AI DRIVEN WORKFORCE MONITORING, PREDICTIVE SUGGESTIONS AND RECOMMENDATION FOR AUTOMOTIVE DIE-CASTING MACHINE IN AUTOMOTIVE MANUFACTURING PLANTS

Abstract

A system of driven workforce monitoring, predictive suggestions and recommendation for automotive die-casting machine in automotive manufacturing plants comprises AIWMTCPSNode (10), which is outfitted with a Raspberry Pi Processor Board (60), Neural Stick (20), GSM Modem (30), MEMS Vibration Sensor (40), Temperature Sensor (45), Pressure Sensor (50), Humidity Sensor (55), Touch Screen HMI Display (25), Speaker (15), and Power Supply (35) provides real-time monitoring, predictive recommendations, and practical insights for maximizing worker productivity and die-casting machine performance in automotive manufacturing plants this innovation uses a Raspberry Pi Processor Board as its central processing unit, which facilitates data processing and communication between sophisticated sensors and the cloud-based analytics platform, this allows real-time performance optimization and monitoring of the automotive die-casting machine.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai driven workforce monitoring, predictive suggestions and recommendation for automotive die-casting machine in automotive manufacturing plants
BACKGROUND OF THE INVENTION
This state-of-the-art system introduces an intelligent and integrated solution for workforce monitoring and die-casting machine optimization, which completely changes automotive manufacturing operations. By means of an advanced range of sensors, the system reliably collects and evaluates data in real time on machine performance, including vibrations, temperature, pressure, and environmental factors. It computes working hours, assesses the amount of work, and generates personalized recommendations to improve productivity using sophisticated machine learning algorithms. The option for authorized operators to enter extra parameters guarantees a thorough evaluation of the manufacturing process.
The current state of die-casting machine performance monitoring and optimization presents challenges for car manufacturing plants. Existing systems sometimes don't provide fast insights into critical machine characteristics, which can result in less-than-ideal performance, increased downtime, and even operational inefficiencies. Machine metrics, such work quantity and working hours, are difficult to manually track and prone to inaccuracy.
WO2020259534A1: Provided are a high thermal conductivity aluminum alloy material and a preparation method therefor, which relate to the field of aluminum alloy materials. The main components of the high thermal conductivity aluminium alloy are silicon, magnesium, iron, copper, manganese, strontium, cerium, lanthanum, and aluminum, and the aluminum alloy material comprises the following components in mass percentage: Si: 6.0%-8.0%; Mg: 0.25%-0.5%; Fe: 0.5%-0.8%; Cu: 0-0.1%; Mn: 0-0.04%; Sr: 0-0.1%; composite rare earth: 0-0.2%; and the balance being aluminum and impurity elements, wherein the mass percentage of each impurity element needs to be controlled below 0.015%. In the embodiment of the present application, by designing the components of the aluminum alloy, the obtained high thermal conductivity aluminum alloy can achieve both high thermal conductivity and good mechanical properties, and is suitable for preparing precise and complex die-casting structural parts applied to terminal products.
RESEARCH GAP: AI driven Workforce Monitoring with IoT and Cloud integration for Automotive Die-Casting Machine is the novelty of the system.
CN105414515B: The invention discloses a kind of die casting mechanism of horizontal cold room vacuum die casting machine, including pressure chamber, compression mod, vacuum valve, drift, penetrate bar, the first stop valve, the first vacuum system, the second stop valve, the second vacuum system, control device and displacement transducer, compression mod includes fixed half and moving half, and the dynamic model cooperatively forms die cavity, ingate, exhaust duct with the cover half;Vacuum valve is arranged at the valve pocket in cover half;Drift is fixedly connected with the bar of penetrating;Sprue gate and tube connection ports are provided with pressure chamber, the second vacuum tube is connected with the tube connection ports, the other end of second vacuum tube is connected with the second stop valve, and second stop valve connects second vacuum system;Control device is used for the operating for controlling first vacuum system and the second vacuum system, and the opening and closing of the first stop valve and the second stop valve;The present invention has the advantages of good vacuumizing effect, setting easy maintenance, fault rate is low, service life is long.
RESEARCH GAP: AI driven Workforce Monitoring with IoT and Cloud integration for Automotive Die-Casting Machine 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 real-time monitoring, analysis, and optimization of automobile die-casting machine performance is achieved by the seamless integration of sensor data, cloud-based analytics, and machine learning by the AIWMTCPSNode. This technology, called AIWMTCPSNode, is used in automobile manufacturing facilities to optimize operational efficiency. It integrates cutting-edge sensors, computational components, and cloud-based analytics to deliver insightful data. The main innovation is its capacity to combine data from multiple sources. It has a Raspberry Pi Processor Board, a Neural Stick, and a number of sensors, including a temperature, pressure, humidity, and MEMS vibration sensor. The automotive die-casting machine's sensors are always gathering data in real time, including vibrations, temperature changes, pressure levels, and ambient variables. For evaluating the machine's general health and performance, this data is essential. Afterwards, a GSM modem is used to safely transfer the gathered sensor data to a customized cloud server, guaranteeing effective communication.
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 real-time monitoring, analysis, and optimization of automobile die-casting machine performance is achieved by the seamless integration of sensor data, cloud-based analytics, and machine learning by the AIWMTCPSNode. This technology, called AIWMTCPSNode, is used in automobile manufacturing facilities to optimize operational efficiency. It integrates cutting-edge sensors, computational components, and cloud-based analytics to deliver insightful data. The main innovation is its capacity to combine data from multiple sources. It has a Raspberry Pi Processor Board, a Neural Stick, and a number of sensors, including a temperature, pressure, humidity, and MEMS vibration sensor. The automotive die-casting machine's sensors are always gathering data in real time, including vibrations, temperature changes, pressure levels, and ambient variables. For evaluating the machine's general health and performance, this data is essential. Afterwards, a GSM modem is used to safely transfer the gathered sensor data to a customized cloud server, guaranteeing effective communication.
Machine learning algorithms analyze data in the cloud to determine working hours and assess the amount of labor. Additional parameters can be entered by authorized operators, enabling the system to evaluate the production process in its entirety. In order to improve productivity and efficiency, machine learning algorithms use the examined data to produce insights and recommendations. The customized cloud server functions as a centralized archive for past data, enabling the AI algorithms to learn new things on a constant basis. In addition to keeping an eye on the machine's current condition, the system also learns from its previous performance, which eventually makes forecasts and recommendations that are more accurate. Multiple interfaces are used to display the results of the machine learning algorithms. Operators on the shop floor are given real-time feedback via the Touch Screen HMI Display, which helps them see recommendations and make wise choices. The system also speaks via a speaker, sending out auditory alerts that require quick action. By connecting the AIWMTCPSNode to the internet, operators and authorities can get suggestions and machine status anytime, anywhere for remote monitoring and decision-making using a customized web dashboard and mobile app.
BEST METHOD OF WORKING
By seamlessly integrating advanced sensors, cloud-based analytics, and machine learning algorithms, the AIWMTCPSNode-which is outfitted with a Raspberry Pi Processor Board, Neural Stick, GSM Modem, MEMS Vibration Sensor, Temperature Sensor, Pressure Sensor, Humidity Sensor, Touch Screen HMI Display, Speaker, and Power Supply-provides real-time monitoring, predictive recommendations, and practical insights for maximizing worker productivity and die-casting machine performance in automotive manufacturing plants.
This innovation uses a Raspberry Pi Processor Board as its central processing unit, which facilitates data processing and communication between sophisticated sensors and the cloud-based analytics platform. This allows real-time performance optimization and monitoring of the automotive die-casting machine.
The AIWMTCPSNode's integrated Neural Stick improves computational capabilities by speeding up machine learning procedures, facilitating effective sensor data analysis, and offering prompt, astute insights for maximizing die-casting machine performance and worker productivity in automotive manufacturing facilities.
The GSM modem integrated into the AIWMTCPSNode is used to enable real-time sensor data transmission from the AIWMTCPSNode to a customized cloud server, guaranteeing seamless connectivity for thorough analysis and monitoring of automotive die-casting machine performance in manufacturing facilities. This promotes safe and effective communication.
The AIWMTCPSNode is equipped with a variety of real-time sensors, including MEMS vibration, temperature, pressure, and humidity sensors. These sensors work together to provide a comprehensive set of data that allows the AIWMTCPSNode to monitor and assess key parameters in automotive die-casting machines, facilitating accurate analysis, predictive maintenance, and suggestions for the best workforce for increased manufacturing efficiency.
The Touch Screen HMI Display, which is interfaced on AIWMTCPSNode, is used to monitor automotive die-casting machines in manufacturing plants and to give operators on the shop floor real-time feedback. It also displays AI-driven suggestions and speeds up decision-making.
The AIWMTCPSNode's Speaker is an audio interface device that provides operators with real-time notifications and feedback. It guarantees that operators are aware of AI-driven recommendations right away and speeds up decision-making for the purpose of maximizing worker productivity and the performance of automotive die-casting machines in manufacturing facilities.

ADVANTAGES OF THE INVENTION
1. This creative solution's AIWMTCPSNode serves as the core intelligence hub, combining machine learning algorithms, cloud-based analytics, and sophisticated sensors in a seamless manner. It provides predictive recommendations, actionable insights, and real-time monitoring to maximize worker productivity and improve die-casting machine performance in automotive manufacturing facilities.
2. The GSM Modem is essential for safe and effective connection since it makes it easier for real-time sensor data from the AIWMTCPSNode to be transferred to a specially designed cloud server. This feature guarantees uninterrupted connectivity, allowing for thorough investigation and tracking of the performance of automobile die-casting machines in manufacturing facilities.
3. A complete set of real-time data is provided by the MEMS Vibration Sensor, Temperature Sensor, Pressure Sensor, and Humidity Sensor taken together. This gives the AIWMTCPSNode the ability to track and evaluate important characteristics in automobile die-casting machines, enabling accurate analysis, preventative maintenance, and workforce recommendations that maximize production efficiency.
4. The Touch Screen HMI Display provides shop floor operators with real-time feedback and an easy-to-use user interface. It presents recommendations powered by AI, facilitating prompt decision-making for increased productivity and plant-level monitoring of automotive die-casting equipment.
5. The Speaker serves as an audio interface, providing operators with real-time notifications and feedback to guarantee that they are aware of AI-driven recommendations right away. This makes it easier to make decisions quickly in order to maximize worker productivity and improve the operation of automobile die-casting machines in manufacturing facilities.
, Claims:1. A system of AI driven workforce monitoring, predictive suggestions and recommendation for automotive die-casting machine in automotive manufacturing plants comprises AIWMTCPSNode (10), which is outfitted with a Raspberry Pi Processor Board (60), Neural Stick (20), GSM Modem (30), MEMS Vibration Sensor (40), Temperature Sensor (45), Pressure Sensor (50), Humidity Sensor (55), Touch Screen HMI Display (25), Speaker (15), and Power Supply (35) provides real-time monitoring, predictive recommendations, and practical insights for maximizing worker productivity and die-casting machine performance in automotive manufacturing plants.
2. The system as claimed in claim 1, wherein this innovation uses a Raspberry Pi Processor Board as its central processing unit, which facilitates data processing and communication between sophisticated sensors and the cloud-based analytics platform, this allows real-time performance optimization and monitoring of the automotive die-casting machine.
3. The system as claimed in claim 1, wherein the AIWMTCPSNode's integrated Neural Stick improves computational capabilities by speeding up machine learning procedures, facilitating effective sensor data analysis, and offering prompt, astute insights for maximizing die-casting machine performance and worker productivity in automotive manufacturing facilities.
4. The system as claimed in claim 1, wherein the GSM modem integrated into the AIWMTCPSNode is used to enable real-time sensor data transmission from the AIWMTCPSNode to a customized cloud server, guaranteeing seamless connectivity for thorough analysis and monitoring of automotive die-casting machine performance in manufacturing facilities, this promotes safe and effective communication.
5. The system as claimed in claim 1, wherein the AIWMTCPSNode is equipped with a variety of real-time sensors, including MEMS vibration, temperature, pressure, and humidity sensors, these sensors work together to provide a comprehensive set of data that allows the AIWMTCPSNode to monitor and assess key parameters in automotive die-casting machines, facilitating accurate analysis, predictive maintenance, and suggestions for the best workforce for increased manufacturing efficiency.
6. The system as claimed in claim 1, wherein the Touch Screen HMI Display, which is interfaced on AIWMTCPSNode, is used to monitor automotive die-casting machines in manufacturing plants and to give operators on the shop floor real-time feedback, it also displays AI-driven suggestions and speeds up decision-making.
7. The system as claimed in claim 1, wherein the AIWMTCPSNode's Speaker is an audio interface device that provides operators with real-time notifications and feedback, it guarantees that operators are aware of AI-driven recommendations right away and speeds up decision-making for the purpose of maximizing worker productivity and the performance of automotive die-casting machines in manufacturing facilities.

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