FFT ANALYSIS, PREDICTIVE CONTROL AUTOMATION, AND MACHINE LEARNING FOR DUAL-AXIS METAL FABRICATION EDM MACHINES VIA MQTT CLOUD COMMUNICATION

Abstract

A fft analysis, predictive control automation, and machine learning for dual-axis metal fabrication edm machines via mqtt cloud communication comprises PiControlXNode (100) that constitutes Raspberry Pi Board (106), GSM modem (102), temperature sensor (104), vibration sensor (103), Analog Digital Converter module, Actuator (107) and Power Supply (105) is deployed in the distant monitoring of dual axis metal EDM machining operations and its efficient transmission to the cloud server for further analytical and predictive control in order to augment the efficiency and reliability of EDM processes the component incorporated in PiControlXNode that is a Raspberry Pi Board is employed as the main component that performs tasks of enabling FFT analysis, predictive control algorithms, and machine learning techniques, enabling real time surveillance and control on the performance of dual axis EDM machines.

Specification

Description:FIELD OF THE INVENTION
This invention relates to fft analysis, predictive control automation, and machine learning for dual-axis metal fabrication edm machines via mqtt cloud communication.
BACKGROUND OF THE INVENTION
The presented work is focused on the development of an intelligent system for two-axis metalworking EDM machines, where FFT analysis and machine learning-based control are proposed to improve the precision and efficiency of the machine. The EDM system incorporates both data capturing and processing through the cloud, allowing it to track KPIs as well as factors affecting the process. Using these scenarios, the system predicts the likelihood of operational failures and enables corrective actions to reduce machine idle times. This approach allows integrating vast amounts of data into the cloud, enabling quick control and making the system very suitable for precision industrial environments at high levels of automation and connectivity.
This invention resolves the high demand of improving the accuracy, dependability and predictive maintenance aspects of dual axis metal EDM machines which are subject to performance decline as a result of prolonged usage which causes wear and vibration as well as thermal factors during sheath operation. Manual Oversight and reactive maintenance can be time consuming and quite costly due to unscheduled downtimes and can also lower the quality of the products being manufactured. This solution utilizes real-time monitoring and predictive control, which leads to a reduction in machine downtimes, reduced costs on maintenance, and guarantees consistency in the quality of the output production. It even makes remote diagnosing and optimizing possible through the provision of data in the cloud and therefore meets the requirements for more effective, extensive as well as intelligent automation in metal fabrication.
US5940301A: The invention pertains to a method and a device for controlling a machine tool, in particular an EDM machine, wherein the control data necessary for machining one or more workpieces is fed from the control device to the machine tool. Therein a list of action data for controlling special procedures (actions) and a list of event data for characterizing prescribed operating states (events), in particular, malfunctions, are permanently provided. The action and event data are linked in setting up the machine such that upon occurrence of one or more events, one or more actions are performed.
RESEARCH GAP: A IoT and Cloud integrated solution for Health Monitoring and Predictive Maintenance for conventional Two-Axis Wire ED is the novelty of the system.
DE102020002692A1: A wire EDM machine (10) comprises a drive control device (70) which is set up to periodically apply voltage pulses between a workpiece (W) and a wire electrode (12) in accordance with a machining program and a machining condition and, at the same time, to move the wire electrode (12) relative to the workpiece (W), a code analyzer (72) which is adapted to analyze the presence or absence of a condition change code for changing the machining condition in the machining program, and a condition changing device (74) which is arranged to analyze the machining condition according to a specified in the condition change code Change ratio when condition change code is present.
RESEARCH GAP: A IoT and Cloud integrated solution for Health Monitoring and Predictive Maintenance for conventional Two-Axis Wire ED 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 invention aims to advance dual axis metal cutting EDM (Electrical discharge machining) construction by the integration of FFT (Fast Fourier Transform) analysis, predictive control and machine gouging algorithms for performance enhancement in real time. The system utilizes Fast Fourier Transform to continuously decompose the complex vibration signal into simpler signals in order to discern the frequency of the signal in order to evaluate the condition of the machine. A machine learning model recognizes these patterns and evaluates the risks of such problems emerging in the future, including tool degradation, drifting, and structural contention. The predictive control algorithm then modifies the operation parameters in order to anticipate such problems and decrease their severity, thereby improving the either precision or life of the machine. The connection between the machine and the cloud services is provided by MQTT, an easy and light messaging protocol that is perfect for real time IoT communications. With this configuration, data is transferred to the cloud seamlessly allowing it to be process, saved, and send to the authorized user for control and management, thus allowing scalability and central monitoring.
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 invention aims to advance dual axis metal cutting EDM (Electrical discharge machining) construction by the integration of FFT (Fast Fourier Transform) analysis, predictive control and machine gouging algorithms for performance enhancement in real time. The system utilizes Fast Fourier Transform to continuously decompose the complex vibration signal into simpler signals in order to discern the frequency of the signal in order to evaluate the condition of the machine. A machine learning model recognizes these patterns and evaluates the risks of such problems emerging in the future, including tool degradation, drifting, and structural contention. The predictive control algorithm then modifies the operation parameters in order to anticipate such problems and decrease their severity, thereby improving the either precision or life of the machine. The connection between the machine and the cloud services is provided by MQTT, an easy and light messaging protocol that is perfect for real time IoT communications. With this configuration, data is transferred to the cloud seamlessly allowing it to be process, saved, and send to the authorized user for control and management, thus allowing scalability and central monitoring.
The purpose of the hardware configuration applied in the system is to allow for efficient monitoring and data acquisition. Sensors collect measurable parameters like vibration and temperature which are important in determining the machine's condition. Signals from such sensors are wired to analog-to-digital converters which change them into digital form that is transferred to the primary control unit. In real-time, adjustable algorithms predict machine states. Detachable ports should be all functional so that data is sent out to the cloud through MQTT for improved remote assist diagnosis and management. The novelty is that it has a modular design that allows expansion in the future if more features or components are required. Unlike traditional EDM machines where operators watch closely the machine condition or make adjustments to the settings to avoid wear and breakdowns, there is no need to worry. With this system, the need to constantly monitor and manage machine performance is eliminated since performance data is collected automatically and analyzed on the go. It understands the vibrations, temperature and other variables to figure out when there could be a problem with the machine, and ensures that it performs any required maintenance before any issue takes place. This predictive ability means a reduction in downtime, lower maintenance expenses, and maintenance free period even with extensive production runs while maintaining optimal quality level.
There is more flexibility and ease in management because data is sent to the cloud, allowing the operators and technicians to evaluate the machine's performance and productivity from any location. It's a wise approach for manufacturers looking to optimize their business processes, as well as to keep their machines operating at - or near - full capacity with little involvement from the staff. The integration of the system's features with IoT and cloud easily brings it to the latest requirements of Industry 4.0, making it ideal for companies wanting to modernize their production processes with the most recent automated technologies.
BEST METHOD OF WORKING
PiControlXNode that constitutes Raspberry Pi Board, GSM modem, temperature sensor, vibration sensor, Analog Digital Converter module, Actuator and Power Supply is deployed in the distant monitoring of dual axis metal EDM machining operations and its efficient transmission to the cloud server for further analytical and predictive control in order to augment the efficiency and reliability of EDM processes.
The component incorporated in PiControlXNode that is a Raspberry Pi Board is employed as the main component that performs tasks of enabling FFT analysis, predictive control algorithms, and machine learning techniques, enabling real time surveillance and control on the performance of dual axis EDM machines.
The component integrated in PiControlXNode that is a GSM Modem is employed to provide effective remote communication connectivity allowing the transmission of real operational data of the EDM machines in the cloud and facilitate remote analysis and control what is needed in today's manufacturing environment.
High-Precision Vibration and Temperature Sensors: Sensors that provide measurements on the level of vibration and temperature which are important parameters that can enhance the understanding of how a machine operates and are used as inputs in predictive maintenance systems.
An Actuator that Modifies the Machine in Real-Time: An actuator that is capable of receiving control signals from the processing unit and serves the purpose of changing machine parameters to predicted values or adjusting them in cases, where performance target levels have not been attained.
ADC module: A high-performance and high-resolution SDR-based ADC continuously converts the and equally sampled output of an STI into a digital database for necessary signal processing by the control unit.
ADVANTAGES OF THE INVENTION
1. The machine performance variations are now easily identifiable using FFT analyses and machine learning, which allows for adjustments in the functionality of the processes. It is important for the quality of the output to be high, especially in metal fabrication, in order to eliminate the risk of errors and wastage.
2. Predictive Maintenance: The innovation has the breakthrough in not only predicting when something is worn out, but also predicting when potential problems will occur. This in turn limits unplanned downtimes. This predictive maintenance approach, on the other hand, lowers costs associated with maintenance and improves the reliability of machines overall.
3. Thanks to the MQTT-based cloud surface, it is easy to perform machine performance operations remotely and without a deadline, which facilitates the addressing of issues and reduces supervision demands.
4. Because of the automated monitoring and predictive control activities, manual intervention is greatly minimized which reduces the cost of labor and increases the lifespan of the machines. This creates a more resource efficient and cost-effective production process.
5. With its modular structure and IoT features, this system has an advantage over others because it can be easily extended to add more features and even other machines in the future. This is in line with the current innovative systems and should be noted as able to cater for future manufacturing demands that are not met today.
, Claims:1. A fft analysis, predictive control automation, and machine learning for dual-axis metal fabrication edm machines via mqtt cloud communication comprises PiControlXNode (100) that constitutes Raspberry Pi Board (106), GSM modem (102), temperature sensor (104), vibration sensor (103), Analog Digital Converter module, Actuator (107) and Power Supply (105) is deployed in the distant monitoring of dual axis metal EDM machining operations and its efficient transmission to the cloud server for further analytical and predictive control in order to augment the efficiency and reliability of EDM processes.
2. The machine as claimed in claim 1, wherein the component incorporated in PiControlXNode that is a Raspberry Pi Board is employed as the main component that performs tasks of enabling FFT analysis, predictive control algorithms, and machine learning techniques, enabling real time surveillance and control on the performance of dual axis EDM machines.
3. The machine as claimed in claim 1, wherein the component integrated in PiControlXNode that is a GSM Modem is employed to provide effective remote communication connectivity allowing the transmission of real operational data of the EDM machines in the cloud and facilitate remote analysis and control what is needed in today's manufacturing environment.
4. The machine as claimed in claim 1, wherein High-Precision Vibration and Temperature Sensors: Sensors that provide measurements on the level of vibration and temperature which are important parameters that can enhance the understanding of how a machine operates and are used as inputs in predictive maintenance systems.
5. The machine as claimed in claim 1, wherein an Actuator that Modifies the Machine in Real-Time: An actuator that is capable of receiving control signals from the processing unit and serves the purpose of changing machine parameters to predicted values or adjusting them in cases, where performance target levels have not been attained.
6. The machine as claimed in claim 1, wherein ADC module: A high-performance and high-resolution SDR-based ADC continuously converts the and equally sampled output of an STI into a digital database for necessary signal processing by the control unit.

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