ALGORITHMIC METHODOLOGY FOR WORKING HOUR ANALYTICS AND MONITORING OF OVERHEAD CONVEYOR CRATE WASHERS IN AUTOMOTIVE MANUFACTURING

Abstract

Disclosed herein a system of Algorithmic methodology for working hour analytics and monitoring of overhead conveyor crate washers in automotive manufacturing comprises AMWH_OHCMote (100), outfitted with a Raspberry Pi Processor Board (100K), GSM Modem (100A), External GPU Board (100C), Actuator Module (100J), Liquid Temperature Sensor (100H), Vibration Sensor (100F), Pressure Sensor (100G), Accelerometer (100E), HMI Display (100B), and Power Supply (100D), revolutionizes the monitoring and optimization of overhead conveyor crate washers in automotive manufacturing. It offers real-time insights, efficiency calculations, and practical recommendations for enhanced operational performance. The AMWH_OHCMote system's Raspberry Pi Processor Board, which functions as the system's central computing platform, effectively processes data from integrated sensors to enable real-time analytics and help with accurate work schedule computation and overall operational efficiency in the monitoring of overhead conveyor crate washers in the automotive industry.

Specification

Description:FIELD OF THE INVENTION
This invention relates to algorithmic methodology for working hour analytics and monitoring of overhead conveyor crate washers in automotive manufacturing.
BACKGROUND OF THE INVENTION
This innovative technology uses machine learning algorithms, cloud-based analytics, and state-of-the-art sensors to revolutionize the management and improvement of overhead conveyor crate washers in the car manufacturing industry. Along with the machine's operating schedule, it gathers real-time data from a variety of sensors, including temperature, vibration, pressure, and accelerometer, and securely stores this data in the cloud. The system computes operational hours, evaluates efficiency, and provides insightful recommendations for improvement by utilizing machine learning. The system's internet connectivity enables remote monitoring through a customized web dashboard, and the Human-Machine Interface (HMI) Display offers an operator-friendly interface for locally accessing these insights.
Traditional methods of monitoring and enhancing overhead conveyor crate washers in the automotive industry provide significant difficulties since they do not have the real-time information required for effective functioning. The absence of an all-encompassing framework for gathering and interpreting data has hindered the precise computation of working hours, assessment of productivity, and delivery of feasible recommendations for enhancement. Decisions made by operators and authorities are not ideal because of impediments to timely information access.
JP6382278B2: The present invention relates to a crate cleaning machine for cleaning a crate for conveying articles. Crate (community box) for transporting goods such as food is transported by sorting and transporting products in the crate for each store at the distribution center, and after receiving the product at each store, the crate is returned and washed It is reused after being cleaned by the equipment. The cleaning device for crate cleaning has a cleaning unit, a rinsing unit, and a drying unit as in Patent Document 1, and is arranged to be linear using a belt conveyor to clean the crate. There are so many things. Moreover, in invention of patent document 1, the device which floats and removes deposits, such as a seal and a tape adhering to a crate surface, from a crate surface in a swelling part is devised. The cleaning device of Patent Document 1 can clean a large amount of crate, but is a large-scale device.
RESEARCH GAP: Working Hour Analytics and Monitoring through combination of Edge technology, IoT, Cloud ML Algorithm of Overhead Conveyor Crate Washers in industrial environment is the novelty of the system.
US5343886A: Apparatus for washing bottles, in which a transfer conveyor define an endless loop belt between an inlet end and outlet end. A plurality of cups are carried along the endless loop. An inlet elevator adjacent the inlet end supplies a quantity of bottles in an open end down orientation to the cups. Fluid injectors are movably mounted on the conveyor to position a nozzle into each open end down oriented bottle to supply fluid to inside of the bottles. An outlet elevator is adjacent outlet end for removing bottles from the apparatus in an open end up orientation.
RESEARCH GAP: Working Hour Analytics and Monitoring through combination of Edge technology, IoT, Cloud ML Algorithm of Overhead Conveyor Crate Washers in industrial environment is the novelty of the system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The AMWH_OHCMote system is a cutting-edge technology designed to improve the effectiveness and capacity for monitoring of overhead conveyor crate washers used in the automobile industry. The ability of this innovation to collect and process data from a range of interconnected sensors is at its core. Among the sensors included in this group are an accelerometer, pressure, vibration, liquid temperature, and others. The gathered data is instantly uploaded in real-time into a customized cloud server together with the machine's on/off schedule. The data is processed and analyzed by the system using Machine Learning techniques after being safely stored in the cloud. In order to calculate working hours based on the machine's on-off schedule, take into account extra input from authorized operators, and assess the amount of work completed, machine learning algorithms are essential. The ML algorithms can evaluate the overhead conveyor crate washers' operational efficiency thanks to this extensive dataset.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The AMWH_OHCMote system is a cutting-edge technology designed to improve the effectiveness and capacity for monitoring of overhead conveyor crate washers used in the automobile industry. The ability of this innovation to collect and process data from a range of interconnected sensors is at its core. Among the sensors included in this group are an accelerometer, pressure, vibration, liquid temperature, and others. The gathered data is instantly uploaded in real-time into a customized cloud server together with the machine's on/off schedule. The data is processed and analyzed by the system using Machine Learning techniques after being safely stored in the cloud. In order to calculate working hours based on the machine's on-off schedule, take into account extra input from authorized operators, and assess the amount of work completed, machine learning algorithms are essential. The ML algorithms can evaluate the overhead conveyor crate washers' operational efficiency thanks to this extensive dataset.
The system's Human-Machine Interface (HMI) Display presents the results of the machine learning analysis. This screen serves as an intuitive user interface, providing authorities and operators with useful information and recommendations produced by the algorithms. These ideas could include suggestions for streamlining operational procedures or raising the crate washers' general effectiveness. Operators are able to make well-informed judgments quickly because to this real-time feedback loop. Moreover, the AMWH_OHCMote's connectivity capabilities go beyond the local interface because the system has internet connectivity. This makes it easier to access a customized online dashboard and to monitor remotely. Through this web dashboard, operators and authorities can easily monitor the system's performance, working recommendations, and analytics, providing a thorough overview of the operation and efficiency of the overhead conveyor crate washers.
BEST METHOD OF WORKING
Using advanced sensors, cloud-based analytics, and machine learning algorithms, the AMWH_OHCMote, outfitted with a Raspberry Pi Processor Board, GSM Modem, External GPU Board, Actuator Module, Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, Accelerometer, HMI Display, and Power Supply, revolutionizes the monitoring and optimization of overhead conveyor crate washers in automotive manufacturing. It offers real-time insights, efficiency calculations, and practical recommendations for enhanced operational performance.
The AMWH_OHCMote system's Raspberry Pi Processor Board, which functions as the system's central computing platform, effectively processes data from integrated sensors to enable real-time analytics and help with accurate work schedule computation and overall operational efficiency in the monitoring of overhead conveyor crate washers in the automotive industry.
In order to ensure real-time monitoring and accessibility for operators and authorities in the optimization of overhead conveyor crate washers in automotive manufacturing, the GSM modem, which is also integrated into the AMWH_OHCMote, is used to facilitate seamless communication by enabling the transmission of logged machine data and analytics to a customized cloud server.
This innovation's computational power is increased by the External GPU Board integrated into AMWH_OHCMote. This board speeds up complicated machine learning algorithms and makes it possible to process sensor data efficiently, which helps with accurate analytics and overhead conveyor crate washer optimization in the automotive manufacturing industry.
To convert the insights produced by machine learning algorithms into practicable modifications, the Actuator Module connected to the AMWH_OHCMote is utilized. This enables dynamic modifications and enhancements in the operational performance of overhead conveyor crate washers in the automotive manufacturing industry.
By monitoring environmental conditions, equipment stability, and performance metrics, the Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer-all of which are connected within the AMWH_OHCMote-allows for extensive real-time data collection, providing vital information for accurate analytics and optimization of overhead conveyor crate washers in the automotive manufacturing industry.
The HMI Display that is interfaced in the AMWH_OHCMote, is used for natural interface, presenting real-time insights, efficiency calculations, and actionable recommendations generated by Machine Learning algorithms for operators and officials to make informed decisions and optimize the performance of expenses conveyor crate washers in automotive manufacturing.
The AMWH_OHCMote's plug-in Power Supply is utilized to power all integrated components with consistent and dependable performance. This allows for ongoing data monitoring, analytics, and optimization of overhead conveyor crate washers in the automotive manufacturing industry.

ADVANTAGES OF THE INVENTION
1. The AMWH_OHCMote system is a revolutionary solution that revolutionizes the optimization and monitoring of overhead conveyor crate washers in the automotive manufacturing industry. It does this by utilizing state-of-the-art sensors, machine learning algorithms, and cloud-based analytics. With its real-time insights, efficiency estimates, and practical recommendations, this technology helps to increase operational performance.
2. The AMWH_OHCMote system's embedded GSM modem allows for the transfer of recorded machine data and analytics to a specially designed cloud server, facilitating smooth communication. For those operating and governing bodies responsible for improving overhead conveyor crate washers in the automotive production industry, this functionality guarantees real-time monitoring and accessibility.
3. The AMWH_OHCMote system's External GPU Board increases its processing capacity, speeding complex machine learning algorithms and enabling effective sensor data processing. This improvement helps with accurate analytics and optimizes overhead conveyor crate washers used in the automobile industry.
4. The AMWH_OHCMote system's Actuator Module converts the insights produced by machine learning algorithms into modifications that may be implemented. This feature promotes improvements in the operational performance of overhead conveyor crate washers in the automotive production industry by enabling dynamic adjustments.
5. The AMWH_OHCMote system's integration of the Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer allows for thorough real-time data collecting. Through the monitoring of environmental conditions, equipment stability, and performance indicators, this integration offers vital information for accurate analytics and optimization of overhead conveyor crate washers in the automotive production industry.
6. The AMWH_OHCMote system's HMI Display serves as an easy-to-use interface, offering up-to-date information, efficiency estimates, and practical recommendations produced by machine learning algorithms. With the help of this interface, operators and authorities may maximize the efficiency of overhead conveyor crate washers in the automotive production industry and make well-informed judgments.
, Claims:1. A system of Algorithmic methodology for working hour analytics and monitoring of overhead conveyor crate washers in automotive manufacturing comprises AMWH_OHCMote (100), outfitted with a Raspberry Pi Processor Board (100K), GSM Modem (100A), External GPU Board (100C), Actuator Module (100J), Liquid Temperature Sensor (100H), Vibration Sensor (100F), Pressure Sensor (100G), Accelerometer (100E), HMI Display (100B), and Power Supply (100D), revolutionizes the monitoring and optimization of overhead conveyor crate washers in automotive manufacturing; and offers real-time insights, efficiency calculations, and practical recommendations for enhanced operational performance.
2. The system as claimed in claim 1, wherein the AMWH_OHCMote system's Raspberry Pi Processor Board, which functions as the system's central computing platform, effectively processes data from integrated sensors to enable real-time analytics and help with accurate work schedule computation and overall operational efficiency in the monitoring of overhead conveyor crate washers in the automotive industry.
3. The system as claimed in claim 1, wherein order to ensure real-time monitoring and accessibility for operators and authorities in the optimization of overhead conveyor crate washers in automotive manufacturing, the GSM modem, which is also integrated into the AMWH_OHCMote, is used to facilitate seamless communication by enabling the transmission of logged machine data and analytics to a customized cloud server.
4. The system as claimed in claim 1, wherein this innovation's computational power is increased by the External GPU Board integrated into AMWH_OHCMote, this board speeds up complicated machine learning algorithms and makes it possible to process sensor data efficiently, which helps with accurate analytics and overhead conveyor crate washer optimization in the automotive manufacturing industry.
5. The system as claimed in claim 1, wherein to convert the insights produced by machine learning algorithms into practicable modifications, the Actuator Module connected to the AMWH_OHCMote is utilized, this enables dynamic modifications and enhancements in the operational performance of overhead conveyor crate washers in the automotive manufacturing industry.
6. The system as claimed in claim 1, wherein by monitoring environmental conditions, equipment stability, and performance metrics, the Liquid Temperature Sensor, Vibration Sensor, Pressure Sensor, and Accelerometer-all of which are connected within the AMWH_OHCMote allows for extensive real-time data collection, providing vital information for accurate analytics and optimization of overhead conveyor crate washers in the automotive manufacturing industry.
7. The system as claimed in claim 1, wherein the HMI Display that is interfaced in the AMWH_OHCMote, is used for natural interface, presenting real-time insights, efficiency calculations, and actionable recommendations generated by Machine Learning algorithms for operators and officials to make informed decisions and optimize the performance of expenses conveyor crate washers in automotive manufacturing.
8. The system as claimed in claim 1, wherein the AMWH_OHCMote's plug-in Power Supply is utilized to power all integrated components with consistent and dependable performance, this allows for ongoing data monitoring, analytics, and optimization of overhead conveyor crate washers in the automotive manufacturing industry.

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