Patent Description:
Lean manufacturing is a systematic method for waste minimization within a manufacturing system while maintaining high productivity. Lean manufacturing takes into account the waste created through unbalanced manufacturing processes, and seeks to identify and focus on products and services that add value to the process. Successful lean manufacturing requires development of tools that assist in the identification and steady elimination of waste. With the growing demands placed on automation in the manufacturing floor to improve yields, methods to improve communication between the machine and supervising operators are increasingly valuable. Thus, a need exists for an open architecture system that enables quick introduction of new capabilities, increases safety, grows functionality, and reduces defective manufactured articles - without large expense or retrofitting in manufacturing settings.

<NPL>, describes cure and fabrication monitoring of composite materials with fibre-optic sensors.

<NPL>, describes a miniaturized flexible dielectric and temperature sensor.

<CIT> describes a system, method and computer-readable storage medium for inspecting a composite part during manufacture and, more particularly, detecting an in-process anomaly displaying a representation of a digital part model including an indication of the in-process anomaly to facilitate the anomaly being addressed in a more timely manner.

<CIT> describes systems, methods, apparatus, and articles of manufacture for automated composite layup quality assurance. A method of automated composite layup inspection is described comprising: receiving, by a processing device, information describing a three-dimensional point cloud of a top surface of a composite layup, the top surface comprising a region of one or more courses of composite layup material, each course comprising a plurality of composite material tapes; determining, by the processing device and based on the three dimensional point cloud, and at least one algorithm, a presence and location of one or more flaws on the top surface; and evaluating, by the processing device and based on the one or more flaws on the top surface, whether the composite layup should be at least one of: (i) accepted as is; (ii) repaired to resolve the one or more flaws; and (iii) rejected.

In the following, aspects not falling under the scope of the claims are for illustrational purposes only. The present disclosure relates to the field of composite materials manufacturing systems, methods, and apparatuses; even more particularly, to systems, methods, and apparatuses for automated quality checks for manufactured composite materials.

According to a first aspect, there is provided an automated inspection system as defined in claim <NUM>.

In certain aspects, the system further comprises a state manager operatively coupled with the core platform to determine a defect associated with the one or more characteristics.

In certain aspects, the state manager is configured to determine whether a first defect exists in the composite article based on the first measurement.

In certain aspects, the state manager is configured to determine whether a second defect exists in the composite article based on the second measurement.

In certain aspects, the state manager is configured to: identify a value corresponding to the one or more characteristics associated with the defect based on the first or second measurement; and calculate a score representing the degree of the defect of the manufactured article based on the identified value.

In certain aspects, the core platform further configured to: receive data regarding a third measurement of the one or more characteristics after performing a trim operation on the composite article; determine whether a third defect exists in the composite article based on the third measurement; and generate an alert in response to a determination that a third defect exists in the composite article.

In certain aspects, the system further comprises a human machine interface operatively coupled with the core platform to provide an interface between an operator and the system.

In certain aspects, the core platform is configured to transmit the alert to the human machine interface, the alert comprising one of an audible or visual alert presented by the human machine interface.

In certain aspects, the system further comprises an actuation system operatively coupled with the core platform to implement the manufacturing process based on instruction from the core platform, wherein the core platform transmits the alert and information regarding the first or second defect to the actuation system to adjust an operating value of a manufacturing process of the system.

In certain aspects, the operating value comprises one of a speed of the manufacturing process, a temperature of the curing stage, and a position of the composite article.

In certain aspects, the sensor system is operatively coupled with one or more of a non-contact ultrasound sensor, a laser sensor, an impedance sensor, an infrared sensor, or a heat sensor.

In certain aspects, the sensor system monitors the one or more characteristics by two or more sensors of the sensor system to determine the first or second defect.

In certain aspects, the characteristic comprises one of a density, a temperature, a chemical composition, and a thickness associated with the composite article.

According to a second aspect, there is provided a method as defined in claim <NUM>.

In certain aspects, the method further comprises: determining, by the core platform, a defect value associated with the first or second characteristic; comparing the defect value to a plurality of defect values; and designating the manufactured article as containing a defect based on the comparison.

In certain aspects, the method further comprises: identifying, by the core platform, a stage at which the defect appears; and adjusting an operating value of a manufacturing process based on the identification.

In certain aspects, the first or second characteristic comprises one of a density, a temperature, a chemical composition, and a thickness associated with the composite article.

In certain aspects, the method further comprises transmitting the alert to a human machine interface operatively coupled with the core platform.

In certain aspects, the alert comprises video or audio identifying the defect.

According to a third aspect, there is provided a non-transitory machine-readable storage device as defined in claim <NUM>.

These and other advantages of the present disclosure may be readily understood with the reference to the following specifications and attached drawings wherein:.

Preferred embodiments of the present disclosure may be described hereinbelow with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail because they may obscure the disclosure in unnecessary detail. For this disclosure, the following terms and definitions shall apply.

The terms "circuits" and "circuitry" refer to physical electronic components (i.e. hardware) and any software and/or firmware ("code") which may configure the hardware, be executed by the hardware, and or otherwise be associated with the hardware. As used herein, for example, a particular processor and memory may comprise a first "circuit" when executing a first set of one or more lines of code and may comprise a second "circuit" when executing a second set of one or more lines of code.

The terms "communicate" and "communicating" refer to (<NUM>) transmitting, or otherwise conveying, data from a source to a destination, and/or (<NUM>) delivering data to a communications medium, system, channel, network, device, wire, cable, fiber, circuit, and/or link to be conveyed to a destination. The term "database" as used herein means an organized body of related data, regardless of the manner in which the data or the organized body thereof is represented. For example, the organized body of related data may be in the form of one or more of a table, a map, a grid, a packet, a datagram, a frame, a file, an e-mail, a message, a document, a report, a list, or data presented in any other form.

The term "in-situ" refers to actions performed during manufacturing operations such that the ongoing process is not interrupted. In this manner, for example, the testing phase is performed without taking the item under test from the manufacturing flow.

The term "and/or" means any one or more of the items in the list joined by "and/or".

The terms "about" and "approximately," when used to modify or describe a value (or range of values), mean reasonably close to that value or range of values. Thus, the embodiments described herein are not limited to only the recited values and ranges of values, but rather should include reasonably workable deviations. As utilized herein, circuitry or a device is "operable" to perform a function whenever the circuitry or device comprises the necessary hardware and code (if any is necessary) to perform the function, regardless of whether performance of the function is disabled, or not enabled (e.g., by a user-configurable setting, factory trim, etc.).

With the advent of the Internet of Things (IoT), whereby computing devices are embedded into everyday objects, the capability of sensing, processing, and communicating task-to-task details has become ubiquitous. To ensure continuous improvement in lean manufacturing, development should focus on issues of data analytics. For example, a computing architecture and infrastructure capable of communicating with a plurality of information sources (e.g., sensors, databases, interfaces, etc.) and/or analyzing data (e.g., firmware, hardware, software, algorithms, etc.) is desirable, such that all data can be compiled into a centralized server and/or data storage. Further, with the proliferation of cheap, accurate sensors, the amount of data to parse to obtain meaningful information requires thoughtful consideration. Therefore, there is great potential to use sensors to obtain data from a variety of processes; however, significant hurdles remain before a complete solution is achieved.

Computing architecture and infrastructure capable of parsing large amounts of data to obtain meaningful information in the context of a variety of systems, for example, the state of an aircraft, the actions of a pilot within that state, and others have been researched and developed over the years. Hardware and software architecture have been developed to benefit manufacturing technologies. As an example, a system can be configured to utilize cameras, a core computer containing core operating principles, and a human machine interface (HMI) (e.g., a tablet or other computing device) to accept commands and/or share information with an operator. In this manner, data can be digitized, such that the manufacturing process checklist can be configured to check for defects (e.g., FOD) on composite manufactured articles. It is further considered that the principles and/or systems described herein will have wide applicability for data capture and analytics, in order to perform continuous improvement leading to increasing automation. Ultimately, the necessity of a manual inspection by a Level <NUM> nondestructive testing (NDT) technician may be eliminated through the methods and systems described herein.

In order to validate a quality measure for one or more characteristics of the composite article, the systems and methods described herein are configured to capture sensor data from a variety of sources and consume and analyze the data in real-time. Therefore, the quality of the potential output can be assessed at multiple stages before a final product is delivered. Advantageously, an error in the manufacturing process/equipment and/or a defect in the manufactured article can be identified at any number of stages and can initiate a response (e.g., an alert, adjustment in the process, etc.) in order to bolster quality in the manufacturing process and produced articles at the earliest possible level to avoid compounding defects in a lean manufacturing environment.

Disclosed herein is a system configured to, inter alia, provide automated quality checks for manufactured composite articles. The system can be configured to perform, for example, a five-step process, which includes in-situ inspection steps at the layup stage, the cure stage, and the trim stage. In particular, the layup stage can perform, for example, a non-contact ultrasound ("NCU") inspection, the cure stage can perform an in-situ validation, whereas the trim stage can employ in-situ non-destructive inspection ("NDI"). Thus, at each of these stages, a separate inspection protocol can be employed. Data to identify defects after a particular stage of the manufacturing process can thus be generated, data that can be used to alert and/or inform an operator or system manager, as well as take action to correct such defects (e.g., adjust a parameter of the manufacturing process).

Described herein is a hardware and/or software architecture which can enable data from multiple sensors to be collected, provided to a core computing platform, analyzed by the core platform, and an alert and/or adjustment can be made in response to an identified issue. In addition to the base architecture, the system can employ one or more subsystems, including a plurality of sensors, to quantify characteristic values associated with the composite article at multiple stages of the manufacturing process.

<FIG> shows a checklist/inspection automation system <NUM> that includes a computer <NUM> (e.g., a processor, microcontroller, etc.) having one or more of a core platform <NUM>, a sensor system <NUM>, and an actuation system <NUM>. The system <NUM> involves the centralization of data via the hardware and software architecture. Additionally, an inspector can be employed as truth data, such as stored in a data storage medium (e.g., integrated with the core platform, remote from the core platform and linked via a network, etc.). In this manner, the quality from a computer based inspection checklist may be at a level similar to or greater than a human inspector might achieve. In some examples, the system <NUM> can be validated by a Turing test; in other words, in identifying issues (e.g., defects) in the composite article, a computer may achieve results similar to that of a human.

An example system architecture for an inspection automation system <NUM> in accordance with one aspect is shown in <FIG>. As illustrated in <FIG>, a core platform <NUM> may operate as a central subsystem that connects the other subsystems via one or more interfaces. The subsystems may communicate with one another through software and/or hardware interfaces using wired and/or wireless communication protocols and hardware. For example, <FIG> illustrates an example flow of information (e.g., data) between the various subsystems.

The core platform <NUM> is operatively coupled with each of the plurality of subsystems, such as those listed below. Each of the plurality of subsystems of the inspection automation system <NUM> may be modular, such that the entire inspection automation system <NUM> can be substantially implemented on various manufacturing systems without substantial modifications. For example, the core platform <NUM> may be configured to communicate with multiple subsystems via one or more software and/or hardware interfaces, which may be a combination of hardware (e.g., permanent or removable connectors) and software. In certain aspects, however, the inspection automation system <NUM> may alternatively be integrated with a particular manufacturing system, thereby directly employing all sensors and indicators in such a system. For example, the inspection automation system <NUM>, or components thereof, may be integrated into the manufacturing machinery at various stages of production during both design and manufacturing of a composite article.

The plurality of subsystems may include, for example, a sensor system <NUM>, an actuation system <NUM>, and a human machine interface ("HMI") system <NUM>, each of which may be operatively coupled with the core platform <NUM>. In certain aspects, the need for a sensor system <NUM> may be mitigated or obviated via use of an integrated manufacturing monitoring system and/or data from a computer control associated with the monitoring system. As can be expected, however, such integration would likely require a degree of modification and/or understanding of the manufacturing system, which may increase complexity. The inspection automation system <NUM> and/or core platform <NUM> may also comprise, or be operatively coupled to, a one or more databases <NUM> and/or a communication system <NUM> (e.g., via communications interface to DDS <NUM>), as provided in <FIG>.

The modular configuration further enables the operator to customize use of subsystems and/or modules or to add/install additional subsystems or modules. For example, when the inspection automation system <NUM> is configured to provide information to the operator via the HMI <NUM> (i.e., without the ability to control the manufacturing process), the actuation system <NUM> may be removed or disabled. Accordingly, depending on the configuration, the inspection automation system <NUM> may be configured with fewer or additional modules, components, or systems within the scope of the appended claims.

<FIG> illustrates an architecture diagram of an example inspection automation system <NUM>. A core platform <NUM> may operate within computer <NUM> and provide, or otherwise serve as, middleware that can be made specific to a particular manufacturing system or process, or configuration through an initial transition and setup phase. In other words, the core platform <NUM> may provide an operating system that provides instructions and/or information to a set of operational applications (e.g., a procedure manager <NUM>, a state manager <NUM>, a trend manager <NUM>, etc.) and communicate signals to one or more of a set of hardware interfaces (e.g., an HMI interface <NUM>, a sensor interface <NUM>), while collecting and logging the data necessary to enable those applications, such as via a Data Distribution Services (DDS). For example, DDS communication systems have the ability to regain performance levels after the introduction or removal of a DDS linked component. Such a modular system architecture is based on distributed intelligence and decentralized control of communication to provide reconfiguration of industrial robots in manufacturing facilities. Thus, the result is part of a system architecture that focuses on maximizing the efficient use of mobile industrial robots during production runs, such as manufacturing of composite articles as described herein.

As shown, the core platform <NUM> serves as the primary autonomous agent and decision-maker, which synthesizes inputs from the sensor system <NUM> and HMI system <NUM> with its acquired knowledge base to determine the overall system state. The core platform <NUM> may process inputs from the various sensor suites and aggregate the resultant information into an understanding of current state of the manufacturing process and/or the composite article. The resultant information may be compared against a composite specific file. For example, the procedure manager <NUM> can provide a breakdown of the various steps and/or stages in the specific manufacturing process. The state manager <NUM> can identify the presence of defects, such as by comparison of stored data with data acquired via one or more sensors <NUM> (e.g., sensors 110a-d), and generate an estimation of the state of the composite article at various stages of the manufacturing process.

Additionally or alternatively, the trend manager <NUM> can capture and analyze data from the sensor system <NUM> to build a library or matrix of values associated with characteristics of a composite article that define a measure of quality. For example, the trend manager application <NUM> provides trend analysis developed using machine learning based on, for example, data acquired via the sensor system <NUM>. For example, the trend manager <NUM> can "learn" which data correspond to a characteristic of the manufacturing process or article (e.g., a measure of density can represent a bubble/void in a resin application; appearance of overlapping or gaps in a layered composite can represent a flawed process; etc.), and identify data from the sensor system <NUM> that corresponds to a defect. In certain aspects, the trend manager application <NUM> may supply data to, or otherwise trigger, the state manager <NUM> to identify a defective state. For example, if the trend manager application <NUM> detects an undesirable trend, the trend may be flagged as an anomaly and reported to the state manager <NUM>.

The core platform <NUM> can host various software processes that track the composite article through the stages of manufacture, as well as any modules for trend analytics (e.g., the trend manager <NUM>, configured to predict and/or aid in identification of defects) and machine learning routines (e.g., deep learning, application of neural networks, etc.). The inspection automation system <NUM> may further generate a log of a given manufacturing stage and/or process for later analysis and/or building a trend database, which may be used to provide data for the trend manager <NUM>. The logs may be used in connection with, for example, quality assurance analysis, system analysis, etc..

The core platform <NUM> outputs may be used to provide messages to the HMI <NUM>. The messages may indicate, for example, checklist progress, identification of defects, warnings to adjust a process, etc. The information can be transmitted to, or to HMI <NUM> via an HMI interface <NUM>. The resultant state knowledge and associated recommendations can be passed to a human operator via the HMI <NUM> or, in certain aspects, to the actuation application <NUM> for correction via one or more robotic systems <NUM> to enable an autonomous response. In some examples, an operator via HMI <NUM> can initiate one or more actions within the system <NUM>, such as to control the robotic system <NUM> via the actuation system <NUM>.

<FIG> shows a system level architecture for a manufacturing quality control scheme. As shown, the core platform <NUM> serves as the central subsystem, or interface, of the inspection automation system <NUM>, connecting and controlling the remaining subsystems (e.g., as individual applications) in an open architecture. The remaining subsystems include, for instance, the HMI <NUM>, the actuation systems <NUM> (e.g., to provide autonomous operation via robotic system(s) <NUM> when desired), the sensor system <NUM> (e.g., to provide data), and other subsystems. Thus, control of the inspection automation system <NUM> hardware may be provided via separate applications specific to a particular piece of hardware.

In certain aspects, the inspection automation system <NUM> and/or core platform <NUM> may employ a computer bus and application (e.g., as an interface) that facilitates discovery of a hardware component of a subsystem within the inspection automation system <NUM> without the need for physical device configuration or user intervention in resolving resource conflicts. Such a configuration may be referred to as "plug and play. " Thus, an operator may readily add or remove systems or subsystems (e.g., as modules) to the system <NUM> via the core platform <NUM> without requiring substantive modification or integration efforts.

In this manner, the core platform <NUM> receives data regarding the manufacturing process and/or the article being manufactured, enabling the core platform <NUM> to develop a complete understanding of the system. The data may be populated by the sensor system <NUM>, which provides information about the manufacturing system in use (e.g., operational stages, state of the composite article, etc.), and other subsystems or sensors. The information can be stored in a data store <NUM>, populated and adjusted to a specific manufacturing process and/or article. For example, data captured during trend analysis can be stored and updated as needed.

The core platform <NUM> can combine information with data from a set of internal state sensors, which also improve redundancy and system robustness, thereby allowing the inspection automation system <NUM> to generate a highly accurate estimate of the composite article's state and system statuses, and to identify deviation from expected results. During manufacturing operations, the data store <NUM> is dynamically updated with real-time data gathered by, inter alia, the inspection automation system's <NUM>, the sensor system <NUM>, the HMI <NUM>, as well as the state manager <NUM>. Once the data store <NUM> for a given procedure/product is populated, the data store <NUM> can be retained in a library or matrix and used for other similar procedures/products for which the inspection automation system <NUM> is monitoring.

The state manager <NUM> employs machine-learning techniques to monitor a state of the manufacturing process and the composite article, and to classify sensor inputs in order to detect the presence of non-normal situations (e.g., a fault in the machine, a defect in the composite article, etc.), and to identify at which stage of the process the issue has occurred. The state manager <NUM> is configured to compare the sensed data against a set of thresholds defined in the procedure manager <NUM> and/or the trend manager <NUM> for the specific manufacturing process (e.g., size of a void, distance of a gap, etc.). The state manager <NUM> may also compare the sensed states against additional information available to the inspection automation system <NUM> (e.g., from an operator via the HMI <NUM>, from a networked information source, etc.) and generate alerts or other messages in response to meeting predetermined or dynamically determined thresholds (e.g., warning thresholds, etc.).

In the case when a defect or anomaly is detected, the procedure manager <NUM> executes one or more predetermined or dynamic checklists, procedures, and actions in order to maintain consistent quality in the manufactured composite articles. Notably, if a departure from expected performance is observed, the operator can be alerted to the presence of a defect, thereby mitigating or avoiding follow-on damaged products. For example, if a particular material, process, etc., is susceptible to a particular type of error (e.g., sensitivity to environmental conditions, temperature, etc.), the inspection automation system <NUM> can identify the presence of such conditions and generate warning (e.g., via HMI <NUM>) and/or commands (e.g., via actuation system <NUM>) to mitigate such events.

The various information pertaining to the operational applications are communicated between the actuation system <NUM>, the sensor system <NUM>, the HMI <NUM>, and other subsystems via, for example, the HMI interface <NUM>, and sensor interface <NUM>. Additionally, the HMI <NUM> contains an interface to the DDS <NUM>, the sensor system <NUM> includes an interface to the DDS <NUM>, and the actuation system <NUM> includes a robot application interface <NUM>, to communicate with the core platform <NUM> via the DDS.

As illustrated, the sensor system <NUM> may comprise a sensor application <NUM> to collect and analyze data from one or more sensors 110a-d, such as optical coordinate measuring machine 110a (for visual inspection), thermal camera 110b, non-contact ultrasound (NCU) 110c and/or ultrasound inspection 110d (used for acoustic inspection), and/or other sensors as needed to identify defects in the process, machines, and/or the composite article. The sensor system <NUM> may employ a monocular or stereovision system, possibly including motion capture markers, to continuously monitor the state of the process by imaging the composite article, the and/or reading what is displayed on the cockpit instruments.

In certain aspects, by comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. Using a stereovision system and/or markers also provides sensing that may prevent collisions between robotic components, etc..

When desired, an actuation system <NUM> executes the actions commanded via the core platform <NUM> to guide the composite article through the manufacturing operation. The actuation system <NUM> executes the actions commanded by the core platform <NUM> to aid in monitoring the progress of a manufactured article and to identify defects therein, including at which stage the defect appeared. As illustrated in <FIG>, for example, the actuation system <NUM> may receive actuation commands and configuration data from the core platform <NUM>, while sending to the core platform <NUM> information such as feedback regarding the actuation system's <NUM> actions.

The HMI <NUM> provides a control and communication interface for the operator. The HMI <NUM> is configurable to operate as a process manager that enables the operator to direct the inspection automation system <NUM>. The HMI <NUM> may include a human-machine interface, which may be based on a touch screen graphical user interface ("GUI") and/or speech-recognition systems. The HMI <NUM> may employ, for example, a tablet computer, a laptop computer, a smart phone <NUM>, or combination thereof. A voice application <NUM> can receive information from an operator via speakers <NUM>. The HMI <NUM> may be located remotely from the manufacturing system or, in certain aspects, employed as an integrated display with the manufacturing system (e.g., an existing display).

As illustrated in <FIG> and <FIG>, the HMI <NUM> may employ a tablet based GUI and an HMI application <NUM> configured to send and/or receive information between the system <NUM> and an operator. An objective of the HMI <NUM> is to enable the operator to interact with the core platform <NUM> to make informed decisions during the manufacturing process (e.g., modify or halt operations based on a detected defect). As illustrated in <FIG>, for example, the HMI <NUM> may receive status information from a subsystem via the core platform <NUM>, while sending to the core platform <NUM> mode commands generated by the HMI <NUM> or input by the operator. The operator may be remote (e.g., monitoring from a consolidated area) or present at the manufacturing device. Thus, in certain aspects, the HMI system <NUM> may be remotely facilitated over a network via a communication system <NUM>.

The HMI <NUM> can display the current state of inspection automation system <NUM> (i.e. the current stage and/or the state of the composite article) as well as which operational applications are currently installed, which operational applications are running and, if they are active, which actions the actuation system <NUM> is taking. In some examples, the HMI <NUM> provides an intuitive display and interface that includes checklist verification and/or alerts from the core platform <NUM>, including predictions of the manufacturing stage and composite article state, such as defect prognosis and deviation alerts. Thus, when the operator views the HMI <NUM>, the operator may review and monitor checklist items, as well as review any alerts. The HMI system <NUM> may give visual and auditory alerts to direct the operator's attention to checklist items.

The state manager <NUM> collects, determines, or otherwise perceives the real-time state of the composite article. As noted above, the state manager <NUM> may perceive the real-time state through, inter alia, a direct connection (e.g., integral with or otherwise hardwired) to the system <NUM>, or via sensor system <NUM>. When a sensor system <NUM> is used, the state manager <NUM> may include a dedicated controller (e.g., processor) or share the controller (e.g., sensor application <NUM>) of the sensor system <NUM>.

The data perceived by the sensor system <NUM> may be encoded and provided to the core platform <NUM> in real-time. The open architecture of the core platform <NUM> enables the incorporation of additional data received via a data bus or DDS to augment the situation data generated by the sensor system <NUM>. As illustrated in <FIG>, for example, the state manager <NUM> and/or the sensor system <NUM> may receive commands and configuration data from the core platform <NUM>, while sending to the core platform <NUM> status and situation information (e.g., defect data) gathered by the sensor system <NUM> or otherwise collected by the state manager <NUM>.

In some examples, the state manager <NUM> applies one or more machine-learning algorithms to the captured data to determine a state of the manufacturing process and/or state of the composite article. By supplementing a physics-based approach to data analysis with machine-learning techniques, the system <NUM> advances the state of the art without relying on the advancement of computational complexity. Additionally, computational based approaches have a tendency to make significant assumptions within the physical models, which may influence results. A neural network based approach, as described herein, will learn to mimic the inspection habits of a human operator, and place weighted values on characteristics of interest to a human inspector.

In particular, artificial neural networks (ANNs) are computing systems inspired by the biological neural networks that constitute animal brains. Such systems "learn" different tasks by observing examples. In some cases, a system employing an ANN is not programmed for a specific task, but the system is able to recognize patterns and employ learned techniques in later tasks. For example, in image recognition, the system learns to identify images that contain a particular object by analyzing example images containing the object, and performing analytics to identify the object in other images. ANN systems have been employed to replace the limited scope of traditional computer algorithms using rule-based programming.

The state manager <NUM> gathers and/or generates a knowledge base necessary to enable the inspection automation system <NUM> to determine information specific to the composite article being manufactured. This includes knowledge of manufacturing performance characteristics, composite material characteristics, checklists, and procedures that define quality in a manufactured composite article. The data may be derived from a combination of data (e.g., from manuals, operator input, etc.) and data acquired during manufacturing (e.g., via sensors 110a-d), which supports machine learning and trend analysis. Machine learning enables the knowledge acquisition process to be performed efficiently and quickly.

The process specific information may be gathered during one or more training periods using (e.g., in order to build a base of knowledge for the machine-learning algorithms, etc.) as well as through direct monitoring of manufacturing operations. The output of this process is built into a library or matrix of historical data, including expected performance and potential issues specific to a particular process and/or article, which can be stored in the data store <NUM>, the state manager <NUM>, and/or the trend manager <NUM>, which are described above with regard to the core platform <NUM>. Contained in this data store <NUM> may be operational procedures, available systems and their designs, and all other information necessary for effective operation of the manufacturing system.

The system is configured to perform an example five-step process, which includes in-situ inspection steps at the layup stage, the cure stage, and the trim stage. In particular, the layup stage performs a NCU inspection; the cure stage performs an in-situ validation, whereas the trim stage employs in-situ NDI. Thus, at each of these stages, a separate inspection protocol is employed. This generates data to identify defects after a particular stage of the manufacturing process, data that can be used to alert and/or inform an operator or system manager, as well as take action to correct such defects (e.g., adjust a parameter of the manufacturing process).

In some examples, the state manager <NUM> analyzes data of characteristics corresponding to integrity of a composite article at different stages of the process. For instance, after a layup process is performed, an in-situ non-contact ultrasound can be performed. The automated defect inspection can be applied to the material to identify wrinkles, folds, foreign objects, bubbles, voids, etc. Information regarding the inspection can be provided to the core platform for analysis, tracking, display for a human operator, etc..

Additionally or alternatively, data can be analyzed after curing the composite article. For instance, the after the composite article is heated and cured, an in-situ validation process is performed to determine is a defect is present (e.g., wrinkles, folds, foreign objects, bubbles, voids, etc.). If a defect is identified, at either stage an alert can be sent and/or information can be sent to an automated manufacturing tool.

Additionally or alternatively, the defect value associated with the characteristic can be compared to a plurality of defect values (e.g., one or more threshold values stored in data store <NUM>). For example, the stored values can be stored in a library or matrix, and learned and/or updated via a trend analysis manager (e.g., trend manager <NUM>). If a defect is identified in the composite article (e.g., via the state manager <NUM>), an alert can be presented to a human operator (e.g., via HMI <NUM>) and/or presented to an actuation system (e.g., actuation system <NUM>) to adjust an operation of the manufacturing process. In some examples, the state manager 136designates the manufactured article as containing a defect and can be singled out for further inspection, destruction, etc..

Thus, the systems and methods described herein are configured to capture sensor data from a variety of sources, consume and analyze the data in real-time to validate a quality measure for one or more characteristics of the composite article. Therefore, the quality of the potential output can be assessed at multiple stages before a final product is delivered. This is achieved by sensing characteristics of the manufacturing process and/or composite article, analyzing the data via machine learning techniques, identifying a defect and/or stage at which the defect arose, and communicating the information to an HMI and/or a robotic actuation system to mitigate an issue prior to proliferation of the issue.

<FIG> illustrates an example process overview. The process includes five main stages: <NUM>) raw material acquisition; <NUM>) material layup application; <NUM>) curing the material; <NUM>) trimming a cured article; and <NUM>) final NDI, which include a final inspection. Each process contains one or more sub-processes.

As shown in the example of <FIG>, the conventional process is improved upon by incorporating an automated inspection step in-situ at various stages to capture and analyze data to determine if defects are being generated and at which stage. As shown, and described in detail, infra, each stage can be individually analyzed, including by one or more sensors and/or machine intelligence algorithms, to identify defects during the manufacturing process. By contrast, conventional processes relied on a single inspection step after the composite article was completed. At that stage, the article itself would be scrapped. Further, if the defect was introduced at an early stage in the process, even as a defect on the first such damaged article is discovered, any number of trailing articles could have been impacted by the same defect before the issue can be corrected.

By use of the presently disclosed system, early detection and analysis of defects, and the stage in which they occur, is achieved. In turn, defects to the manufacturing process can be addressed before multiple articles are affected. Such a response will limit the amount of raw materials needed, limit downtime due to identification of problems within the manufacturing process, and reduce the number of discarded articles that have completed the manufacturing process, for instance. Further, by employing automated inspections throughout the process, manual inspections upon completion of the article can be limited or eliminated, which will help decrease cycle times and improve quality control.

<FIG> illustrates another example process overview for manufacturing a composite article. In the example of <FIG>, the raw material stage is represented as processes <NUM>-<NUM>, the layup stage is represented as processes <NUM>-<NUM> stage is represented as processes <NUM>-<NUM> stage is represented as process <NUM>, and the final stage is represented as processes <NUM>-<NUM>. The in-situ inspection process has yet to be incorporated.

As shown in the example of <FIG>, the raw material stage may include five steps of employing a manual stringer layup and a freezer, which may be used to cool and extend the life of the raw material. For example, stringers and skin structures cooperate to provide flexural and torsional stiffness to various sections of a product (e.g., a skin of an aircraft). The layup stage may include three steps of automated fiber placement (AFP) and stringer assembly, and a step of skin assembly. The cure stage includes an autoclave curing step and a demold step, with the cured article being placed in a router. At the trim stage, the composite article is trimmed and dimensioned to suit its particular purpose. During the final inspection stage, a nondestructive inspection (NDI) action is applied to the composite article, and then visually inspected. The wet layup molding process is performed, such that layers of reinforced fiber are combined with liquid resin to create a high quality laminate. This process can include placement of reinforcement material into and/or against a mold in different layers. The edges of the manufactured article are then sealed, and the final product may be weighted, packaged, and shipped.

<FIG> illustrates detail of an example raw material process. For instance, the raw material is prepared on a manual stringer to provide form and structure for the composite article. The result is then inspected using a non-contact ultrasound. Suitable non-contact ultrasound equipment is currently manufactured by the Ultran Group. As further illustration, <FIG> provide example elements of a non-contact ultrasound inspection. <FIG> shows a transmitter L, which provides ultrasound waves through a medium, to be received by a receiver R. Based on the characteristics of the transmitted and received waves, a determination can be made of one or more characteristics of the medium. <FIG> illustrates a graphical representation of the waves interacting with the medium, whereas <FIG> shows a result of NCU imaging. For example, the top portion <NUM> illustrates a <NUM> CFRP prepreg with trapped protective papers at <NUM>, whereas bottom portion <NUM> illustrates <NUM> CFRP prepreg with trapped protective film at <NUM>.

<FIG> illustrates detail of an example layup process. For instance, an automated defect inspection is performed to identify the presence of wrinkles, folds, and/or foreign object damage (FOD) in the composite.

<FIG> illustrate example elements of a laser implemented inspection system. In an example, the inspection system can be the LASERGUIDE system manufactured by Assembly Guidance Systems Inc. <FIG> illustrates a laser triangulation implementation on a manufactured article, where the laser inspection system performs calculation (e.g., executes algorithms) to identify an object. <FIG> illustrates the use of a laser implement profilometer (e.g., a measuring instrument to measure a profile of a surface) to identify a gap in a surface of an object via a laser line applied to the surface. <FIG> provides a graphical representation of the captured data, which indicates a negative value corresponding to the gap.

<FIG> and <FIG> illustrate detail of an example inspection process. In the example of <FIG>, the deficiencies of conventional systems are identified as only providing in process inspection for the quality of the raw material for manual layup, or if the composite raw material was laid down properly in accordance with Automated Fiber Placement (AFP). <FIG> illustrates the lack of inspections prior to the final inspection stage. AFP, also known as advanced fiber placement, is a method of manufacturing composite materials. For instance, composite materials are lightweight yet offer equivalent or greater strength than metals. Composite materials are increasingly used in industries where strength and weight are controlling factors, such as in aircraft manufacturing.

For example, an AFP process places materials on molds or mandrels to provide a structural frame on which to form composite layups, such as by applying a number of layered material on the frame. This technique provides precision and increased deposition rates. Automated fiber placement is used to manufacture complex structures that are impractical or impossible to manufacture otherwise.

<FIG> illustrates detail of an example cure process. For instance, the cure process can be performed in an autoclave or oven. For instance, curing a composite article is typically a heat-activated process taking place in an oven, autoclave, etc. The curing process can change the chemical properties of the material (e.g., resin) used to create the composite, resulting in a stronger finished product, yet having a customizable structure, as described herein.

<FIG> illustrates an example five-step process, which includes in-situ inspection steps at the layup stage, the cure stage, and the trim stage. In particular, the layup stage includes a NCU inspection, the cure stage performs an in-situ validation, whereas the trim stage includes in-situ NDI. Thus, at each of these stages, a separate inspection protocol is employed. This generates data to identify defects after a particular stage of the manufacturing process, data that can be used to alert and/or inform an operator or system manager, as well as take action to correct such defects (e.g., adjust a parameter of the manufacturing process).

In a conventional manual inspection, a human operator usually employs a testing platform (e.g., a CNC router) to move a sensor (e.g., NCU) over a finished composite article. The human operator moves the machine over the composite, scanning for troublesome regions, and makes a decision as to whether voids and/or bubbles in the composite article, as show in <FIG>, are within a threshold value and suitable for use. <FIG> illustrates example defects in a composite article. For example, material layers can overlap, leaving gaps in a layer exposing layers beneath. Such defects result in a weakened composite where, too often in conventional processes and systems, such defects would go unnoticed until the composite article has completed the manufacturing process. Thus, advantages of the presently disclosed system and process include early detection and attention to the manufacturing steps that caused such defects.

A robotic system can be used in the inspection process (i.e. versus manual inspection), however a human operator/inspector is typically still required due to the limited data capture and analysis generated during the manufacturing process. The present disclosure, by contrast, can eliminate the need for a human operator by improving in-situ inspection protocols, analyzing the data in near real-time and providing detection of defects and actionable information during the manufacturing process. Thus, the systems and processes described herein may obviate the need for human and/or manual inspection protocols, while saving resources and reducing cycle time.

<FIG> illustrates a flowchart of an example implementation of an inspection automation system (e.g., inspection automation system <NUM>). The computer <NUM> and core platform <NUM> may implement the method <NUM> of <FIG> by executing machine-readable instructions, such as stored on a non-transitory machine-readable storage device, such as data store <NUM>. Example implementations include an application specific integrated circuit and/or a programmable control circuit.

The method <NUM> measures (e.g., by sensors 110a-d) a first characteristic corresponding to integrity of a composite article after application of a plurality of layers of one or more raw materials at block <NUM>. For example, after a layup process is performed, an in-situ non-contact ultrasound can be performed. The automated defect inspection can be applied to the material to identify wrinkles, folds, foreign objects, bubbles, voids, etc. Information regarding the inspection can be provided to the core platform for analysis, tracking, display for a human operator, etc. If a defect is present, at this stage an alert can be sent to a human operator (e.g., via HMI <NUM>) and/or information can be sent to an automated manufacturing tool (e.g., actuation system <NUM>) to adjust an operation of the manufacturing process.

Additionally or alternatively, at block <NUM>, the method measures (e.g., by sensors 110a-d) a second characteristic corresponding to integrity of the composite article after curing the composite article. For example, the after the composite article is heated and cured, an in-situ validation process is performed to determine is a defect is present (e.g., wrinkles, folds, foreign objects, bubbles, voids, etc.). If the second characteristic reveals a defect, at this stage an alert can be sent and/or information can be sent to an automated manufacturing tool, as with block <NUM>.

In block <NUM>, the method determines (e.g., by core platform <NUM>) whether a defect value exists, as described herein. At block <NUM>, the defect value is compared to a plurality of defect values (e.g., one or more threshold values stored in data store <NUM>). For example, the stored values can be stored in a library or matrix, and learned and/or updated via a trend analysis manager (e.g., trend manager <NUM>). Whether a defect exists is determined, in block <NUM>, based on processing of the measured and stored data (e.g., via the state manager <NUM>). For example, if the method determines that no defect exists, the method returns to block <NUM> to continuously monitor the characteristics of the composite article.

If a defect is identified in the composite article (e.g., via the state manager <NUM>), the method generates an alert in block <NUM>. As described herein, the alert can be an audible or visual alert to a human operator (e.g., via HMI <NUM>) and/or presented to an actuation system (e.g., actuation system <NUM>) to adjust an operation of the manufacturing process. In some examples, the method designates the manufactured article as containing a defect based on the comparison in block <NUM>. In response, the defective article can be singled out for further inspection, destruction, etc..

These systems and methods described herein may be similarly useful in trend analytics for different fields, such as flight operational quality assurance (FOQA), flight data monitoring (FDM), and flight data analysis for airplanes, engine maintenance for cars, as well as other data intensive environments.

Early identification and adjustment of a manufacturing process in response improves overall quality and yields a higher proportion of saleable products for delivery, with the added benefit of increasing output quantity as a result. In terms of commercialization, these systems and methods can tools to increase competitiveness for manufacturers who employ them.

The market for smart factory technology is currently valued in the hundreds of billions of dollars, and is expected to continue to grow at a healthy rate over the next several years. In order to remain competitive in the manufacturing market, manufacturers should develop smarter tools that will enable greater production efficiencies. The disclosed systems and methods provide for manufacturer's hardware and software to advance the principles of lean manufacturing while maintaining or exceeding quality benchmarks.

Claim 1:
An automated inspection system (<NUM>) for monitoring a manufacturing process comprising:
a core platform (<NUM>) to operatively coupled with a plurality of subsystems via one or more interfaces;
a sensor (<NUM>) system (<NUM>) operatively coupled with the core platform (<NUM>) to monitor one or more characteristics of a composite article being manufactured, the core platform (<NUM>) configured to:
receive a first measurement of the one or more characteristics of a composite article from the sensor (<NUM>) system (<NUM>) during layup of the composite article;
receive data regarding a second measurement of the one or more characteristics from the sensor (<NUM>) system (<NUM>) after curing the composite article; and
generate an alert in response to a determination that a defect exists in the composite article based on the first or second measurement; and
characterized in that a state manager (<NUM>) is operatively coupled with the core platform (<NUM>) and configured to determine that a defect exists in the composite article, wherein determining that a defect exists in the composite article comprises comparing data acquired by one or more sensors to one or more stored threshold values, said threshold values having been learned through a machine learning process by a trend analysis manager (<NUM>).