1. A smart robotic system for quality control in manufacturing processes comprising: one or more robotic arms equipped with sensors for identifying defects in manufactured products; a central processing unit for receiving data from the sensors and controlling the movement of the robotic arms; a machine learning module for analyzing the data and identifying patterns of defects; and a user interface for displaying the results of the quality control process.

2. The system of claim 1, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.

3. The system of claim 1, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.

4. The system of claim 1, wherein the machine learning module is trained on a dataset of known defects to identify and classify defects in real-time.

5. The system of claim 1, further comprising a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system.

6. The system of claim 1, wherein the user interface comprises a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.

7. A method for quality control in manufacturing processes using a smart robotic system, comprising the steps of: equipping one or more robotic arms with sensors for identifying defects in manufactured products; programming a central processing unit to control the movement of the robotic arms and receive data from the sensors; training a machine learning module on a dataset of known defects to identify and classify defects in real-time; analyzing the data collected by the sensors and the machine learning module to identify patterns of defects; and displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action.

8. The method of claim 7, further comprising the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects.

9. The method of claim 7, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.

10. The method of claim 7, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors SMART ROBOTIC SYSTEM FOR QUALITY CONTROL IN MANUFACTURING PROCESSES Abstract A smart robotic system for quality control in manufacturing processes may contain one or more robotic arms equipped with sensors for recognizing flaws in made items. Such a system may be included in embodiments of the current disclosure. A central processing unit may also be included in certain embodiments. This device is responsible for collecting data from the sensors and directing the movement of the robotic arms. In certain embodiments, there may additionally be a machine learning module included for the purpose of evaluating the data and identifying patterns of problems. A user interface for showing the results of the quality control process may also be included in embodiments in certain circumstances. , Claims:Claims :

1. A smart robotic system for quality control in manufacturing processes comprising: one or more robotic arms equipped with sensors for identifying defects in manufactured products; a central processing unit for receiving data from the sensors and controlling the movement of the robotic arms; a machine learning module for analyzing the data and identifying patterns of defects; and a user interface for displaying the results of the quality control process.

2. The system of claim 1, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.

3. The system of claim 1, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.

4. The system of claim 1, wherein the machine learning module is trained on a dataset of known defects to identify and classify defects in real-time.

5. The system of claim 1, further comprising a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system.

6. The system of claim 1, wherein the user interface comprises a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.

7. A method for quality control in manufacturing processes using a smart robotic system, comprising the steps of: equipping one or more robotic arms with sensors for identifying defects in manufactured products; programming a central processing unit to control the movement of the robotic arms and receive data from the sensors; training a machine learning module on a dataset of known defects to identify and classify defects in real-time; analyzing the data collected by the sensors and the machine learning module to identify patterns of defects; and displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action.

8. The method of claim 7, further comprising the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects.

9. The method of claim 7, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.

10. The method of claim 7, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors

Description:SMART ROBOTIC SYSTEM FOR QUALITY CONTROL IN MANUFACTURING PROCESSES
Field of the Invention
[0001] The present invention relates to a smart robotic system for quality control in manufacturing processes. More specifically, the invention relates to an improved system for detecting defects in products using advanced sensors, algorithms, and machine learning techniques, and providing real-time feedback to operators. The system can adapt to different lighting conditions and product variations and maintain consistent performance in different environments, thereby enhancing the overall quality control process in manufacturing.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Quality control is an essential aspect of manufacturing processes, as it ensures that the products being produced meet the desired specifications. Traditionally, quality control measures have involved manual inspections, which are time-consuming and prone to errors. Moreover, human inspectors may get tired or distracted, which can lead to an increased risk of errors and inconsistencies.
[0004] To address these limitations, smart robotic systems have been introduced in recent years, which offer a more efficient and reliable method of quality control. Smart robotic systems use advanced sensors and algorithms to analyze products and detect defects. These systems can work continuously without fatigue, providing consistent results with minimal human intervention. Few of the patent litratures are listed below.
[0005] US20220187847A1 (By: STRONG FORCE VCN PORTFOLIO 2019) A robot fleet management platform includes datastores configured to store a governance library defining governance standards. Processors execute computer-readable instructions to implement a governance-enabling intelligence layer that receives and responds to intelligence requests received from intelligence service clients. The intelligence layer includes artificial intelligence services including machine learning, rules-based intelligence, digital twin, robot process automation, and machine vision. The set of governance standards is applied to decisions made by one or more of the set of artificial intelligence services. An intelligence layer controller coordinates performance of the artificial intelligence services on behalf of the intelligence service clients and performance of analyses corresponding to the artificial intelligence services based on the set of governance standards. The intelligence layer returns decisions determined by the artificial intelligence services in response to the intelligence requests. The decisions are determined based on intelligence service data sources and the set of analyses.
[0006] US20220197247A1 (By: STRONG FORCE VCN PORTFOLIO 2019) An information technology system for a distributed manufacturing network includes an additive manufacturing management platform with an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from a set of distributed manufacturing network entities and execute simulations on digital twins of the set of distributed manufacturing network entities to make classifications, predictions, and optimization-related decisions for the set of distributed manufacturing network entities. The information technology system includes a distributed ledger system integrated with a digital thread and configured to provide unified views of workflow and transaction information to the set of distributed manufacturing network entities.
[0007] JP2573547B2 (By: FANUC ROBOTICS) A method and system for the flexible assembly of components into an assembly at an assembly station within an assembly area in an adaptive, programmable fashion. Several programmable locators mounted on a platform work cooperatively to receive and support components or parts having critical positioning features at approximate locations. The programmable locators then move the components so that the critical positioning features and hence the components are at desired locations. Thereafter, part position and orientation are constrained at retaining locations while the components are in their desired locations. Processing equipment at least partially joins the retained components either at the assembly station or at a separate processing station. One of the programmable locators may provide one of the retaining locations. Preferably, at least one sensor mounted on one of the programmable locators provides at least one feedback signal for a control means which controls at least one programmable locator to adapt its position with respect to at least one critical feature of its part to thereby relocate the part. In this way, verification of the accuracy of the positioning and holding is provided.

[0008] However, existing smart robotic systems have limitations in their ability to accurately detect defects in products due to factors such as lighting conditions, variations in product materials and shapes, and environmental factors. For example, some products may have reflective surfaces that can cause glare or shadows, making it difficult for smart robotic systems to identify defects accurately. Similarly, variations in product materials and shapes can affect the system's ability to detect defects accurately. Environmental factors such as temperature, humidity, and vibration can also impact the system's performance, making it challenging to maintain consistent quality control.
[0009] There is therefore a need for an improved smart robotic system that can address these limitations and provide more accurate and efficient quality control in manufacturing processes. The improved system should be able to adapt to different lighting conditions and product variations, maintain consistent performance in different environments, and provide real-time feedback to operators.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The present invention relates to a smart robotic system for quality control in manufacturing processes. More specifically, the invention relates to an improved system for detecting defects in products using advanced sensors, algorithms, and machine learning techniques, and providing real-time feedback to operators. The system can adapt to different lighting conditions and product variations and maintain consistent performance in different environments, thereby enhancing the overall quality control process in manufacturing.
[00013] Embodiments of the present disclosure may include a smart robotic system for quality control in manufacturing processes, wherein the smart robotic system including one or more robotic arms equipped with sensors for identifying defects in manufactured products. Embodiments may also include a central processing unit for receiving data from the sensors and controlling the movement of the robotic arms. Embodiments may also include a machine learning module for analyzing the data and identifying patterns of defects. Embodiments may also include a user interface for displaying the results of the quality control process.
[00014] In some embodiments, the sensors may include cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products. In some embodiments, the central processing unit may be programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.
[00015] In some embodiments, the machine learning module may be trained on a dataset of known defects to identify and classify defects in real-time. In some embodiments, the system may include a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system. In some embodiments, the user interface may include a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.
[00016] Embodiments of the present disclosure may also include a method for quality control in manufacturing processes using a smart robotic unit, wherein the method including the steps of equipping one or more robotic arms with sensors for identifying defects in manufactured products. Embodiments may also include programming a central processing unit to control the movement of the robotic arms and receive data from the sensors.
[00017] Embodiments may also include training a machine learning module on a dataset of known defects to identify and classify defects in real-time. Embodiments may also include analyzing the data collected by the sensors and the machine learning module to identify patterns of defects. Embodiments may also include displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action.
[00018] In some embodiments, the method may include the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects. In some embodiments, the sensors may include cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products. In some embodiments, the central processing unit may be programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.
Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] FIG. 1 is a flowchart illustrating a method to provide a smart robotic system for quality control in manufacturing processes, according to some embodiments of the present disclosure.
Detailed Description
[00021] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00022] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00023] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items..
[00024] The present invention relates to a smart robotic system for quality control in manufacturing processes. More specifically, the invention relates to an improved system for detecting defects in products using advanced sensors, algorithms, and machine learning techniques, and providing real-time feedback to operators. The system can adapt to different lighting conditions and product variations and maintain consistent performance in different environments, thereby enhancing the overall quality control process in manufacturing.
[00025] A clever robotic system for quality control in manufacturing processes, the system includes, wherein the manufacturing process may involve completing one or more further stages, according to some implementations of the system, which may also include finishing the manufacturing process. There might be one or more robotic arms, each of which is outfitted with sensors that are meant to identify defects in the products that are being manufactured. A centralized processing unit, often known as a CPU, which is in charge of gathering data from the sensors and coordinating the movement of the robotic arms. A component of machine learning that conducts a study of the data and locates reoccurring patterns of errors. A user interface for the programmed that is designed with the intention of displaying the results of the quality assurance operation.
[00026] The sensors, in some implementations, may take the shape of cameras, microphones, or other kinds of sensors that are specially intended to recognize visual, auditory, or tactile problems in the manufactured products. In other words, the sensors may be able to detect defects in the created items. In some implementations, the central processing unit, or CPU, may be programmed to control the movement of the robotic arms in order to inspect the manufactured products and to take corrective action based on the data received from the sensors. This is done in order to ensure that the products meet the required standards. In different implementations, the information collected by the sensors could be utilized to guide the inspection procedure. The machine learning module in some implementations may be trained to recognize and classify faults in real time by being presented with a dataset that contains instances of known vulnerabilities. This may be done by exposing the module to the dataset. A feedback loop that, based on the information acquired by the robotic system, enables modifications to be made to the manufacturing process in order to achieve optimal results. A control panel that displays information on the state of the quality assurance process in real time. The number of issues that were discovered, how those issues were categorized, and any recommendations for resolving those issues that were made.
[00027] FIG. 1 is a flowchart that depicts a process that illustrates the technique in accordance with some implementations of the current disclosure. This method was drawn to illustrate the technology. In some implementations of the method, step 110 may consist of providing one or more robotic arms with sensors that can identify defects in the objects that are being formed. These flaws may be found in the item that is being created. Programming a central processing unit to control the movement of robotic arms and receive data from sensors may be part of the process that takes place at step 120 of the method. During the step 130 of the process, it is feasible to train a machine learning module on a dataset of known faults in order to detect and categories errors in real time. This may be done by using the dataset during step 130 of the procedure. The method may, as part of step 140, include doing an analysis of the data obtained by the sensors and the machine learning module in order to detect recurrent error patterns. Step 150 of the process may entail displaying the results of the quality control procedure on a user interface. These findings may include the number of vulnerabilities discovered, the kind of problems discovered, and recommendations on how to improve the current state of affairs.
[00028] The process of adjusting the manufacturing process based on the data obtained by the robotic system in certain implementations, with the goal of increasing product quality and decreasing the amount of errors that occur in the process. The sensors, in some implementations, may take the shape of cameras, microphones, or other kinds of sensors that are specially intended to recognize visual, auditory, or tactile problems in the manufactured products. In other words, the sensors may be able to detect defects in the created items. In some implementations, the central processing unit, or CPU, may be programmed to control the movement of the robotic arms in order to inspect the manufactured products and to take corrective action based on the data received from the sensors. This is done in order to ensure that the products meet the required standards. In different implementations, the information collected by the sensors could be utilized to guide the inspection procedure.
[00029] The present invention relates to a smart robotic system for quality control in manufacturing processes, which comprises one or more robotic arms equipped with sensors for identifying defects in manufactured products. The sensors may comprise cameras, microphones,

or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products. The robotic arms are controlled by a central processing unit (CPU) that receives data from the sensors and controls the movement of the robotic arms to inspect the manufactured products.
[00030] In addition to the robotic arms and sensors, the system includes a machine learning module for analyzing the data collected by the sensors and identifying patterns of defects. The machine learning module is trained on a dataset of known defects to identify and classify defects in real-time. The system further includes a user interface for displaying the results of the quality control process. The user interface may comprise a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.
[00031] The smart robotic system for quality control in manufacturing processes may further include a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system. This feedback loop allows for continuous improvement of the manufacturing process, which can result in improved product quality and reduced defects.
[00032] The method for quality control in manufacturing processes using the smart robotic system comprises equipping one or more robotic arms with sensors for identifying defects in manufactured products, programming the CPU to control the movement of the robotic arms and receive data from the sensors, training the machine learning module on a dataset of known defects to identify and classify defects in real-time, analyzing the data collected by the sensors and the machine learning module to identify patterns of defects, and displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action. The method may also include the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects.
[00033] In summary, the smart robotic system for quality control in manufacturing processes described herein provides a reliable, efficient, and cost-effective means of identifying defects in manufactured products. The system utilizes advanced sensors, machine learning algorithms, and robotic technology to inspect products and identify patterns of defects in real-time, enabling manufacturers to make data-driven decisions and continuously improve their manufacturing processes. The system also provides a user-friendly interface for displaying results and recommendations for corrective action, making it easy for manufacturers to take action to improve product quality and reduce defects.
[00034] A smart robotic system for quality control in manufacturing processes may be included in embodiments of the present disclosure. This system may comprise one or more robotic arms equipped with sensors for recognizing flaws in made items. A central processing unit may also be included in certain embodiments, which is responsible for collecting data from the sensors and direcing the movement of the robotic arms. In certain embodiments, there may additionally be a machine learning module included for the purpose of evaluating the data and identifying patterns of problems. A user interface for showing the results of the quality control process may also be included in embodiments in certain circumstances.
[00035] It is possible that the sensors may contain cameras, microphones, or other sorts of sensors in certain implementations, and their purpose would be to identify visual, aural, or tactile flaws in the made items. The central processing unit (CPU) in some implementations may be programmed to control the movement of the robotic arms in order to inspect the manufactured products and to take corrective action based on the data received from the sensors. In other implementations, the data from the sensors may be used to inform the inspection process.
[00036] In some implementations, the machine learning module may be taught to recognize and categories errors in real time by being exposed to a dataset containing examples of known flaws. A feedback loop that allows the system to make adjustments to the manufacturing process based on the data acquired by the robotic system may be included in certain implementations of the system. The user interface may, in certain implementations, include a dashboard that displays real-time statistics on the quality control process. This data may include the number of faults that were found, the categories of defects that were found, and suggestions for remedial action.
[00037] The present disclosure may also include a method for quality control in manufacturing processes using a smart robotic unit. This method may include the steps of equipping one or more robotic arms with sensors for identifying defects in manufactured products. Alternatively, this method may include a smart robotic unit that is used to perform quality control in manufacturing processes. Programming the central processing unit to control the movement of robotic arms and receive data from sensors is another possible aspect of embodiments.
[00038] In certain embodiments, it may also be possible to train a machine learning module to recognize and categories flaws in real time by using the dataset of known defects as a training set. In certain embodiments, identifying patterns of faults may also include performing an analysis on the data obtained from the sensors and the machine learning module. Displaying the results of the quality control process on a user interface, including the number and kinds of errors found as well as suggestions for remedial action, is another aspect that may be included in certain embodiments.
[00039] The technique may, in some implementations, comprise the step of modifying the manufacturing process in response to the data obtained from the robotic system in order to raise the level of product quality and decrease the number of flaws. It is possible that the sensors may contain cameras, microphones, or other sorts of sensors in certain implementations, and their purpose would be to identify visual, aural, or tactile flaws in the made items. The central processing unit (CPU) in some implementations may be programmed to control the movement of the robotic arms in order to inspect the manufactured products and to take corrective action based on the data received from the sensors. In other implementations, the data from the sensors may be used to inform the inspection process.
[00040] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00041] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00042] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00043] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00044] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00045] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
 

Claims
I/We Claim:
1. A smart robotic system for quality control in manufacturing processes comprising: one or more robotic arms equipped with sensors for identifying defects in manufactured products; a central processing unit for receiving data from the sensors and controlling the movement of the robotic arms; a machine learning module for analyzing the data and identifying patterns of defects; and a user interface for displaying the results of the quality control process.
2. The system of claim 1, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.
3. The system of claim 1, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.
4. The system of claim 1, wherein the machine learning module is trained on a dataset of known defects to identify and classify defects in real-time.
5. The system of claim 1, further comprising a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system.
6. The system of claim 1, wherein the user interface comprises a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.
7. A method for quality control in manufacturing processes using a smart robotic system, comprising the steps of: equipping one or more robotic arms with sensors for identifying defects in manufactured products; programming a central processing unit to control the movement of the robotic arms and receive data from the sensors; training a machine learning module on a dataset of known defects to identify and classify defects in real-time; analyzing the data collected by the sensors and the machine learning module to identify patterns of defects; and displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action.
8. The method of claim 7, further comprising the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects.
9. The method of claim 7, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.
10. The method of claim 7, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors

SMART ROBOTIC SYSTEM FOR QUALITY CONTROL IN MANUFACTURING PROCESSES
Abstract
A smart robotic system for quality control in manufacturing processes may contain one or more robotic arms equipped with sensors for recognizing flaws in made items. Such a system may be included in embodiments of the current disclosure. A central processing unit may also be included in certain embodiments. This device is responsible for collecting data from the sensors and directing the movement of the robotic arms. In certain embodiments, there may additionally be a machine learning module included for the purpose of evaluating the data and identifying patterns of problems. A user interface for showing the results of the quality control process may also be included in embodiments in certain circumstances. , Claims:Claims
I/We Claim:
1. A smart robotic system for quality control in manufacturing processes comprising: one or more robotic arms equipped with sensors for identifying defects in manufactured products; a central processing unit for receiving data from the sensors and controlling the movement of the robotic arms; a machine learning module for analyzing the data and identifying patterns of defects; and a user interface for displaying the results of the quality control process.
2. The system of claim 1, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.
3. The system of claim 1, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors.
4. The system of claim 1, wherein the machine learning module is trained on a dataset of known defects to identify and classify defects in real-time.
5. The system of claim 1, further comprising a feedback loop for adjusting the manufacturing process based on the data collected by the robotic system.
6. The system of claim 1, wherein the user interface comprises a dashboard displaying real-time data on the quality control process, including the number and types of defects detected, and recommendations for corrective action.
7. A method for quality control in manufacturing processes using a smart robotic system, comprising the steps of: equipping one or more robotic arms with sensors for identifying defects in manufactured products; programming a central processing unit to control the movement of the robotic arms and receive data from the sensors; training a machine learning module on a dataset of known defects to identify and classify defects in real-time; analyzing the data collected by the sensors and the machine learning module to identify patterns of defects; and displaying the results of the quality control process on a user interface, including the number and types of defects detected, and recommendations for corrective action.
8. The method of claim 7, further comprising the step of adjusting the manufacturing process based on the data collected by the robotic system to improve product quality and reduce defects.
9. The method of claim 7, wherein the sensors comprise cameras, microphones, or other types of sensors for detecting visual, auditory, or tactile defects in the manufactured products.
10. The method of claim 7, wherein the central processing unit is programmed to control the movement of the robotic arms to inspect the manufactured products and to take corrective action based on the data received from the sensors