WIRELESS INTEGRATED NRF AND CLOUD TECHNOLOGICAL OPERATIONAL CONTROL SOLUTION FOR TWO-SLIDE DIE-CASTING MACHINE IN DISCRETE ELECTRONICS MANUFACTURING

Abstract

A wireless integrated nrf and cloud technological operational control system for two-slide die-casting machine in discrete electronics manufacturing comprises CMDCTGCM Node (100) incorporates an Arduino Tiny Machine Learning Kit (200), an accelerometer (500), current (600) and temperature sensors (800), a camera module, an actuator (700), an HMI capacitive touch display (400), a buzzer (1000) and power supply (900), allowing intensive data gathering, machine control, and predictive computations for perfect die casting machines in components manufacturing the Tiny Machine Learning Kit offered by Arduino and mounted in the CMDCTGCM Node processes data on the device and applies ML whilst on-device enabling on the fly monitoring and response to anomalies in machine performance.

Specification

Description:FIELD OF THE INVENTION
This invention relates to wireless integrated nrf and cloud technological operational control solution for two-slide die-casting machine in discrete electronics manufacturing.
BACKGROUND OF THE INVENTION
This development presents an all-inclusive approach to preventive care and regulation of die-casting machines within the component manufacturing industry. The presented system employs advanced sensors to keep track of temperature, acceleration, and current and uses vision-based monitoring for more effective analysis of the machines. The automated control of the machine is facilitated by an actuator while interaction of users with the device is smooth and effortless through a touch-based interface. The information obtained is sent over the internet to a specially designed cloud where it is processed by machine learning for purposes of recommending solutions to the problems identified in the earlier analysis. In this case, the operators and other authorized users will be able to carry out real-time monitoring of machine features by using a web dashboard or local interface which in turn helps to avert machine problems and can reduce downtime whilst enhancing operational efficiency.
This invention pertains to the improvement of unplanned wait times, maintenance, and overall operational problems in die casting machines utilized in component manufacturing. Other than capitalizing on reactive measures, predictive maintenance tends to be deficient in providing sufficient productivity leading to unavailable system causing increased production costs. Additionally, manual inspections of the machine's conditions are unreliable and do not allow for interventions because of being too general and qualitative. Such an innovation takes care of these concerns by introducing predictive maintenance and intelligent control, making use of real-time data and machine learning to make predictions of failure, enhance machine operation, and timing of maintenance action. As a consequence, it improves the processes, reduces rated times, and cuts down the expenses on maintenance, which regularizes and economizes production processes.
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 and machine learning-based predictive maintenance and control of die casting machines in component manufacturing is the novelty of the system.
CN204710798U: The utility model discloses a kind of automobile component die casting machine foundry goods rinsing table, comprise cleaning table top, cleaning hairbrush and shower, lifting column is connected with on the downside of cleaning table top, lifting column bottom connecting fluid cylinder pressure, cleaning table top is located at inside tank, tank left and right sides inwall is all provided with ultrasonic cleaner, support is provided with on the upside of tank, support bottom is fixedly connected on tank, support upper center is vertically provided with rotating shaft, rotating shaft bottom connects horizontally disposed brush handle, cleaning hairbrush is installed with on the downside of brush handle, the utility model automobile component die casting machine foundry goods rinsing table, adopt circulation hydro-peening, lifting is scrubbed and is cleaned the foundry goods after die casting with Ultrasonic Cleaning, cleaning performance is good, efficiency is high, without the need to manual operation, reduce labour intensity, and saved resource, reduce use cost, can carry out air-dry to foundry goods after cleaning in addition, effectively prevent foundry goods to get rusty, and rinsing table conveniently moving is quick, practicality and convenience high.
RESEARCH GAP: AI and machine learning-based predictive maintenance and control of die casting machines in component manufacturing 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.
This innovation works by employing multiple sensors and actuators for constant measurement and regulation of the die casting machine's operations. The system gathers critical machine health indicators including temperature, vibration, and electrical parameters among others. Furthermore, a vision-based module appears to be capturing either images or videos of the machine in order to provide operational footage in detail as it works. All these inputs are analyzed on-site for control purposes in the first instance, which ensures that damage to the harsh environment for the system is minimized, including through the use of an actuator to switch the machine on or off. Data that is taken by the system is sent over to a bespoke cloud server for additional processing. Deep learning techniques are then applied for data pattern learning to make predictions about possible breakdowns and maintenance actions. So this can help operators to do the maintenance work in the appropriate time rather than wait until the issue becomes serious. The system that is cloud based and therefore facilitates monitoring remotely and efficiently as it possible to monitor and evaluate the performance of the machines from any location.
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.
This innovation works by employing multiple sensors and actuators for constant measurement and regulation of the die casting machine's operations. The system gathers critical machine health indicators including temperature, vibration, and electrical parameters among others. Furthermore, a vision-based module appears to be capturing either images or videos of the machine in order to provide operational footage in detail as it works. All these inputs are analyzed on-site for control purposes in the first instance, which ensures that damage to the harsh environment for the system is minimized, including through the use of an actuator to switch the machine on or off. Data that is taken by the system is sent over to a bespoke cloud server for additional processing. Deep learning techniques are then applied for data pattern learning to make predictions about possible breakdowns and maintenance actions. So this can help operators to do the maintenance work in the appropriate time rather than wait until the issue becomes serious. The system that is cloud based and therefore facilitates monitoring remotely and efficiently as it possible to monitor and evaluate the performance of the machines from any location.
The data has been analyzed and can be easily viewed on a web dashboard and touchscreen interface. These interfaces display the most current information, graphics, and insights to the operators and any other authorized personnel enabling them to act accordingly and be well informed. When simple to use interfaces are combined with real time monitoring and predictive analytics, the system improves machine performance but also saves time and costs related to conventional maintenance activities.
BEST METHOD OF WORKING
The CMDCTGCM Node incorporates an Arduino Tiny Machine Learning Kit, an accelerometer, current and temperature sensors, a camera module, an actuator, an HMI capacitive touch display, a buzzer and power supply, allowing intensive data gathering, machine control, and predictive computations for perfect die casting machines in components manufacturing.
The Tiny Machine Learning Kit offered by Arduino and mounted in the CMDCTGCM Node processes data on the device and applies ML whilst on-device enabling on the fly monitoring and response to anomalies in machine performance.
The actuator incorporated in the CMDCTGCM Node provides automated control of the machines' on/off switch by analyzing the data from the sensors which increases safety and minimizes the need for manual intervention on the activities of the die casting machine.
The HMI capacitive touch display which forms part of the CMDCTGCM Node has made it easier to view real time machine data for visualization, analytics and recommendation which enhances the situation awareness and decision making of the operators.
Equipped with a camera that is integrated into CMDCTGCM Node, the machine operations monitoring system offers vision-based machine operational insights and enables more efficient predictive maintenance of the machines.
Backed by the CMDCTGCM Node, the cloud based processing of all sensor data helps integrate machine learning features, assisting the authorized users with actionable insights and monitoring facilities over the internet.
A buzzer fitted in the CMDCTGCM Node provides instant sound notification for critical machine parameters helping forestall machine failures through timely action.
ADVANTAGES OF THE INVENTION
1. The integration of the Arduino Tiny Machine Learning Kit makes it possible to process data on the device, which provides quicker reaction times, as well as the ability to process data in real time, thus improving the efficiency of the machine in operation.
2. The use of sensors like the accelerometer, a current sensor, and a temperature sensor enable the system to be able to anticipate unforeseen breakdowns or failures ensuring proper maintenance scheduling so that no unplanned downtimes are experienced.
3. Based on actual data, the actuator makes it possible to automatically turn the die casting machine on and off, promoting operational safety and accuracy.
4. Through the camera module, some vision-based information is provided which broadens the comprehension of some of the machine conditions concerning more intensive surveillance.
5. The HMI is easy to use as it features a capacitive touch display where machine operators can simply see the status of the machine, analyze the information, and view recommendations presented to them without the need to undergo complex processes.
6. Additionally, the system uses a machine learning model to push data to a different custom cloud server understanding that actionable intelligence and prediction can be generated allowing a decision to be made with adequate information available.
, Claims:1. A wireless integrated nrf and cloud technological operational control system for two-slide die-casting machine in discrete electronics manufacturing comprises CMDCTGCM Node (100) incorporates an Arduino Tiny Machine Learning Kit (200), an accelerometer (500), current (600) and temperature sensors (800), a camera module, an actuator (700), an HMI capacitive touch display (400), a buzzer (1000) and power supply (900), allowing intensive data gathering, machine control, and predictive computations for perfect die casting machines in components manufacturing.
2. The system as claimed in claim 1, wherein the Tiny Machine Learning Kit offered by Arduino and mounted in the CMDCTGCM Node processes data on the device and applies ML whilst on-device enabling on the fly monitoring and response to anomalies in machine performance.
3. The system as claimed in claim 1, wherein the actuator incorporated in the CMDCTGCM Node provides automated control of the machines' on/off switch by analyzing the data from the sensors which increases safety and minimizes the need for manual intervention on the activities of the die casting machine.
4. The system as claimed in claim 1, wherein the HMI capacitive touch display which forms part of the CMDCTGCM Node has made it easier to view real time machine data for visualization, analytics and recommendation which enhances the situation awareness and decision making of the operators.
5. The system as claimed in claim 1, wherein equipped with a camera that is integrated into CMDCTGCM Node, the machine operations monitoring system offers vision-based machine operational insights and enables more efficient predictive maintenance of the machines.
6. The system as claimed in claim 1, wherein backed by the CMDCTGCM Node, the cloud based processing of all sensor data helps integrate machine learning features, assisting the authorized users with actionable insights and monitoring facilities over the internet.
7. The system as claimed in claim 1, wherein a buzzer fitted in the CMDCTGCM Node provides instant sound notification for critical machine parameters helping forestall machine failures through timely action.

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