CLOUD AND NRF-BASED HEALTH MONITORING ADD-ON FOR SERVO INJECTION MOLDING MACHINE IN MEDICAL EQUIPMENT MANUFACTURING

Abstract

This invention discloses a cloud and nRF-based health monitoring system for Servo Injection Molding Machines in medical equipment manufacturing. The system comprises a TCDSIM_MEMote (100) module for data acquisition and transmission, and a RCDSIM_MEMote (110) module for user interface and remote monitoring. The TCDSIM_MEMote (100) uses an nRF Module with Patch (100A) for wireless data transmission to a cloud server, while the RCDSIM_MEMote (110) uses an nRF Module with Patch (110B) and a GSM Modem (110A) for remote access and real-time data display via its HMI Display (110D). This system enables proactive maintenance, improved operational control, and comprehensive health monitoring of the Servo Injection Molding Machine.

Specification

Description:FIELD OF THE INVENTION
This invention relates to a Cloud and nRF-Based Health Monitoring Add-On for Servo Injection Molding Machine in Medical Equipment Manufacturing
BACKGROUND OF THE INVENTION
The issue we have is that the medical equipment manufacturing industry does not adequately monitor the condition of its servo injection molding machines. The absence of real-time functionality and comprehensive data analysis in conventional monitoring systems leads to inefficiencies, unplanned downtime, and possible quality issues in the manufacturing workflow. Without a strong system, operators struggle to find and fix important problems in a timely manner, which increases maintenance costs and reduces the overall efficiency of the equipment.
EP0894604B1 - This patent describes an injection mold design, injection molding machine, and method for forming multi-layer plastic articles through over-molding. The machine allows different geometrical profiles in each layer by using composite cavities with movable cavity extensions that engage during the second layer's formation, creating profiles such as threads. The innovative design enables precise control over layer profiles in complex over-molded parts.
Research Gap: The novelty here is a wireless health monitoring solution using nRF and cloud technology, specifically for servo injection molding machines used in medical equipment manufacturing.
PH12016500204A1 - This invention relates to a method and machine for adjusting to changes in molten plastic material properties during injection molding. A controller detects changes in material flowability and modifies the target injection pressure to ensure complete mold filling and prevent flaws like short shots or flashing.
Research Gap: The innovation introduced here is a wireless solution using nRF and cloud technology to monitor the health of servo injection molding machines, tailored for applications in medical equipment manufacturing.
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 ground-breaking development changes the way that medical equipment is produced using Servo Injection Molding Machines for health monitoring. It has a sophisticated system that combines wireless communication options with cloud-based IoT technology. Through the intelligent placement of sensors, the system is able to obtain critical machine data, including temperature, vibration, and current levels.
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.
This cutting-edge health monitoring system offers a complete and user-friendly solution for monitoring Servo Injection Molding Machines in the field of producing medical equipment by seamlessly integrating IoT-based cloud technology, nRF-based remote solutions, and machine learning algorithms. The two primary modules of the system, TCDSIM_MEMote and RCDSIM_MEMote, combine powerful hardware and cloud-based technologies. A central Raspberry Pi Processor Board in the TCDSIM_MEMote module manages the integration of many sensors, such as a temperature, vibration, and current sensor. When combined, these sensors allow the Servo Injection Molding Machine to provide crucial data points. Wireless connectivity is facilitated by a nRF Module with Patch, which sends the gathered data to a cloud server created especially for this innovation. The system's foundation is this cloud architecture, which makes data storage, retrieval, and analysis effortless.
In order to assure continuous operation and offer real-time feedback, the TCDSIM_MEMote module includes an indicator and power supply simultaneously. With its GSM modem, HMI display, and power supply, the RCDSIM_MEMote module is a perfect match for the TCDSIM_MEMote. Beyond local borders, communication is made possible via the GSM modem, and important data and alarms are presented on the HMI display, which functions as an operator interface. The primary purpose entails the Servo Injection Molding Machine being continuously monitored, with sensor data being transferred to the cloud server. In order to produce outputs relating to hydraulic oil conditions, such as Trending Data Charts, Real-Time Data, and Critical Alerts, predefined machine learning algorithms analyze the data. Through a local web dashboard and a customized user interface on the HMI Display, operators within the industry premises can access this important information. Operators may monitor the health of the machine, evaluate real-time performance indicators, and get instant warnings for any serious faults or abnormalities found by login into their accounts.
BEST METHOD OF WORKING
1. Real-time data is collected and transmitted from a Servo Injection Molding Machine to a customized cloud server via the TCDSIM_MEMote, which is outfitted with a Raspberry Pi Processor Board, nRF Module with Patch, Temperature Sensor, Vibration Sensor, Current Sensor, Indicator, and Power Supply. This allows for thorough health monitoring and analysis for proactive maintenance during the medical equipment manufacturing process.
2. The RCDSIM_MEMote is used to give operators a user interface and guarantee seamless real-time monitoring of critical data and alerts related to the Servo Injection Molding Machine in the manufacturing of medical equipment. It is equipped with a Raspberry Pi Processor Board, nRF Module with Patch, GSM Modem, HMI Display, and Power Supply.
3. The nRF Module with Patch, which is built into both of the motes, is utilized to enable wireless communication. This makes it easier for vital real-time data to be transmitted from sensors on the Servo Injection Molding Machine to the customized cloud server, guaranteeing streamlined connectivity and data flow for thorough health monitoring in the production of medical equipment.
4. The Temperature, Vibration, and Current sensors are all integrated into TCDSIM_MEMote. Together, these sensors are used to gather important data points from the Servo Injection Molding Machine, allowing proactive maintenance during the medical equipment manufacturing process and enabling comprehensive health monitoring through real-time data analysis.
5. The RCDSIM_MEMote's integrated GSM modem is used to enable remote communication and smooth data transmission across local boundaries. This allows operators to monitor the health of the Servo Injection Molding Machine in real time during the manufacturing process of medical equipment and receive critical alerts.
6. To improve operational control and decision-making in the manufacturing of medical equipment, the RCDSIM_MEMote-interfaced HMI Display gives operators real-time insights and an intuitive platform to monitor and interpret vital data related to the Servo Injection Molding Machine.
ADVANTAGES OF THE INVENTION
1. The ability of TCDSIM_MEMote to gather and send real-time data from a Servo Injection Molding Machine to a customized cloud server is essential to this breakthrough. This feature facilitates thorough health monitoring and analysis for preventative maintenance during the production of medical equipment.
2. The RCDSIM_MEMote plays a key role in this breakthrough by enabling remote connectivity, offering an operator interface, and guaranteeing smooth real-time monitoring of vital data and warnings related to the Servo Injection Molding Machine in the production of medical equipment.
3. The invention relies heavily on the nRF Module with Patch, which makes wireless communication possible. It makes it easier for vital real-time data to be transmitted from the Servo Injection Molding Machine's sensors to the customized cloud server, guaranteeing constant connectivity and data flow for thorough health monitoring in the production of medical equipment.
4. The Temperature Sensor, Vibration Sensor, and Current Sensor are integrated into the innovation and serve vital roles by collecting important data points from the Servo Injection Molding Machine together. This procedure makes proactive maintenance in the production of medical equipment possible and permits thorough health monitoring through real-time data analysis.
5. A key component of this innovation is the HMI Display, which provides operators with a user-friendly platform and real-time insights to monitor and comprehend vital information about the Servo Injection Molding Machine. This improves decision-making and operational control in the production of medical equipment.
, Claims:1. A Cloud and nRF-Based Health Monitoring Add-On for Servo Injection Molding Machine in Medical Equipment Manufacturing, comprising the TCDSIM_MEMote (100), which includes a Raspberry Pi Processor Board (100G), nRF Module with Patch (100A), Temperature Sensor (100F), Vibration Sensor (100E), Current Sensor (100D), Indicator (100B), and Power Supply (100C), for collecting and transmitting real-time data from a Servo Injection Molding Machine to a customized cloud server for proactive maintenance during the medical equipment manufacturing process.
2. The system, as claimed in Claim 1, further comprising a RCDSIM_MEMote (110) module, which includes a Raspberry Pi Processor Board (110E), nRF Module with Patch (110B), GSM Modem (110A), HMI Display (110D), and Power Supply (110C); said module providing operators with a user interface and seamless real-time monitoring of critical data and alerts related to the Servo Injection Molding Machine.
3. The system, as claimed in Claim 1, wherein the nRF Module with Patch (100A and 110B), integrated into both the TCDSIM_MEMote (100) and RCDSIM_MEMote (110) modules, enables wireless communication for transmitting vital real-time data from the Servo Injection Molding Machine to the customized cloud server, ensuring streamlined connectivity and data flow for thorough health monitoring.
4. The system, as claimed in Claim 1, wherein the Temperature Sensor (100F), Vibration Sensor (100E), and Current Sensor (100D), integrated into the TCDSIM_MEMote (100) module, collect important data points from the Servo Injection Molding Machine, enabling proactive maintenance and comprehensive health monitoring through real-time data analysis.
5. The system, as claimed in Claim 2, wherein the RCDSIM_MEMote (110) module's integrated GSM Modem (110A) enables remote communication and smooth data transmission, allowing operators to monitor the health of the Servo Injection Molding Machine in real-time and receive critical alerts.
6. The system, as claimed in Claim 2, wherein the RCDSIM_MEMote (110) module's HMI Display (110D) provides operators with real-time insights and an intuitive platform to monitor and interpret vital data related to the Servo Injection Molding Machine, improving operational control and decision-making.

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