SYSTEM OF AI RECOMMENDATION BASED MONITORING OF TWO-SLIDE DIE-CASTING MACHINES IN THE ELECTRONICS MANUFACTURING THROUGH VIBRATION AND ACCELERATION TRENDING DATA

Abstract

Disclosed herein a System of AI Recommendation based monitoring of Two-Slide Die-Casting Machines in the Electronics Manufacturing through Vibration and Acceleration Trending Data comprises TSDCM_EMVAMote (10), which is outfitted with a Raspberry Pi Processor Board (18), GSM Modem, (11) Vibration Sensor (16), 3 Axis Accelerometer, Actuator Module (17), Feedback Sensor (13), Indicator (12), and Power Supply (14); wherein Vibration and acceleration data are collected, analyzed, and interpreted for proactive maintenance and improved operational efficiency. The central processing unit, the Raspberry Pi Processor Board, coordinates data gathering, analysis, and communication; this makes it easier to integrate sensor data from Two-Slide Die-Casting Machines with cloud computing and cutting-edge machine learning algorithms for real-time monitoring and AI-based recommendations in the electronics manufacturing industry. The built-in GSM modem in the TSDCM_EMVAMote provides smooth cellular connectivity, enabling the device to send acceleration and vibration data to a customized cloud server; and this guarantees real-time tracking of Two-Slide Die-Casting Machines in the electronics manufacturing industry and enables timely AI-based recommendations and alerts.

Specification

Description:FIELD OF THE INVENTION
This invention relates to System of AI Recommendation based monitoring of Two-Slide Die-Casting Machines in the Electronics Manufacturing through Vibration and Acceleration Trending Data
BACKGROUND OF THE INVENTION
Monitoring and maintaining the health of Two-Slide Die-Casting Machines is a major challenge facing the electronics manufacturing industry right now. Many of the monitoring systems in use today are too basic to provide real-time insights into the state of the machines, which increases downtime and inefficiencies. The complex nature of tracking vibration and acceleration patterns cannot be adequately addressed by traditional methods of data gathering and analysis.
CN105414515B 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 recommendations for the Two-Slide Die-Casting Machines using sensory data analysis with Machine learning algorithms is the novelty of the system.
CN204710798U 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 recommendations for the Two-Slide Die-Casting Machines using sensory data analysis with Machine learning algorithms 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.
This innovative solution uses artificial intelligence and cutting-edge technology to revolutionize the supervision of Two-Slide Die-Casting Machines in the electronics manufacturing industry. The machines are equipped with sensors that are positioned strategically to record acceleration and vibration data. This allows the system to provide real-time information about the machines' performance and operating state. The collected data is sent to a specialized cloud server, where sophisticated machine learning algorithms examine trends and produce recommendations and ideas powered by artificial intelligence. Through an easy-to-use dashboard, operators may readily view this information and receive email notifications for timely attention to significant situations.
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.
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.
In the electronics manufacturing industry, the TSDCM_EMVAMote invention serves as an intelligent monitoring system for Two-Slide Die-Casting Machines. To efficiently evaluate machine health, it makes use of cutting-edge technology and machine learning algorithms. The procedure starts with the Raspberry Pi Processor Board serving as the focal point and managing the gathering of data from the die-casting machines' 3-Axis Accelerometer and vibration sensor, which are placed strategically. These sensors record important acceleration and vibration information. The GSM modem then makes it possible to connect to the cellular network, which permits the data to be transferred to a dedicated cloud server with ease. Predefined machine learning algorithms in the cloud examine the data, examining acceleration and vibration patterns to produce intelligent AI-based recommendations and suggestions regarding the performance and well-being of the Two-Slide Die-Casting Machines.
The results of this investigation are shared via a number of platforms. First, an HMI (Human-Machine Interface) display shows suggestions and alerts, giving operators on the job site access to real-time data. In addition, remote monitoring and management are made possible by a personalized web dashboard that may be accessed via user accounts. The system also has proactive alert systems that notify operators via email when a critical issue is identified by the AI algorithms, guaranteeing prompt attention to the issue. The functionality of the system is further improved by the Actuator Module and Feedback Sensor. Based on the AI's recommendations, the Actuator Module can carry out tasks like modifying the machine's settings or starting up maintenance. The outcomes of these actions are recorded by the Feedback Sensor, which provide useful information for the monitoring system's ongoing optimization and improvement.
BEST METHOD OF WORKING
Disclosed herein a System of AI Recommendation based monitoring of Two-Slide Die-Casting Machines in the Electronics Manufacturing through Vibration and Acceleration Trending Data comprises TSDCM_EMVAMote (10), which is outfitted with a Raspberry Pi Processor Board (18), GSM Modem, (11) Vibration Sensor (16), 3 Axis Accelerometer, Actuator Module (17), Feedback Sensor (13), Indicator (12), and Power Supply (14); wherein Vibration and acceleration data are collected, analyzed, and interpreted for proactive maintenance and improved operational efficiency.
The central processing unit, the Raspberry Pi Processor Board, coordinates data gathering, analysis, and communication; this makes it easier to integrate sensor data from Two-Slide Die-Casting Machines with cloud computing and cutting-edge machine learning algorithms for real-time monitoring and AI-based recommendations in the electronics manufacturing industry.
The built-in GSM modem in the TSDCM_EMVAMote provides smooth cellular connectivity, enabling the device to send acceleration and vibration data to a customized cloud server; and this guarantees real-time tracking of Two-Slide Die-Casting Machines in the electronics manufacturing industry and enables timely AI-based recommendations and alerts.
Essential vibration and acceleration data from Two-Slide Die-Casting Machines are provided by the Vibration Sensor and 3 Axis Accelerometer, both of which are connected in TSDCM_EMVAMote; and which enables thorough monitoring and analysis for the creation of real-time AI-based recommendations and alerts in the electronics manufacturing industry. The Actuator Module connected to TSDCM_EMVAMote is used to put AI recommendations into practice, allowing proactive maintenance and adjustments for Two-Slide Die-Casting Machines in the electronics manufacturing industry; and this results in decreased downtime and increased operational efficiency.
The Feedback Sensor, which is attached to the TSDCM_EMVAMote, records the results of the actions performed by the Actuator Module; and this data is essential for the monitoring system for Two-Slide Die-Casting Machines in the electronics manufacturing industry to be continuously optimized, improved, and refined.
The through data collection, analysis, and AI-based recommendations, the Power Supply plugin in TSDCM_EMVAMote supports the seamless monitoring of Two-Slide Die-Casting Machines in electronics manufacturing by ensuring the continuous and reliable operation of the device and supplying the necessary electrical power for all components.
ADVANTAGES OF THE INVENTION
1. This creative method relies heavily on the TSDCM_EMVAMote, which combines artificial intelligence, cloud computing, and sensors in a seamless manner. Through the gathering, processing, and interpretation of vibration and acceleration data, this integration makes it possible to monitor Two-Slide Die-Casting Machines in real-time during the electronics manufacturing process. Encouraging proactive maintenance and improving operating efficiency are the main objectives.
2. This invention's GSM modem provides dependable cellular connectivity. It enables real-time monitoring of Two-Slide Die-Casting Machines in electronics manufacturing by enabling the TSDCM_EMVAMote to broadcast vibration and acceleration data to a customized cloud server. This feature makes it easier to send out timely AI-based recommendations and notifications to maximize the machines' operational performance.
3. This ground-breaking system relies heavily on the Vibration Sensor and 3 Axis Accelerometer to collect vital vibration and acceleration data from Two-Slide Die-Casting Machines. In the electronics manufacturing industry, this data plays a critical role in enabling thorough monitoring and analysis that results in the creation of real-time AI-based recommendations and alarms.
4. One of this innovation's main components, the Actuator Module, carries out a crucial task by acting on AI recommendations. This feature greatly improves operational efficiency and decreases downtime by enabling proactive maintenance and adjustment processes for Two-Slide Die-Casting Machines in the electronics manufacturing industry.
5. The Input This invention's sensor is essential for recording the results of movements made by the actuator module. The monitoring system created for Two-Slide Die-Casting Machines in the electronics manufacturing industry would benefit greatly from this data's constant optimization, enhancement, and fine-tuning.
, Claims:1. A System of AI Recommendation based monitoring of Two-Slide Die-Casting Machines in the Electronics Manufacturing through Vibration and Acceleration Trending Data comprises TSDCM_EMVAMote (10), which is outfitted with a Raspberry Pi Processor Board (18), GSM Modem, (11) Vibration Sensor (16), 3 Axis Accelerometer, Actuator Module (17), Feedback Sensor (13), Indicator (12), and Power Supply (14); wherein Vibration and acceleration data are collected, analyzed, and interpreted for proactive maintenance and improved operational efficiency.
2. The system as claimed in claim 1, wherein the central processing unit, the Raspberry Pi Processor Board, coordinates data gathering, analysis, and communication; this makes it easier to integrate sensor data from Two-Slide Die-Casting Machines with cloud computing and cutting-edge machine learning algorithms for real-time monitoring and AI-based recommendations in the electronics manufacturing industry.
3. The system as claimed in claim 1, wherein the built-in GSM modem in the TSDCM_EMVAMote provides smooth cellular connectivity, enabling the device to send acceleration and vibration data to a customized cloud server; and this guarantees real-time tracking of Two-Slide Die-Casting Machines in the electronics manufacturing industry and enables timely AI-based recommendations and alerts.
4. The system as claimed in claim 1, wherein essential vibration and acceleration data from Two-Slide Die-Casting Machines are provided by the Vibration Sensor and 3 Axis Accelerometer, both of which are connected in TSDCM_EMVAMote; and which enables thorough monitoring and analysis for the creation of real-time AI-based recommendations and alerts in the electronics manufacturing industry.
5. The system as claimed in claim 1, wherein the Actuator Module connected to TSDCM_EMVAMote is used to put AI recommendations into practice, allowing proactive maintenance and adjustments for Two-Slide Die-Casting Machines in the electronics manufacturing industry; and this results in decreased downtime and increased operational efficiency.
6. The system as claimed in claim 1, wherein the Feedback Sensor, which is attached to the TSDCM_EMVAMote, records the results of the actions performed by the Actuator Module; and this data is essential for the monitoring system for Two-Slide Die-Casting Machines in the electronics manufacturing industry to be continuously optimized, improved, and refined.
7. The system as claimed in claim 1, wherein through data collection, analysis, and AI-based recommendations, the Power Supply plugin in TSDCM_EMVAMote supports the seamless monitoring of Two-Slide Die-Casting Machines in electronics manufacturing by ensuring the continuous and reliable operation of the device and supplying the necessary electrical power for all components.

Documents