PREDICTIVE MAINTENANCE AND RECOMMENDATION SYSTEM FOR AUTOMOTIVE METAL STAMPING MACHINES USING MACHINE LEARNING AND LORAWAN TECHNOLOGY

Abstract

A predictive maintenance and recommendation system for automotive metal stamping machines using machine learning and lorawan technology comprises MetalShapeIoT (100) satellite, which comprises of an STM32 Board (101), LoRaWAN Module (102), Pressure Sensor (103), Temperature Sensor (104), Vibration Sensor (105), Actuator (107) and Power Supply (106), allows real-time data collection and automatic feedback which enables the sending of vital information, the state of the machine via the cloud server with the aim of improving maintenance and effectiveness of metal stamping operations the MetalShapeIoT Node, incorporating an STM32 Board, LoRaWAN Module, Pressure Sensor, Temperature Sensor, Vibration Sensor, and Actuator, expands functionality because there is the possibility for continuous condition monitoring and stopping the work process when something is wrong, ensuring that conditions for developing potential faults are quickly managed to avoid machine damage.

Specification

Description:FIELD OF THE INVENTION
This invention relates to predictive maintenance and recommendation system for automotive metal stamping machines using machine learning and lorawan technology.
BACKGROUND OF THE INVENTION
This system is meant for determining when to perform maintenance on auto metal stamping machines. The system is supposed to keep a watch on essential parameters of the machines and maintain efficiency. Thermographs, pressure sensors and accelerometers are used by the thermographic analysis system to gather information on the operational condition of the machines in order to detect anomalies. The information is collected wirelessly - it is sent to the control unit that forwards the information to a cloud server for advanced analytics, management by AI, and data generation with further predictive maintenance procedures. The operators are able to access the current status and warnings regarding those parameters through the local screen device or off a web page; thus they are able to respond as soon as the need arises. In case a major difference is sensed, the actuator in turn will cut off the power supply to the machine which will enhance productivity by minimizing downtime.
This project tackles the essential problem of metal stamping in the automotive industry, which is a need for predictive maintenance to avoid unplanned machine down time, unplanned productivity loss and safety issues. Old maintenance practices do not take issues of wear and tear out of machines and wait for the machine to malfunction or a schedule to change. It is now possible with this development to detect problems before they occur by continuously monitoring operational parameters including pressure, temperature, and vibration. Predictive models within the system along with automatic closing of machines will treat maintenance as a responsive action rather than pre-emptive action therefore maintenance will enhance the life of machines, improve operations by reducing unforeseen stoppages and making the manufacturing facilities efficient.
CN102756251B: The invention relates to a high-strength steel plate hot-stamping molding production line and production process thereof. The high-strength steel plate hot-stamping molding production line is mainly composed of a 2.6*1800 mm coil feed line unit, a blanking stacking unit, a stack breaking target hitting unit, a 20m heating furnace unit, a fast loading unit, a 800t pressing unit, a fast blanking unit, a 1000*1800mm laser cutting unit and a 1200*2000mm shot blasting fat liquoring. Each unit has a programmable logic controller (PLC) control system, and the PLC control system of each unit is connected with a production line main station PLC control system through a PROFIBUS industrial bus. The production process includes uncoiling, blanking, pre-molding, breaking stacks, detecting double sheets, centering and entering the furnace, heating austenite evenly, taking out from the furnace fast, feeding materials to a pressing machine, stamping and quenching, blanking, cutting and liquoring fat. The high-strength steel plate hot-stamping molding production line and the production process has the advantages of optimizing configuration and automation of the production line, improving production efficiency, and enlarging an application range of the production line.
RESEARCH GAP: The novelty of this system is its LoRaWAN-based predictive maintenance for automotive metal stamping machines, integrating real-time sensor data, automated machine halting, and cloud-based AI analytics for proactive machine management.
CN109759510B: The invention belongs to the technical field of dies, and particularly relates to an automobile sheet metal stamping die; the punching die comprises a bottom plate, a base, a lower die holder, an upper punching die, a top plate, a first air cylinder, a supporting unit, a material ejecting unit, a pushing unit and a controller; the punching die is fixedly connected to the top plate through a first air cylinder, and racks are arranged at two ends of the upper punching die; the base is fixedly arranged on the bottom plate; the lower die holder is fixedly connected to the bottom of the base through a supporting unit, and a T-shaped hole is formed in the lower die holder; the ejection units are respectively positioned in the T-shaped holes and comprise ejection columns and U-shaped supports; the pushing unit comprises a first gear, a swing rod, a first rod, a T-shaped rod and a wedge-shaped block; the invention utilizes the movement of the upper punch die in the vertical direction to enable the ejection column to move in the vertical direction, and the punched automobile metal plate is ejected out of the lower die holder through the movement of the ejection column, so that the phenomenon that the lower die holder is integrally ejected by using power equipment with higher power is avoided, and one source is multipurpose, thereby reducing the manufacturing cost of the stamping die.
RESEARCH GAP: The novelty of this system is its LoRaWAN-based predictive maintenance for automotive metal stamping machines, integrating real-time sensor data, automated machine halting, and cloud-based AI analytics for proactive machine management.
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.
For this purpose, specific machine parameters including pressure, temperature and vibration are constantly being tracked. These variables are recorded in specialized sensors mounted on the machine and they provide real time information about the operational condition of the machine. This information is processed internally in the systems in order to detect possible concern areas such as wear, fault or malfunction of machine components. If any harmful values are recorded, this information will be sent to the control unit for further processing and rectification actions. The control unit can communicate wirelessly relativelly easily which means it can send the eached data to the cloud. After being uploaded to the server, the data is then processed through machine learning models which correlate actual numbers with previous statistics to look for similarities and threats. Then, proactive maintenance alerts and strategies are devised by the system so that operators target breakdowns before they occur. In this way, the system provides sophisticated diagnostics and maintenance strategies that go beyond normal calendar maintenance and this in turn improves the operational efficiency of the stamping machines.
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.
For this purpose, specific machine parameters including pressure, temperature and vibration are constantly being tracked. These variables are recorded in specialized sensors mounted on the machine and they provide real time information about the operational condition of the machine. This information is processed internally in the systems in order to detect possible concern areas such as wear, fault or malfunction of machine components. If any harmful values are recorded, this information will be sent to the control unit for further processing and rectification actions. The control unit can communicate wirelessly relativelly easily which means it can send the eached data to the cloud. After being uploaded to the server, the data is then processed through machine learning models which correlate actual numbers with previous statistics to look for similarities and threats. Then, proactive maintenance alerts and strategies are devised by the system so that operators target breakdowns before they occur. In this way, the system provides sophisticated diagnostics and maintenance strategies that go beyond normal calendar maintenance and this in turn improves the operational efficiency of the stamping machines.
The system has a local display and web-based dashboard which allows operators and other authorized personnel to carry out cloud based analysis as well. Such a display goes several steps ahead of the conventional system, providing instant alerts regarding the status of the machine and relevant notifications in real-time. The actuator of the system switches off the machine if an anomaly crosses a dangerous level to protect the machine and personnel from encountering risks and accidents. Hence, allowing for timely maintenance based on data generated from the equipment, coupled with predictive capabilities and automatic responses to safety, allows this system to ensure that machines are fully utilized.
BEST METHOD OF WORKING
MetalShapeIoT satellite, which comprises of an STM32 Board, LoRaWAN Module, Pressure Sensor, Temperature Sensor, Vibration Sensor, Actuator and Power Supply, allows real-time data collection and automatic feedback which enables the sending of vital information, the state of the machine via the cloud server with the aim of improving maintenance and effectiveness of metal stamping operations.
The MetalShapeIoT Node, incorporating an STM32 Board, LoRaWAN Module, Pressure Sensor, Temperature Sensor, Vibration Sensor, and Actuator, expands functionality because there is the possibility for continuous condition monitoring and stopping the work process when something is wrong, ensuring that conditions for developing potential faults are quickly managed to avoid machine damage.
The LoRaWAN module, located within the MetalShapeIoT Node and the Control Node, enables transmitting machine data wirelessly over long distances, allows authorized users to oversee the condition of a stamping machine from a distance thereby making it possible to reduce the time an operation takes and the need to be physically present.
A Display Interface within the Control Node, which is assisted with Raspberry Pi Zero W, allows for the site operators to view the machine state in real time with alerts on relevant conditions thus improving the machine status awareness and allowing for maintenance to be carried out in anticipation in a situation whereby the factory faces a high volume of demand.
The Control Node GSM Modem allows for is also seamless and allows for remote data uploads to the designed cloud server and web access to the machine data for the purpose of predictive analytics that improve machine availability and help with maintenance planning.
ADVANTAGES OF THE INVENTION
1. Thanks to the Pressure Sensor, Temperature Sensor and Vibration Sensor incorporated in the MetalShapeIoT Node, this system enables monitoring of key parameters in a constant mode, thus detecting possible problems at incipient stages enabling proactive measures.
2. With MetalShapeIoT Node made with an Actuator, upon sensing critical differences, the machine can immediately stop thus ensuring protection of the operator and the machine.
3. Operating LoRaWAN Module in both MetalShapeIoT Node and Control Node, the system uploads and streams data in real time to cloud server over GSM Modem or WiFi for automated machine learning analytics and predictive maintenance information thereby enabling the operators to maintain machines from wherever they are situated.
4. The Control Nodes comprises the Raspberry Pi Zero W and Display which allows for local charting of data while the web dashboard is useful for remote access by higher authorities thus increasing the efficiency of day to day operations.
5. By taking advantage of predictive information available on the custom cloud server integrated with the machine learning model and timely detection of anomalies, the system prevents sudden breaks downs which leads to longer machine wear and less maintenance costs.
, Claims:1. A predictive maintenance and recommendation system for automotive metal stamping machines using machine learning and lorawan technology comprises MetalShapeIoT (100) satellite, which comprises of an STM32 Board (101), LoRaWAN Module (102), Pressure Sensor (103), Temperature Sensor (104), Vibration Sensor (105), Actuator (107) and Power Supply (106), allows real-time data collection and automatic feedback which enables the sending of vital information, the state of the machine via the cloud server with the aim of improving maintenance and effectiveness of metal stamping operations.
2. The system as claimed in claim 1, wherein the MetalShapeIoT Node, incorporating an STM32 Board, LoRaWAN Module, Pressure Sensor, Temperature Sensor, Vibration Sensor, and Actuator, expands functionality because there is the possibility for continuous condition monitoring and stopping the work process when something is wrong, ensuring that conditions for developing potential faults are quickly managed to avoid machine damage.
3. The system as claimed in claim 1, wherein the LoRaWAN module, located within the MetalShapeIoT Node and the Control Node, enables transmitting machine data wirelessly over long distances, allows authorized users to oversee the condition of a stamping machine from a distance thereby making it possible to reduce the time an operation takes and the need to be physically present.
4. The system as claimed in claim 1, wherein a Display Interface within the Control Node, which is assisted with Raspberry Pi Zero W, allows for the site operators to view the machine state in real time with alerts on relevant conditions thus improving the machine status awareness and allowing for maintenance to be carried out in anticipation in a situation whereby the factory faces a high volume of demand.
5. The system as claimed in claim 1, wherein the Control Node GSM Modem allows for is also seamless and allows for remote data uploads to the designed cloud server and web access to the machine data for the purpose of predictive analytics that improve machine availability and help with maintenance planning.

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