one or more workflow parameters associated with the spindle bearing assembly based on at least one simulation run using the configured digital copy; e) Perform at least one action based on the one or more workflow parameters to generate a result; and f) Generate a notification on the user interface (165) indicating the result of performing the at least one action.

2. The method according to claim 1, further comprising: A secure wallet adapted based on the result of performing the at least one action is run, wherein the secure wallet is a piece of code that can run on a decentralized network to control transactions between consumer nodes and manufacturer nodes, wherein the consumer node is a node associated with a consumer of the processing equipment (105), and wherein the manufacturer node is a node associated with a manufacturer of the processing equipment (105).

3. The method of claim 1, wherein selecting the at least one virtual copy from the plurality of virtual copies stored in the digital library comprises: Metadata is identified from the input data, the metadata indicating one or more requirements for performing the virtual orchestration within the virtual layer; Search logic is generated based on the identified metadata; and Based on the generated search logic, a search is performed in the digital library that includes the plurality of virtual copies to select the virtual copy.

4. The method of claim 1, wherein the virtual copy comprises an integrated dynamic thermal model, the integrated dynamic thermal model comprising at least a one-dimensional dynamic model operatively coupled to a one-dimensional thermal model of the spindle bearing assembly.

5. The method of claim 4, wherein the virtual copy further comprises a motion simulation model operatively coupled to the integrated dynamic thermal model, wherein the at least one motion simulation model is configured to dynamically calculate contact forces in the spindle bearing assembly.

6. The method according to any one of the preceding claims, wherein calculating the one or more workflow parameters associated with the spindle bearing assembly based on at least one simulation run using a configured digital copy comprises: Run one or more simulation instances of a configured digital copy in a simulation environment to generate one or more simulation results that indicate the one or more workflow parameters.

7. The method according to any one of the preceding claims, wherein the result of performing the at least one action is an optimal configuration of the spindle bearing assembly, wherein performing the at least one action based on the one or more workflow parameters includes: Determine whether the one or more workflow parameters meet predetermined criteria; If at least one of the workflow parameters does not meet the predetermined criteria: Select one or more other virtual copies from the digital library; and For each of the one or more other virtual copies, repeat steps (c) to (e) according to claim 1 until the one or more workflow parameters meet the predetermined criteria.

8. The method according to claims 1 and 7, further comprising: If one or more workflow parameters meet the predetermined criteria: Identify the configuration of the spindle bearing assembly corresponding to the selected virtual copy, wherein the identified configuration represents the optimal configuration of the spindle bearing assembly.

9. The method according to any one of the preceding claims, wherein the result of performing the at least one action is optimal operating conditions for the spindle bearing assembly, wherein the optimal operating conditions are associated with one of autonomous and manual operation of the machining equipment (105), and wherein performing the at least one action based on the one or more workflow parameters comprises: An optimization algorithm is used to calculate the optimal operating conditions for the spindle bearing assembly based on one or more workflow parameters.

10. The method according to any one of the preceding claims, wherein the result of performing the at least one action is a deviation between at least one of the workflow parameters and a corresponding measurement parameter, and wherein performing the at least one action based on the one or more workflow parameters comprises: Calculate the deviation between at least one of the workflow parameters and the corresponding measurement parameter.

11. The method according to claims 1 and 10, wherein the result of performing the at least one action is a control parameter for controlling the operation of the processing equipment (105), and wherein performing the at least one action based on the one or more workflow parameters comprises: If the deviation is greater than the predefined value: The control parameters are calculated based on the calculated deviation, wherein the calculated control parameters are adapted to reduce the deviation between the at least one workflow parameter and the measurement parameter when applied to the processing equipment (105).

12. The method according to claim 10 or 11, wherein the deviation is associated with the production time of the processing equipment (105).

13. The method of claims 1 and 2, wherein operating the secure wallet adapted based on the result of performing the at least one action further comprises: Identify a secure wallet template based on the one or more requests received with the request; and Based on the result of performing at least one action for virtual orchestration of the processing equipment (105), the selected secure wallet template is used to configure the secure wallet.

14. An apparatus (110) comprising: One or more processing units (155); and A memory unit (170) communicatively coupled to one or more processing units (155), wherein the memory unit (170) includes a virtual orchestration module (175) stored in the form of machine-readable instructions executable by the one or more processing units (155), wherein the virtual orchestration module (175) is configured to perform method steps according to any one of claims 1 to 13 for performing virtual orchestration to manage and use the processing equipment (105).

15. A computer program product having machine-readable instructions stored therein, which, when executed by one or more processing units, cause the processing units to perform the method according to any one of claims 1 to 13.

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