Product development using process control planning digital twins

Abstract

To digitally display, analyze, determine and optimize quality assurance of a product and process adequacy with respect to a specification and an acceptance criteria using a management plan.SOLUTION: In a method of developing a product, a digital system model (DSM) is generated which describes the product and a manufacturing process for the product, and a digital twin (DTw) of an instance of a component of a manufacturing process is generated. The DTw contains a digital replica of the instance. The method receives attribute data as to attributes of a process / process characteristic that are subject to a fluctuation cause which affects a product quality. The digital replica is performed using input of the attribute data, thereby updating the DTw to replicate the component instance of the manufacturing process that was performed. A data analysis of the manufacturing process is performed based on the updated DTw, and the manufacturing process is modified based on the data analysis to reduce the fluctuation in one or more product characteristics.SELECTED DRAWING: Figure 2

Description

Background Art

[0001] The present disclosure relates generally to the manufacture of products, and more particularly to the development of products that use a digital twin of a manufacturing process management plan or other components for manufacturing a product. The development life cycle for many products includes the design, development, and verification of the product, as well as the manufacturing process for manufacturing the product. The stages of the development life cycle are carried out in several different ways. Advanced Product Quality Planning (APQP) is a structured framework for planning and completing the work of the development life cycle to ensure that a high-quality product that meets cost goals is provided on time. Some APQP criteria describe planning, product design and development, process design and development, product and process verification, and current production, use, and maintenance after delivery.

[0002] The manufacturing process for a product may include several components such as a management plan leading to a manufacturing process having a quality assurance process. The management plan inputs the main product and process characteristics, design, process, machine failure mode and effect analysis (D / P / M FMEA), and production process flow, and defines management work and inspection, verification and evaluation methods to ensure quality in production. The management plan includes organizational management measurements of machines and processes to ensure build per print. Conventionally, a management plan is a description in a form readable by workers of management element requirements and how to meet those requirements. This limits the use of management plans by automation, and thus also reduces their value in the development life cycle.

[0003] Therefore, it would be desirable to have a system and method that takes into account at least some of the above problems and other possible problems that may occur.

Summary of the Invention

[0004] The exemplary embodiments of this disclosure broadly relate to the design and manufacture of products. The exemplary embodiments provide a digital system model (DSM) of reliable data describing a product and the manufacturing process for producing it, as well as a digital twin (DTw) of an instance of its components, such as a control plan for the manufacturing process. The control plan can be used to digitally visualize, analyze, determine, and optimize quality assurance of the product and process compliance with specifications and acceptance criteria. The DTw may be connected to real-world data collected from sensors during production operation, including manufacturing, quality, and environmental data.

[0005] Exemplary embodiments can use collected real-world data to verify and evaluate the validity of products and processes element by element and collectively for all elements within a control plan. In this regard, the DTw of the control plan may be a digital substitute for the physical production and quality assurance processes and can provide automated quality acceptability through digital tracing. The DSM and DTw of the control plan can convert traditional text-based information into digital, model-based, and machine-executable information, which may be analyzed, simulated, and optimized using various methods and data. Verification and evaluation may be continuous throughout the manufacturing process rather than at set stages in the manufacturing process, and when verification and evaluation are continuous, inspection interruptions and stop / restart cycles may be reduced. Exemplary embodiments may result in higher first-run quality rates, higher production rates, and higher equipment availability.

[0006] Therefore, this disclosure includes, but is not limited to, the following exemplary embodiments.

[0007] Several exemplary embodiments provide a method for developing a product. The method includes generating a reliable digital system model (DSM) of data describing a manufacturing process for producing a product, including the product and the machinery, materials, methods, equipment, and manpower used in the manufacturing process; generating a digital twin (DTw) of a case of components of the manufacturing process from the DSM, the DTw including a digital replica of a case of components of the manufacturing process, including one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process; receiving attribute data measured when the manufacturing process was performed to produce a product, the attribute data relating to attributes of process characteristics that are exposed to one or more causes of variation in product characteristics, including machinery, materials, methods, equipment, or manpower; running a digital replica using the attribute data input, thereby updating the DTw to replicate the case of components of the manufacturing process that was performed; performing a data analysis of the manufacturing process based on the updated DTw; and modifying the product or manufacturing process based on the data analysis, thereby modifying the product or manufacturing process to reduce variation in one or more of the product characteristics.

[0008] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any exemplary embodiments described above, a component of a manufacturing process is a control plan that further identifies process characteristics, methods for controlling the causes of variability to which the process characteristics are exposed, and methods for verifying and evaluating the attributes of the process characteristics, and generating a DTw of an example of a component includes generating a DTw of an example of a control plan, and modifying a manufacturing process includes modifying one or more of the control methods.

[0009] In some exemplary embodiments of the methods of any of the aforementioned exemplary embodiments, or any combination of any of the aforementioned exemplary embodiments, the method further includes establishing a digital thread from the DTw, which leads to trusted data of the DSM and attribute data measured when the manufacturing process was performed.

[0010] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any exemplary embodiments described above, generating a DSM includes evaluating a metamodel of components of a manufacturing process that specifies a logical schema for the components, and encoding reliable data about the components according to the logical schema.

[0011] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any of the exemplary embodiments described above, the components of the manufacturing process include one or more machines used in the manufacturing process, one or more of the machines being represented as computer models stored in a catalog and separated and distinguished from the DSM and DTw, and generating a DSM includes generating a DSM that calls out the computer models, and generating a DTw includes generating an example of the computer model stored in the catalog.

[0012] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any exemplary embodiments described above, the components of the manufacturing process include one or more methods used in the manufacturing process, one or more of the methods being represented as algorithms stored in a catalog and separated and distinguished from DSMs and DTws, where generating a DSM includes generating a DSM that calls out a method, and generating a DTw includes generating an example of an algorithm stored in the catalog.

[0013] In some exemplary embodiments of the methods of any of the aforementioned exemplary embodiments, or any combination of any of the aforementioned exemplary embodiments, the components of the manufacturing process include one or more machines and methods used in the manufacturing process, and generating the DTw includes identifying one or more machines and methods from the DSM, and generating the DTw, which includes examples of computer models and algorithms that represent one or more machines and methods.

[0014] In some exemplary embodiments of the methods of any of the aforementioned exemplary embodiments, or any combination of any of the aforementioned exemplary embodiments, generating a DTw further includes configuring a DTw to which an example of a computer model and algorithm is connected to a source of attribute data.

[0015] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or in any combination of any of the exemplary embodiments described above, performing a digital replica includes performing a computer model and algorithm using attribute data input from an attribute data source.

[0016] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any exemplary embodiments described above, generating a DTw involves generating digital twins (DTws) of multiple instances of a component of a manufacturing process, attribute data is received from multiple runs of the manufacturing process for each of the DTws, a digital replica is run to update the DTws to replicate the multiple instances of the component that were run, and data analysis is performed based on the updated DTws.

[0017] In some exemplary embodiments of the methods of any of the exemplary embodiments described above, or any combination of any exemplary embodiments described above, the data analysis is performed on an updated DTws from a run of a digital replica such as replicating multiple instances of the component, and the modification of the product or manufacturing process includes modifying the reliable data of the DSM based on the data analysis of multiple instances of the component.

[0018] Several exemplary embodiments provide an apparatus for developing a product. The apparatus comprises a memory configured to store computer program code, and a processing circuit configured to access the memory. The processing circuit is configured to execute a computer-readable program to cause the apparatus to perform at least one of the methods of any of the above-described exemplary embodiments, or any combination of any of the above-described exemplary embodiments.

[0019] Several exemplary embodiments provide a computer-readable storage medium for developing a product. The computer-readable storage medium is non-transient and has computer-readable program code stored therein. The program code causes a device to perform at least one of the methods of any of the above exemplary embodiments, or any combination of any of the above exemplary embodiments.

[0020] The features, aspects, and advantages of this disclosure, as well as other features, aspects, and advantages, will become apparent upon reading the following detailed description in conjunction with the accompanying drawings, which are briefly mentioned below. In the specific and exemplary embodiments described herein, whether two, three, four, or more features or elements expressed in this disclosure are expressly combined or otherwise enumerated, this disclosure includes any combination of such features or elements. This disclosure is intended to be read holistically, so that any separable feature or element of this disclosure, in any aspect and exemplary embodiment, can be considered to be combined unless expressly stated in the context of this disclosure.

[0021] Therefore, it should be understood that this "Summary of the Invention" is provided only for the purpose of summarizing some exemplary embodiments in order to provide a basic understanding of some aspects of the present disclosure. Therefore, it is recognized that the above exemplary embodiments are merely examples and should not be construed in any way as narrowing the scope or essence of the present disclosure. Other exemplary embodiments, aspects, and advantages will become apparent by referring to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings illustrate, by way of example, the principles of some of the described exemplary embodiments.

[0022] As described above, although the exemplary embodiments of the present disclosure have been described in general terms, reference is now made to the accompanying drawings. These drawings are not necessarily drawn to scale.

Brief Description of the Drawings

[0023] [Figure 1] An aircraft according to some exemplary embodiments of the present disclosure is shown. [Figure 2] A system for developing a product according to some exemplary embodiments is shown. [Figure 3A] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3B] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3C] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3D] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3E] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3F] A flowchart showing various steps in a method for developing a product according to an exemplary embodiment. [Figure 3G] A flowchart showing various steps in a method of developing a product according to an exemplary embodiment. [Figure 4] An apparatus according to some exemplary embodiments is shown. **DETAILED DESCRIPTION OF THE INVENTION**

[0024] With reference to the accompanying drawings, certain embodiments of the present disclosure will be described more comprehensively hereinafter. The accompanying drawings show some but not all of the embodiments of the present disclosure. In fact, the various embodiments of the present disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art. Throughout, like reference numerals represent like elements.

[0025] Unless specifically stated or otherwise apparent from the context, references to first, second, etc. should not be construed as implying a particular order. Also, a feature that may be described as being on top of another feature (unless specifically stated or otherwise apparent from the context) may instead be below it and vice versa. Similarly, a feature that may be described as being to the left of another feature may instead be to the right of it and vice versa. Also, although quantitative measurements, values, geometric relationships, etc. are referred to herein, unless otherwise specified, any or all of these, even if not all, may be approximate, being factors of possible acceptable variation, such as absolute or due to engineering tolerances.

[0026] As used herein, unless otherwise specified or evident from the context, the “or” in a pair of operands is an “inclusive or,” thereby in contrast to an “exclusive or,” which is true only if one or more operands are true, and false if all operands are true. For example, "[A] or [B]" is true if [A] is true, if [B] is true, or if [A] and [B] are true. Furthermore, the prepositions “a” and “an” mean “one or more” unless otherwise specified or evident from the context. In addition, unless otherwise specified, the terms “data,” “content,” “digital content,” “information,” and similar terms may sometimes be used interchangeably.

[0027] The exemplary embodiments of this disclosure broadly relate to the manufacture of products. The exemplary embodiments may be applied to the development of any of several different products, such as mechanical products, electrical products, electromechanical products, software products, chemical products, biological products, etc. More specific examples of suitable products include manned or unmanned transport vehicles (e.g., electric transport vehicles, orbital transport vehicles, ships, aircraft, spacecraft, etc.), aircraft hangars, office buildings, bridges, or other types of products. Products are broadly produced in accordance with (as a result of) a manufacturing process.

[0028] Exemplary embodiments provide a digital system model (DSM) of reliable data describing a product and the manufacturing process for producing it, as well as a digital twin (DTw) of instances of its components, such as a control plan for the manufacturing process. The control plan can be used to digitally visualize, analyze, determine, and optimize quality assurance of the product and process's validity against specifications and acceptance criteria. The DTw may be connected to real-world data collected from sensors during production operation, including manufacturing, quality, and environmental data. In some, but not all, embodiments, various terms used herein are defined in the context of an Advanced Product Quality Plan (APQP), for example, as in the SAE International Aerospace Series, Requirements for Advanced Product Quality Plans and Production Part Approval Processes, SAE Standard AS9145, Rev. Nov. 2016.

[0029] Figure 1 shows an aircraft 100 representing a type of product or assembly of a product that may be developed and manufactured according to exemplary embodiments of the present disclosure. As shown, the aircraft includes a fuselage 104, wings 106, and a tail 108. The aircraft also includes high-level systems 110, such as a propulsion system. In the particular embodiment shown in Figure 1, the propulsion system includes engines 112 mounted on two wings. In other embodiments, the propulsion system may include engines supported by other components, e.g., other parts of the aircraft, including the fuselage and / or tail. The high-level systems may also include an electrical system 114, a hydraulic system 116, and / or an environmental system 118. Any number of other systems may also be included.

[0030] Figure 2 shows a system 200 for developing a product 202, such as an aircraft 100 or an aircraft component, according to an exemplary embodiment of the present disclosure. The system may include any of several different subsystems (each a separate system) for performing one or more functions or operations. In some embodiments, the system includes machinery 204, materials 206, methods 208, equipment 210, and manpower 212 used in a manufacturing process 214 for producing the product. In some embodiments, the manufacturing process includes maintenance and servicing of at least some of the machinery used in the manufacturing process. Also as illustrated, the system includes one or more sensors or systems 216 configured to record or report data before, during, and / or after the production of the product 202.

[0031] The sensor or system 216 may include mounted or unmounted external sensors for one or more of the following: machinery 204, materials 206, equipment 210, or manpower 212, and may include an Internet of Things (IoT) in which sensors are embedded to record or report data. The sensor and system may include sensor concentrators and sensor aggregators, etc. More specific examples of sensors and systems include temperature sensors, humidity sensors, light sensors, distance / depth sensors, three-dimensional (3D) scanners, laser measuring sensors, imaging sensors such as cameras (e.g., digital cameras, infrared cameras, thermal cameras, depth-aware distance cameras, stereo cameras), acoustic sensors (e.g., microphones), etc. Further examples of sensors and systems include position sensors (e.g., GPS, inertial navigation, Wi-Fi-based positioning, RFID-based positioning), motion sensors (e.g., accelerometers), orientation sensors (e.g., gyroscopes, compasses, magnetometers), inertial measuring units (IMUs), pressure sensors, clocks, calendars, light sensors, tilt sensors, landmark systems, dynamometers, telemetry sensors, and fiber optic sensors.

[0032] In some embodiments, the data recorded or reported by the sensor or system 216 includes attribute data 218 measured when the manufacturing process 214 was performed to produce the product 202. The attribute data broadly includes attributes of process characteristics that are exposed to one or more sources of variation (input variables) among machinery, materials, methods, equipment, or manpower, which result in variations (output variables) in product characteristics.

[0033] Characteristics in exemplary embodiments are distinctive features, dimensions, or properties of a process or its output (product 202) from which attribute data 218 may be collected. Product characteristics are features or properties of a product that are described by reliable data about the product. Process characteristics include process variables (input variables) that have a cause-and-effect relationship with product characteristics. Process characteristics may be measured as they occur, and their variability may be controlled to minimize product variability. There may be one or more process characteristics for one or more of the product characteristics. Similarly, process characteristics may affect multiple product characteristics.

[0034] System 200 in the exemplary embodiments of this disclosure also includes a model-based engineering (MBE) subsystem 220 and a digital twin (DTw) execution subsystem 222. In some embodiments, the MBE subsystem and the DTw execution subsystem are located in the same location as or directly coupled to each other and / or to machinery 204, equipment 210, or sensors / systems 216. Furthermore or alternatively, in some embodiments, the MBE subsystem and the DTw execution subsystem may communicate with each other and / or with one or more of the machinery, equipment, or sensors / systems via one or more computer networks 224. Furthermore, it will be understood that any one or more of the above-described subsystems, although illustrated as parts of the system, may function or operate as a separate system without being involved with any of the other subsystems. It should also be understood that the system may include one or more further or alternative subsystems other than those shown in Figure 2.

[0035] The MBE subsystem 220 is configured to generate a digital system model (DSM) 226 of reliable data 228 that describes the product 202 and the manufacturing process 214 for producing the product, including machinery 204, materials 206, methods 208, equipment 210, and manpower 212 used in the manufacturing process. The DSM is a digital representation of the product that integrates the reliable data that defines the product and the manufacturing process with associated artifacts, and the DSM may be captured within a set of digitally linked data sources and models. The reliable data may include several components of the manufacturing process, such as key characteristics, design, process, manufacturing failure mode and effect analysis (D / P / M FMEA), production process flow, and control plan.

[0036] The DTw execution subsystem 222 is comprised of and enabled by a DTw 232 of an instance 234 of a component 230 of a manufacturing process 214 from a DSM 226. The DTw includes a digital replica 236 of the instance of a component of the manufacturing process, including one or more of the machinery 204, materials 206, methods 208, equipment 210, or manpower 212 used during the manufacturing process. In some embodiments, the DTw execution subsystem is configured to generate the DTw from the DSM in the DTw execution environment through a series of steps of conversion. The DTw may therefore provide an alternative that can understand the behavior and characteristics of the physical production system through analysis and simulation, as well as an automated quality buyoff based on attribute data 218 from sensors / systems 216.

[0037] The DTw execution subsystem 222 is configured to receive attribute data 218 measured when the manufacturing process 214 was executed to produce product 202 from sensors / systems 216, etc. The DTw execution subsystem is configured to use the attribute data input to execute a digital replica 236, thereby updating DTw 232 to replicate the instance 234 of the components of the executed manufacturing process. Based on the updated DTw, the DTw execution subsystem is configured to perform data analysis of the manufacturing process, and based on the data analysis, modify the product or manufacturing process to reduce variability in one or more of the product characteristics.

[0038] In the case of a complex manufacturing process, one example of data analysis and correction that can be performed involves extracting correlations between measured values ​​of multiple manufacturing variables and quality metrics. These manufacturing variables may include environmental control data and machine attributes (e.g., tensile strength, head speed), and quality metrics may include classifications of the manufactured products, such as optimal, insignificant, or defective. Appropriate corrections may be encoded from the correlations to generate a machine control dataset, which is then fed into automated machines performing the manufacturing process. This optimizes the balance between these inputs, thereby improving quality.

[0039] In some embodiments, a component 230 of the manufacturing process 214 is a control plan that identifies process characteristics 238. In some of these embodiments, the control plan further identifies control methods 240 for the causes of variability to which the process characteristics are exposed, and verification and evaluation (V&V) methods 242 for attributes of the process characteristics. In some of these embodiments, a DTw execution subsystem 222 is also configured to generate a DTw 232 for an example 234 of the control plan. The DTw execution subsystem is then configured to modify one or more of the control methods, thereby modifying, improving, or optimizing the manufacturing process.

[0040] In some embodiments, the DTw execution subsystem 222 is further configured to establish a digital thread 244 from the DTw 232, which leads to trusted data 228 from the DSM 226 and attribute data 218 measured when the manufacturing process 214 was executed. In a product context, digital threads are known to describe a framework for connecting data flows and generating a holistic view of the data across the entire lifecycle of the product, along with data traceability for the product. Exemplary embodiments may leverage a more traceable, accurate, reusable, and efficient model-based approach.

[0041] The structure of components 230, such as control plans, enables the systematic identification of measurement data requirements within the context of the digital thread 244 spanning engineering, management, and execution. As design changes or methods evolve or are newly discovered, these data requirements may also evolve along with the control plan as improvements. These data requirements may provide coherence and justification for a set of attribute data 218, which is often difficult (which data to collect and for what purpose). Coherence in the digital thread may enable the connection of attribute data for DTw 232 with corresponding attribute data from the manufacturing process 214. Furthermore, it may enable product / process engineering design to position the results derived from the attribute data within the context of manufacturing process issue identification, problem solving, and optimization.

[0042] In some embodiments, an MBE subsystem 220 configured to generate a DSM 226 includes an MBE subsystem configured to access a metamodel 246 of the components of a manufacturing process 214 that specifies a logical schema 248 for the components. The MBE subsystem is then configured to encode trusted data 228 for the components according to the logical schema, thereby generating a DSM.

[0043] The design of components of DSM226, such as control plans, may provide a metamodel 246 as a representation of the control plan. The design may enable the systematic generation of arbitrary instances of control plans for each element, and using behavioral models, results from low-level V&V for each attribute can be aggregated and synthesized into a high-level control plan that historically views the entire manufacturing process 214. The design may establish a blueprint encoded within a modeling tool for generating a consistent, model-based control plan based on the digital thread 244. The model-based control plan may be analyzed through formal techniques and simulation methods.

[0044] In some embodiments, the components 230 of the manufacturing process 214 include one or more machines 204 used in the manufacturing process, which are represented as computer models 250, stored in a catalog 252 and separated and distinguished from the DSM 226 and DTw 232. In some of these embodiments, the MBE subsystem 220 is configured to generate a DSM that calls out the computer model. Also in some of these embodiments, the DTw execution subsystem 222, configured to generate a DTw, includes a DTw execution subsystem configured to generate instances of the computer model stored in the catalog.

[0045] In some embodiments, the components 230 of the manufacturing process 214 include one or more of the V&V methods 242 used to analyze data generated during the manufacturing process, which are represented as algorithms 254 and stored in a catalog 252 and separated and distinguished from the DSM 226 and DTw 232. In some of these embodiments, the MBE subsystem 220 is configured to generate DSMs that call out the methods. The DTw execution subsystem 222, configured to generate DTws, includes a DTw execution subsystem configured to generate instances of the algorithms stored in the catalog and prepare them to perform the task of receiving data from the manufacturing process.

[0046] Catalog 252 in various exemplary embodiments may therefore store computer models 250 and algorithms 254 for machinery 204 and V&V methods 242 used to analyze data generated during the manufacturing process 214. Separation of computer models and algorithms from the core structure of DSM 226 and DTw 232 may enable the development and evaluation of those computer models and algorithms, as well as combined behavioral models of the manufacturing process, using independent, dedicated analysis tools, based on the requirements of control plans for verification and evaluation at each process step. This may include mechanisms for modeling the behavior and characteristics of machinery and processes. These may be monitored and analyzed by DTw for quality assurance. Appropriate embodiments of mechanisms include establishing relationships between control elements (e.g., primary-secondary, alternating, and conditional) and combining element-level evaluation and verification results for the manufacturing process (e.g., AND, OR, grouping by importance, etc.). Furthermore, analysis, simulation, and optimization algorithms / routines may be called out as methods within the DSM of the manufacturing process component 230, and may be incorporated within the DTw of the component (e.g., control plan).

[0047] Furthermore, catalog 252 may enable the MBE subsystem 220 to generate DSM 226 without native support for generating and running computer model 250 and algorithm 254. The catalog can be used to organize computer models and algorithms in the following manner: that is, it enables the MBE subsystem to select several appropriate ones for the configuration of the DSM when the DSM is generated. Similarly, the DTw execution subsystem 222 may generate DTw 232 by illustrating those computer models and methods configured within the DSM from the catalog.

[0048] In some embodiments, the components 230 of the manufacturing process 214 include one or more of the machinery 204 and methods 208 used in the manufacturing process. In some of these embodiments, a DTw execution subsystem 222 configured to generate a DTw 232 includes a DTw execution subsystem configured to identify one or more of the machinery and methods to be called out from the DSM 226. The DTw execution subsystem is configured to generate a DTw that includes a computer model 250 and an example of an algorithm 254 representing one or more of the machinery and methods.

[0049] In some embodiments, a DTw execution subsystem 222 configured to generate a DTw 232 further includes a DTw execution subsystem designed to configure a DTw, with an example of a computer model 250 and an algorithm 254 connected to a source of attribute data 218. In some embodiments, a DTw execution subsystem 222 designed to run a digital replica 236 includes a DTw execution subsystem designed to run a computer model 250 and an algorithm 254 using the input of attribute data 218 from an attribute data source.

[0050] In some embodiments, the DTw execution subsystem 222 is designed to generate digital twins (DTws) 232 of multiple instances 234 of a component 230 of a manufacturing process 214. In some of these embodiments, attribute data 218 is received by the DTw execution subsystem 222 from multiple runs of the manufacturing process for each DTws. A digital replica 236 is executed by the DTw execution subsystem to update the DTws to replicate multiple instances of the executed component. Data analysis is then performed by the DTw execution subsystem based on the updated DTws.

[0051] In some further embodiments, the DTw execution subsystem 222 is designed to perform data analysis based on updated DTws from a digital replica execution that replicates multiple instances of the component. In some of these embodiments, the DTw execution subsystem is configured to modify the trusted data 228 of the DSM 226 based on the data analysis of multiple instances of the component.

[0052] Figures 3A to 3G are flowcharts illustrating various steps in a method 300 for developing a product 202 according to various exemplary embodiments of the present disclosure. The method includes generating a digital system model (DSM) 226 of reliable data 228 describing the product and a manufacturing process 214 for producing the product, including machinery 204, materials 206, methods 208, equipment 210, and manpower 212 used in the manufacturing process, as shown in block 302. The method includes generating a digital twin (DTw) 232 of an example 234 of a component 230 of the manufacturing process from the DSM 226, as shown in block 304. In some of these embodiments, the DTw includes a digital replica 236 of an example of a component of the manufacturing process, including one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process.

[0053] Method 300 includes receiving attribute data 218 measured when a manufacturing process 214 was performed to produce a product 202, as shown in block 306. The attribute data includes attributes of process characteristics that are exposed to one or more causes of variation among machinery 204, materials 206, methods 208, equipment 210, or manpower 212 that result in variations in product characteristics. The method includes running a digital replica 236 using the input of attribute data, as shown in block 308, thereby updating DTw 232 to replicate instances 234 of the components 230 of the performed manufacturing process. The method includes performing a data analysis of the manufacturing process based on the updated DTw, as shown in block 310. The method further includes modifying a product or manufacturing process based on the data analysis, as shown in block 312, to reduce variations in one or more of the product characteristics.

[0054] In some embodiments, a component 230 of the manufacturing process 214 is a control plan that identifies process characteristics 238. In some of these embodiments, the control plan further identifies control methods 240 for the causes of variability to which the process characteristics are exposed, and verification and evaluation methods 242 for attributes of the process characteristics. Also in some of these embodiments, generating a DTw 232 for a component example 234 in block 304, as shown in block 314, includes generating a DTw for a control plan example. And modifying the manufacturing process in block 312, as shown in block 316, includes modifying one or more of the control methods.

[0055] In some embodiments, as shown in block 318, method 300 further includes establishing a digital thread 244 from DTw 232, which leads to trusted data 228 of DSM 226 and attribute data 218 measured when the manufacturing process 214 was performed.

[0056] In some embodiments, generating the DSM226 in block 302, as shown in block 320, involves accessing a metamodel 246 of the components 230 of the manufacturing process 214, which specifies a logical schema 248 for the components. Then, as shown in block 322, the trusted data 228 for the components is encoded according to the logical schema.

[0057] In some embodiments, the components 230 of the manufacturing process 214 include one or more of the machinery 204 used in the manufacturing process, one or more of the machinery being represented as a computer model 250, which is stored in a catalog 252 and separated and distinguished from the DSM 226 and DTw 232. In some of these embodiments, generating a DSM in block 302, as shown in block 324, includes generating a DSM that calls out a computer model. Also in some of these embodiments, generating a DTw in block 304, as shown in block 326, includes generating an example of the computer model stored in the catalog.

[0058] In some embodiments, a component 230 of the manufacturing process 214 includes one or more of the V&V methods 242 used to analyze data generated during the manufacturing process, and one or more of the V&V methods are represented as algorithms 254, which are stored in a catalog 252 and separated and distinguished from DSMs 226 and DTws 232. In some of these embodiments, generating a DSM in block 302, as shown in block 328, includes generating a DSM that calls out a method, and generating a DTw in block 304, as shown in block 330, includes generating an example of the algorithm stored in the catalog.

[0059] In some embodiments, the components 230 of the manufacturing process 214 include one or more of the machinery 204 and methods 208 used in the manufacturing process. In some of these embodiments, generating DTw 232 in block 304, as shown in block 332, includes identifying one or more of the machinery and methods from DSM 226. Then, as shown in block 334, DTw is generated including instances of computer models 250 and algorithms 254 that represent one or more of the machinery and methods. In some further embodiments, generating DTw in block 334, as shown in block 336, further includes constituting DTw, where the instances of computer models and algorithms are connected to a source of attribute data 218.

[0060] In some embodiments, as shown in block 338, running a digital replica 236 in block 308 includes running a computer model 250 and algorithm 254 using the input attribute data 218 from an attribute data source.

[0061] In some embodiments, generating a DTw232 in block 304, as shown in block 340 of Figure 3G, involves generating digital twins (DTws) of multiple instances 234 of a component 230 of a manufacturing process 214. In some of these embodiments, in block 306, attribute data 218 is received from multiple runs of the manufacturing process for each of the DTws; in block 308, a digital replica 236 is run to update the DTws to replicate multiple instances of the executed component; and in block 310, data analysis is performed based on the updated DTws.

[0062] In some further embodiments, data analysis is performed in block 310 based on updated DTws from a run of a digital replica 236, such as duplicating multiple instances of the component. In some of these embodiments, modifying the product or manufacturing process in block 312, as shown in block 342, includes modifying the trusted data 228 of the DSM 226 based on data analysis of multiple instances of the component.

[0063] According to exemplary embodiments of this disclosure, the system 200 and its subsystems, including the MBE subsystem 220 and the DTw execution subsystem 222, may be implemented by various means. Means for implementing the system and its subsystems may include hardware, either alone or following the instructions of one or more computer programs from a computer-readable storage medium. In some examples, one or more devices may function as the system and its subsystems illustrated and described herein, or be configured to implement them otherwise. In examples involving two or more devices, each device may be connected to or otherwise able to communicate with one another in several different ways, for example, directly or indirectly via a wired or wireless network.

[0064] Figure 4 shows an apparatus 400 according to several exemplary embodiments of the present disclosure. Generally, the apparatuses of exemplary embodiments of the present disclosure may comprise, include, or be embodied in one or more fixed or portable electronic devices. Examples of suitable electronic devices include smartphones, tablet computers, laptop computers, desktop computers, workstation computers, and server computers. The apparatus may include one or more of several components, such as a processing circuit 402 (e.g., a processor unit) connected to a memory 404 (e.g., a storage device).

[0065] The processing circuit 402 may consist of one or more processors, either standalone or combined with one or more memories. The processing circuit is generally any computer hardware capable of processing information, such as data, computer programs, and / or other suitable electronic information. The processing circuit is composed of a collection of electronic circuits, some of which may be packaged as a single integrated circuit or multiple interconnected integrated circuits (integrated circuits are more commonly referred to as "chips"). The processing circuit may be configured to execute computer programs, which may be stored on the processing circuit or otherwise stored in memory 404 (of the same or a different device).

[0066] The processing circuit 402 may be several processors, a single multicore processor, or any other type of processor, depending on the particular embodiment. Furthermore, the processing circuit may be implemented using several heterogeneous processor systems (where the main processor resides on a single chip along with one or more secondary processors). In another embodiment, the processing circuit may be a symmetric multiprocessor system encompassing multiple homogeneous processors. In yet another example, the processing circuit may be embodied as one or more ASICs or FPGAs, or may include them. Thus, the processing circuit may be capable of executing a computer program to perform one or more functions, but the processing circuits in various examples may be capable of performing one or more functions without the assistance of a computer program. In any case, the processing circuit may be appropriately programmed to perform functions or operations according to the exemplary embodiments of this disclosure.

[0067] Memory 404 is generally any computer hardware capable of temporarily and / or permanently storing information such as data, computer programs (e.g., computer-readable program code 406), and / or other appropriate information. Memory may include volatile and / or non-volatile memory and may be fixed or removable. Suitable examples of memory include random-access memory (RAM), read-only memory (ROM), hard drives, flash memory, thumb drives, removable computer diskettes, optical discs, magnetic tapes, or any combination thereof. Optical discs may include compact discs (read-only memory (CD-ROM), read / write (CD-R / W)), DVDs, etc. In various contexts, memory may be referred to as computer-readable storage media. Computer-readable storage media are non-transient devices capable of storing information and may be distinguished from computer-readable transmission media (such as transient electronic signals capable of carrying information from one place to another). The computer-readable media described herein may generally refer to computer-readable storage media or computer-readable transmission media.

[0068] The processing circuit 402 may be connected to one or more interfaces in addition to the memory 404 for displaying, transmitting, and / or receiving information. Interfaces may include a communication interface 408 (e.g., a communication unit) and / or one or more user interfaces. The communication interface may be configured to transmit and / or receive information to and from, for example, other devices or networks. The communication interface may be configured to transmit and / or receive information via physical (wired) and / or wireless communication links. Examples of suitable communication interfaces include network interface controllers (NICs) and wireless NICs (WNICs).

[0069] The user interface may include a display 410 and / or one or more user input interfaces 412 (e.g., input / output units). The display may be configured to present or otherwise display information to the user, and preferred examples include liquid crystal displays (LCDs), light-emitting diode displays (LEDs), plasma display panels (PDPs), etc. The user input interfaces may be wired or wireless and may be configured to receive information from the user into, for example, a device for processing, storage, and / or display. Preferred examples of user input interfaces include microphones, image or video capture devices, keyboards or keypads, joysticks, touch-sensitive surfaces (separate from or integrated with the touchscreen), biometric sensors, etc. The user interface may further include one or more interfaces for communicating with peripheral devices (e.g., printers, scanners, etc.).

[0070] As described above, program code instructions may be stored in memory and executed by processing circuits (programmed by program code instructions) to implement the functions of the systems, subsystems, tools, and corresponding elements described herein. Any suitable program code instructions may be loaded from a computer-readable storage medium into a computer or other programmable device to manufacture a particular machine, which may be recognized as such, and which will serve as a means for implementing the functions specified herein. Such program code instructions may be stored in a computer-readable storage medium, which can issue instructions to a computer, processing circuit, or other programmable device to produce a particular machine or a particular product by functioning in a particular manner. Instructions stored in a computer-readable storage medium can produce a product, which will serve as a means for performing the functions described herein. Program code instructions may be read from a computer-readable storage medium and loaded into a computer, processing circuit, or other programmable device in order to configure the computer, processing circuit, or other programmable device to perform an operation that is to be performed by the computer, processing circuit, or other programmable device.

[0071] The reading, loading, and execution of program code instructions may be performed sequentially, such that one instruction is read, loaded, and executed at a time. In some exemplary embodiments, the reading, loading, and / or execution may be performed in parallel, such that multiple instructions are read, loaded, and / or executed together. Executing program code instructions may generate a process implemented by a computer, processing circuit, or other programmable device, such that the instructions executed by the computer provide steps for implementing the functions described herein.

[0072] The execution of instructions by a processing circuit, or the storage of instructions in a computer-readable storage medium, supports a combination of steps for performing a specific function. Thus, the device 400 may include a processing circuit 402 configured to execute computer-readable program code 406 stored in memory, and a computer-readable storage medium or memory 404 connected to the processing circuit. It will also be understood that one or more functions and combinations of functions may be performed by a computer system based on special-purpose hardware, and / or by a processing circuit that performs a specific function, or by a combination of special-purpose hardware and program code instructions.

[0073] Further exemplary and non-exclusive embodiments of this disclosure are described in the following paragraphs.

[0074] In one embodiment of the present disclosure, an apparatus (400) for developing a product (202) comprises a memory (404) configured to store computer-readable program code (406), and a processing circuit (402) configured to access the memory, wherein the processing circuit executes the computer-readable program code to generate a digital system model (DSM) (226) (302) of reliable data (228) describing a manufacturing process for producing the product, including at least the product and machinery (204), materials (206), methods (208), equipment (210), and manpower (212) used in the manufacturing process (214), and generates a digital twin (DTw) (232) (304) of an example (234) of a component (230) of the manufacturing process, wherein the DTw is a digital twin of an example of a component of the manufacturing process, including one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process. The system is configured to generate a DTw including a replica (236), receive (306) attribute data (218) measured when the manufacturing process was performed to produce a product, the attribute data being about attributes of process characteristics that are exposed to one or more causes of variation among machinery, materials, methods, equipment, or manpower that result in variations in product characteristics, run (308) a digital replica (236) using the input attribute data, thereby updating the DTw to replicate instances of the components (230) of the performed manufacturing process, run a digital replica (310) performing data analysis of the manufacturing process based on the updated DTw, and modify (312) a product or manufacturing process based on the data analysis, thereby modifying the product or manufacturing process to reduce variations in one or more of the product characteristics.

[0075] Optionally, in the apparatus (400) of the preceding paragraph, the components (230) of the manufacturing process (214) are a control plan that further identifies process characteristics (238), control methods (240) for the causes of variability to which the process characteristics are exposed, and verification and evaluation methods (242) for the attributes of the process characteristics, and the apparatus that can be made to generate (304) a DTw (232) of an example of a component (234) includes the apparatus that can be made to generate (314) a DTw of an example of a control plan, and the apparatus that can be made to modify (312) the manufacturing process includes the apparatus that can be made to modify (316) one or more of the control methods.

[0076] Optionally, in one of the devices (400) in the preceding paragraph, the device may be made to establish a digital thread (244) (318) from DTw (232) which leads to reliable data (228) of the DSM (226) and attribute data (218) measured when the manufacturing process (214) was performed.

[0077] Optionally, one of the devices (400) in the preceding paragraph may be configured to generate a DSM (226) (302), and to access (320) a metamodel (246) of components of a manufacturing process (214) that specifies a logical schema (248) for the components (230), and to encode (322) reliable data (228) about the components according to the logical schema.

[0078] Optionally, in one of the apparatuses (400) in the preceding paragraph, the components (230) of a manufacturing process (214) include one or more machines (204) used in the manufacturing process, one or more of which are represented as computer models (250) stored in a catalog (252) and separated and distinguished from DSMs (226) and DTws (232), and the apparatus that is made to generate a DSM (302) includes the apparatus that is made to generate a DSM that calls out the computer model (324), and the apparatus that is made to generate a DTw (304) includes the apparatus that is made to generate an example of the computer model stored in the catalog (326).

[0079] Optionally, in one of the apparatuses (400) in the preceding paragraph, the components (230) of a manufacturing process (214) include one or more methods used in the manufacturing process, one or more of the methods being represented as algorithms (254) which are stored in a catalog and separated and distinguished from DSMs (226) and DTws (232), and the apparatus that can generate a DSM (302) includes the apparatus that can generate a DSM that calls out a method (328), and the apparatus that can generate a DTw (304) includes the apparatus that can generate an example of the algorithm stored in the catalog (330).

[0080] Optionally, one of the apparatuses (400) in the preceding paragraphs, wherein the components (230) of a manufacturing process (214) include one or more of the machinery (204) and methods (208) used in the manufacturing process, and the apparatus that can be made to generate a DTw (232) (304) includes the apparatus that can be made to identify one or more of the machinery and methods from a DSM (226) (332), and generate a DTw (334) which includes examples of computer models (250) and algorithms (254) that represent one or more of the machinery and methods.

[0081] Optionally, one or more of the devices (400) in the preceding paragraphs may further include a device that can be made to generate (334) a DTw (232), and a device that can be made to configure (336) a DTw, wherein an example of a computer model (250) and an algorithm (254) is connected to a source of attribute data (218).

[0082] Optionally, one of the devices (400) in the preceding paragraphs, which is to be made to perform a digital replica (236) (308), includes a device that is to be made to perform a computer model (250) and an algorithm (254) (338) using attribute data input from a source of attribute data (218).

[0083] Optionally, one of the devices (400) in the preceding paragraphs, which is configured to generate (304) a DTw (232), includes a device configured to generate (340) digital twins (DTws) of multiple instances of the components (230) of the manufacturing process (214), attribute data (218) is received from multiple runs of the manufacturing process for each of the DTws (306), a digital replica (236) is executed (308) to update the DTws to replicate the multiple instances of the executed components, and data analysis is performed based on the updated DTws (310).

[0084] Optionally, one of the devices (400) in the preceding paragraphs may perform data analysis based on updated DTws from the execution of a digital replica (236) that replicates multiple instances of a component, and may perform modification (312) of a product or manufacturing process, including a device which may perform modification (228) of the reliable data (228) of the DSM (226) based on data analysis of multiple instances of a component.

[0085] In another embodiment of the present disclosure, a method (300) for developing a product (202) includes generating a digital system model (DSM) (226) of reliable data (228) describing the product and a manufacturing process for producing the product, including machinery (204), materials (206), methods (208), equipment (210), and manpower (212) used in the manufacturing process (214); generating a digital twin (DTw) (232) (304) of an example (234) of a component (230) of the manufacturing process from the DSM, wherein the DTw includes a digital replica (236) of the example of a component of the manufacturing process, which includes one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process; and generating a DTw which describes the manufacturing process performed to produce the product. Receiving (306) attribute data measured at any given time, wherein the attribute data is of an attribute of a process characteristic that is exposed to one or more causes of variation among machinery, materials, methods, equipment, or manpower that cause variation in product characteristics; running a digital replica (308) using the input of the attribute data, thereby updating the DTw to replicate an example of the components of the manufacturing process that has been performed; running a digital replica (310) based on the updated DTw; and modifying (312) the product or manufacturing process based on the data analysis, which includes modifying the product or manufacturing process to reduce variation in one or more of the product characteristics.

[0086] Optionally, in the method (300) of the preceding paragraph, the components (230) of the manufacturing process (214) are a control plan that further identifies process characteristics (238), control methods (240) for the causes of variability to which the process characteristics are exposed, and verification and evaluation methods (242) for the attributes of the process characteristics, and generating a DTw (232) for an example of a component (234) (304) includes generating a DTw for an example of a control plan (314), and modifying the manufacturing process (312) includes modifying one or more of the control methods (316).

[0087] Optionally, one of the methods (300) described in the preceding paragraph further includes establishing a digital thread (244) from DTw (232) which leads to reliable data (228) of DSM (226) and attribute data (218) measured when the manufacturing process (214) was performed.

[0088] Optionally, one of the methods in the preceding paragraph (300) includes generating a DSM (226) (302) by accessing (320) a metamodel (246) of the components (230) of a manufacturing process (214) that specifies a logical schema (248) about the components, and encoding (322) reliable data (228) about the components according to the logical schema.

[0089] Optionally, in one of the methods (300) described in the preceding paragraph, the components (230) of a manufacturing process (214) include one or more machines (204) used in the manufacturing process, one or more of which are represented as computer models (250) stored in a catalog (252) and separated and distinguished from DSMs (226) and DTws (232), and generating a DSM (302) includes generating a DSM (324) that calls out the computer model, and generating a DTw (304) includes generating an example of the computer model stored in the catalog (326).

[0090] Optionally, in one of the methods (300) described in the preceding paragraph, the components (230) of a manufacturing process (214) include one or more methods used in the manufacturing process, one or more of which are represented as algorithms (254) that are stored in a catalog and separated and distinguished from DSMs (226) and DTws (232), and generating a DSM (302) includes generating a DSM that calls out a method (328), and generating a DTw (304) includes generating an example of an algorithm stored in a catalog (330).

[0091] Optionally, in one of the methods (300) described in the preceding paragraphs, the components (230) of the manufacturing process (214) include one or more machines (204) and methods (208) used in the manufacturing process, and generating a DTw (232) (304) includes identifying one or more machines and methods from a DSM (226) (332), and generating a DTw (334) which includes examples of computer models (250) and algorithms (254) that represent one or more machines and methods.

[0092] Optionally, one or more of the methods (300) described in the preceding paragraphs further include generating (334) a DTw (232) by constructing (336) a DTw in which an example of a computer model (250) and an algorithm (254) is connected to a source of attribute data (218).

[0093] Optionally, one of the methods (300) in the preceding paragraphs, performing a digital replica (236) (308) includes performing a computer model (250) and an algorithm (254) (338) using attribute data input from a source of attribute data (218).

[0094] Optionally, in one of the methods in the preceding paragraph (300), generating a DTw (232) (304) includes generating (340) digital twins (DTws) of multiple instances of the components (230) of a manufacturing process (214), attribute data (218) is received from multiple runs of the manufacturing process for each of the DTws (306), a digital replica (236) is run (308) to update the DTws to replicate the multiple instances of the components that were run, and data analysis is performed based on the updated DTws (310).

[0095] Optionally, in one of the methods (300) described in the preceding paragraphs, the data analysis is performed on updated DTws from the execution of a digital replica (236) such that multiple instances of the component are replicated, and the modification of the product or manufacturing process (312) includes modifying the reliable data (228) of the DSM (226) based on the data analysis of multiple instances of the component.

[0096] Those skilled in the art, benefiting from the teachings presented in the above description and the associated figures, will be able to recall numerous modifications and other embodiments of the disclosure as specified herein. Therefore, it should be understood that the disclosure is not limited to the specific embodiments disclosed, and that modifications and other embodiments are also intended to be included in the accompanying claims. Furthermore, while the above description and the associated drawings illustrate exemplary embodiments in light of specific exemplary combinations of elements and / or functions, it should be recognized that various combinations of elements and / or functions may be provided by alternative embodiments, without departing from the accompanying claims. That is, for example, combinations of elements and / or functions different from those explicitly described above may be envisioned, as may be specified in part of the accompanying claims. Certain terms are used herein, but they are used in a general and explanatory sense only and are not intended to be limiting.

Claims

1. A device (400) for developing a product (202), A memory (404) configured to store computer-readable program code (406), and The device includes a processing circuit (402) configured to access the memory, and the processing circuit executes computer-readable program code to provide at least the following to the device: To generate a digital system model (DSM) (226) of reliable data (228) describing the manufacturing process for producing the product, including the product itself, as well as machinery (204), materials (206), methods (208), equipment (210), and manpower (212) used in the manufacturing process (214). 304) generating a digital twin (DTw) (232) of an example (234) of the components (230) of the manufacturing process from the DSM, wherein the DTw includes a digital replica (236) of the example of the components of the manufacturing process, which includes one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process. Receiving attribute data (218) measured when the manufacturing process was performed to produce the product (306), wherein the attribute data is of an attribute of process characteristics that is exposed to one or more causes of variation among the machinery, materials, methods, equipment, or manpower that cause variations in product characteristics, Using the input attribute data, execute the digital replica (236) (308), thereby updating the DTw to replicate the instance of the component (230) of the executed manufacturing process. Based on the updated DTw, perform data analysis of the manufacturing process (310), and An apparatus (400) configured to perform the modification of the product or the manufacturing process (312) based on the data analysis, wherein the modification reduces the variation in one or more of the product characteristics.

2. The components (230) of the manufacturing process (214) are a control plan that further specifies the process characteristics (238), a control method (240) for controlling the causes of the variability to which the process characteristics are exposed, and a verification and evaluation method (242) for the attributes of the process characteristics. The apparatus (400) according to claim 1, wherein the apparatus which is authorized to generate (304) the DTw (232) of the example (234) of the component includes the apparatus which is authorized to generate (314) the DTw of the example of the control plan, and the apparatus which is authorized to modify (312) the manufacturing process includes the apparatus which is authorized to modify (316) one or more of the control methods.

3. Furthermore, the apparatus (400) according to claim 1 or 2, wherein the apparatus is made to perform the task of establishing a digital thread (244) from the DTw (232) which leads to the reliable data (228) of the DSM (226) and the attribute data (218) measured when the manufacturing process (214) was performed.

4. The device that is made to perform the task of generating the DSM (226) (302) is Accessing (320) the metamodel (246) of the components of the manufacturing process (214) which specifies a logical schema (248) for the components (230), and The apparatus (400) according to claim 1 or 2, comprising the apparatus which can be made to encode (322) the reliable data (228) for the components according to the logical schema.

5. The components (230) of the manufacturing process (214) include one or more of the machines (204) used in the manufacturing process, and the one or more of the machines are represented as a computer model (250) which is stored in a catalog (252) and is separated and distinguished from the DSM (226) and the DTw (232). The apparatus (400) according to claim 1 or 2, wherein the apparatus capable of generating the DSM (302) includes the apparatus capable of generating the DSM that calls out the computer model (324), and the apparatus capable of generating the DTw (304) includes the apparatus capable of generating an example of the computer model stored in the catalog (326).

6. The components (230) of the manufacturing process (214) include one or more of the methods used in the manufacturing process, and the one or more of the methods are represented as algorithms (254) which are stored in a catalog and are separated and distinguished from the DSM (226) and the DTw (232). The apparatus (400) according to claim 1 or 2, wherein the apparatus capable of generating the DSM (302) includes the apparatus capable of generating the DSM that calls out the method (328), and the apparatus capable of generating the DTw (304) includes the apparatus capable of generating an example of the algorithm stored in the catalog (330).

7. The components (230) of the manufacturing process (214) include one or more of the machines (204) and methods (208) used in the manufacturing process, and the apparatus that is made to perform the production (304) of the DTw (232) is To specify one or more of the machines and methods from the DSM (226) (332), and The apparatus includes a device that can perform the task of generating the DTw (334), which includes examples of computer models (250) and algorithms (254) that represent one or more of the machines and methods, The apparatus which is made to generate (334) the DTw (232) further includes the apparatus which is made to configure (336) the DTw, wherein the examples of the computer model (250) and the algorithm (254) are connected to a source of attribute data (218), The apparatus (400) according to claim 1 or 2, wherein the apparatus which is made to execute (308) the digital replica (236) includes the apparatus which is made to execute (338) the computer model (250) and the algorithm (254) using input of the attribute data (218) from the source of the attribute data.

8. The apparatus that is authorized to generate the DTw (232) (304) includes the apparatus that is authorized to generate (340) digital twins (DTws) of multiple instances of the components (230) of the manufacturing process (214), The attribute data (218) is received from multiple runs of the manufacturing process for each of the DTws (306), the digital replica (236) is run (308) to update the DTws to replicate the multiple instances of the components that have been run, and the data analysis is performed based on the updated DTws (310). The data analysis is performed based on the DTws updated from the execution of the digital replica (236) such that it replicates the multiple instances of the component, The apparatus (400) according to claim 1 or 2, which is configured to perform the modification (312) of the product or the manufacturing process, and which is configured to perform the modification (228) of the DSM (226) based on the data analysis of the plurality of cases of the component.

9. A method (300) for developing a product (202), To generate a digital system model (DSM) (226) of reliable data (228) describing the manufacturing process for producing the product, including the product itself, as well as machinery (204), materials (206), methods (208), equipment (210), and manpower (212) used in the manufacturing process (214). 304) generating a digital twin (DTw) (232) of an example (234) of the components (230) of the manufacturing process from the DSM, wherein the DTw includes a digital replica (236) of the example of the components of the manufacturing process, which includes one or more of the machinery, materials, methods, equipment, or manpower used in the manufacturing process. Receiving attribute data (218) measured when the manufacturing process was performed to produce the product (306), wherein the attribute data is of an attribute of process characteristics that is exposed to one or more causes of variation among the machinery, materials, methods, equipment, or manpower that cause variations in product characteristics, Executing the digital replica (308) using the attribute data input, thereby updating the DTw to replicate the instances of the components of the executed manufacturing process, Based on the updated DTw, perform data analysis of the manufacturing process (310), Based on the data analysis, modify the product or the manufacturing process (312), which includes modifying the product or the manufacturing process to reduce the variation in one or more of the product characteristics. Furthermore, a method (300) comprising establishing a digital thread (244) from the DTw (232) leading to the reliable data (228) of the DSM (226) and the attribute data (218) measured when the manufacturing process (214) was performed.

10. The components (230) of the manufacturing process (214) are a control plan that further specifies the process characteristics (238), a control method (240) for controlling the causes of the variability to which the process characteristics are exposed, and a verification and evaluation method (242) for the attributes of the process characteristics. The method (300) of claim 9, wherein generating the DTw (232) of the example (234) of the component (304) includes generating the DTw of the example of the control plan (314), and modifying the manufacturing process (312) includes modifying one or more of the control methods (316).

11. The process of generating the aforementioned DSM (226) (302) is as follows: Accessing (320) the metamodel (246) of the component (230) of the manufacturing process (214) which specifies a logical schema (248) for the component, and The method (300) of claim 9 or 10, comprising encoding (322) the reliable data (228) for the components according to the logical schema.

12. The components (230) of the manufacturing process (214) include one or more of the machines (204) used in the manufacturing process, and the one or more of the machines are represented as a computer model (250) which is stored in a catalog (252) and is separated and distinguished from the DSM (226) and the DTw (232). The method according to claim 9 or 10 (300), wherein generating the DSM (302) includes generating the DSM that calls out the computer model (324), and generating the DTw (304) includes generating an example of the computer model stored in the catalog (326).

13. The components (230) of the manufacturing process (214) include one or more of the methods used in the manufacturing process, and the one or more of the methods are represented as algorithms (254) which are stored in a catalog and are separated and distinguished from the DSM (226) and the DTw (232). The method (300) of claim 9 or 10, wherein generating the DSM (302) includes generating the DSM that calls out the method (328), and generating the DTw (304) includes generating an example of the algorithm stored in the catalog (330).

14. The components (230) of the manufacturing process (214) include one or more of the machinery (204) and the method (208) used in the manufacturing process, and the production of the DTw (232) (304) is, To specify one or more of the machines and methods from the DSM (226) (332), and The process includes generating the DTw (334), which includes examples of computer models (250) and algorithms (254) that represent one or more of the machines and methods, Generating the DTw (232) (334) further includes configuring the DTw (336) such that the computer model (250) and the example of the algorithm (254) are connected to a source of attribute data (218), The method according to claim 9 or 10 (300), wherein executing the digital replica (236) (308) includes executing the computer model (250) and the algorithm (254) (338) using input of the attribute data (218) from the source of the attribute data.

15. Generating the DTw (232) (304) includes generating (340) digital twins (DTws) of multiple instances of the components (230) of the manufacturing process (214), The attribute data (218) is received from multiple runs of the manufacturing process for each of the DTws (306), the digital replica (236) is run (308) to update the DTws to replicate the multiple instances of the components that have been run, and the data analysis is performed based on the updated DTws (310). The data analysis is performed based on the DTws updated from the execution of the digital replica (236) such that it replicates the multiple instances of the component, The method (300) of claim 9 or 10, wherein modifying the product or the manufacturing process (312) includes modifying the reliable data (228) of the DSM (226) based on the data analysis of the plurality of cases of the component.

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