MACHINE LEARNING AND XBEE-ENABLED CENTRALIZED RECOMMENDATION SYSTEM FOR VIBRATION AND SHAKING ANALYTICS IN INDUSTRIAL SWIVEL HEAD MILLING MACHINES

Abstract

A machine learning and xbee-enabled centralized recommendation system for vibration and shaking analytics in industrial swivel head milling machines comprises Monitoring node consists of control unit, wireless RF communication module, vibration sensor (104), current sensor (103), displacement sensor (107), and indicator display device with an independent power supply (106) unit, which makes possible capturing of machine parameter on real time basis, this node provides accurate capture and transmission of machine health-related data to boost the industrial swivel head milling monitoring practice on a proactive level receiving node has a central control unit, wireless RF communication module, touch interface display, and does not rely to any external power source allowing real time display and interaction, this node allows the operators to receive up to date developments of the machine conditions as well as transfer data to the cloud for analysis to improve their availability and situational awareness in industrial settings.

Specification

Description:FIELD OF THE INVENTION
This invention relates to machine learning and xbee-enabled centralized recommendation system for vibration and shaking analytics in industrial swivel head milling machines.
BACKGROUND OF THE INVENTION
This development shows the conception of a structure that focuses on faster assessment of vibrations and shocks for use by an industrial swivel head milling machine and offers recommendations based on smart analytic algorithms. The system is comprised of a node that is mounted on the milling machine and is designed to record the parameters of its operation such as; the vibration, current and displacement data. This data is sent wirelessly to a receiving node for visualization on an interactive screen. All data is additionally pulled to the cloud where artificial intelligence patterns recognition, maintenance needed, and recommendations for improving the machines operation and durability are performed. This system provides improved accuracy of control and cost-effective approaches to predictive control in industrial practice.
The swivel head milling machine is an essential tool in precision engineering thus be used within various industrial contexts. However, for such machines, functional problems associated with excessive vibration or shaking, reduced tool life, degradation of product quality, and unforeseen breakdowns are common. The maintenance management approach that dominated in the recent past involves managing issues after they occur because there is no preventative check when machines are operating. This invention provides a solution to these concerns by providing continuous, real time, actionable intelligence across the critical operational metrics, making it easier to identify and resolve issues before they develop into more complex problems. This system employs machine learning that helps in providing predictive maintenance functionality which ultimately helps in reducing the costs incurred in maintenance, machine downtimes and improves productivity and quality enhancement in industrial milling operations.
CN210282607U: A machine head of numerical control equipment comprises a drilling unit and a milling unit; the drilling unit comprises a workpiece tracking and pressing mechanism of the drilling unit; the drilling unit tracking workpiece pressing mechanism comprises a first pressing plate pressing mechanism and a second pressing plate pressing mechanism which are used for pressing a workpiece when hole positions in the vertical direction are processed, a first pressing wheel pressing mechanism which is used for pressing the workpiece when horizontal hole positions on the rear side surface of the workpiece are processed, a second pressing wheel pressing mechanism which is used for pressing the workpiece when horizontal hole positions on the front side surface of the workpiece are processed, a third pressing wheel pressing mechanism which is used for pressing the workpiece when horizontal hole positions on the left side surface of the workpiece and vertical hole positions on the bottom surface of the workpiece are processed, a fourth pressing wheel pressing mechanism which is used for pressing the workpiece when horizontal hole positions on the right side surface of the workpiece are processed; the milling unit comprises a milling unit tracking workpiece pressing mechanism; the plate clamping device has the advantages that the plate can be well clamped and pressed in the machining process, so that the plate is not easy to deviate and deform.
RESEARCH GAP: Machine learning-driven predictive maintenance and real-time vibration analytics for industrial swivel head milling machines, enabled by XBee-based wireless data transmission, is the novelty of this system.
CN201776493U: The utility model relates to the field of machine tools, in particular to a high-rigid, high-precision numerically controlled engraving and milling machine capable of quickly replacing tools, which comprises a bed casting, a crossbeam casting, a carriage casting, a worktable, a headstock with a spindle, a tool magazine plate, a tool magazine bracket and an air cylinder; the crossbeam casting comprises a crossbeam body, crossbeam legs, an X-axis bearing block and an X-axis motor seat which are integrated; the tool magazine bracket is arranged on the crossbeam legs, the air cylinder is firmly connected with the tool magazine bracket, the air cylinder shaft of the air cylinder passes through the tool magazine bracket and is connected with the tool magazine plate, guide rails are arranged at the bottom of the tool magazine plate, and the tool magazine plate is connected with the tool magazine bracket through the guide rails; the tool magazine plate is provided with a spring clamping unit, and a limiting unit is arranged at the bottom of the tool magazine plate as well. The structure of the engraving and milling machine is simple, the engraving and milling machine is convenient to operate, positioning is accurate, tools can be quickly replaced, the quality of tool replacement can be effectively guaranteed, moreover, the precision and stability of the machine tool can be increased, the product-machining precision is increased, the rigidity of the machine tool is enhanced, the service life of the machine tool is prolonged, and the occupied floor area is reduced.
RESEARCH GAP: Machine learning-driven predictive maintenance and real-time vibration analytics for industrial swivel head milling machines, enabled by XBee-based wireless data transmission, is the novelty of this 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.
The main aim of this innovation is to assist industries in overcoming problems that result from unplanned machine breakdowns, substandard products and the excessive maintenance of milling machines. With the possibility for uninterrupted observation, this system provides operators and managers with a different perspective of the machine in their possession without having to go through extensive technical specifications. Maintenance issues are avoided with this method as well as server based analysis which assists in performing maintenance as well as how and when it should be done in simple terms so that the maintenance issues are avoided. The manufacture of machines and their various parts would be more economical because this innovative technique reduces machinery downtime and optimizes machine parts' durability as well as minimizes severe disruptions. This innovation is composed of a two-node system, and its constituents are intended for the monitoring and analysis of the critical parameters of the industrial swivel head milling machine. It was also mounted in the machine in the shape of a concept that includes microburst changes within the concerning parameters such as current within the machine and maximum vibration. These measurements are made using sensors whose frequency may encourage them targeting, microcharge as fluctuation expectancies within the units operational status require attention. The information collected at the monitoring node is sent over RF communication module wirelessly which allows for data transfer with low latency to the destination, which is an interactive interface that relays real-time metrics.
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.
The main aim of this innovation is to assist industries in overcoming problems that result from unplanned machine breakdowns, substandard products and the excessive maintenance of milling machines. With the possibility for uninterrupted observation, this system provides operators and managers with a different perspective of the machine in their possession without having to go through extensive technical specifications. Maintenance issues are avoided with this method as well as server based analysis which assists in performing maintenance as well as how and when it should be done in simple terms so that the maintenance issues are avoided. The manufacture of machines and their various parts would be more economical because this innovative technique reduces machinery downtime and optimizes machine parts' durability as well as minimizes severe disruptions. This innovation is composed of a two-node system, and its constituents are intended for the monitoring and analysis of the critical parameters of the industrial swivel head milling machine. It was also mounted in the machine in the shape of a concept that includes microburst changes within the concerning parameters such as current within the machine and maximum vibration. These measurements are made using sensors whose frequency may encourage them targeting, microcharge as fluctuation expectancies within the units operational status require attention. The information collected at the monitoring node is sent over RF communication module wirelessly which allows for data transfer with low latency to the destination, which is an interactive interface that relays real-time metrics.
The data is pushed to a tailored cloud environment with machine learning algorithms for the given industrial machinery once received. Patterns in the data that has been captured are determined by the AI. The AI detects wear, misalignment, or points in time where certain components would fail. Such analytics are filtered so that recommendations can be made, allowing operators to meet maintenance requirements without waiting until the machine fails and breaks down. Using this technical structure, it is possible to move smoothly from the machine's operating environment into the machine's analytics, allowing the two to be linked: machine data and machine intelligent decision-making. The interfaces of the system are intelligible so that the conclusions can be interpreted easily by technical and non-technical personnel alike, with operators being able to quickly and easily understand the recommendations made. Additionally, this solution's flexibility allows it to be installed on multiple machines in an industrial plant, which provides a consistent and centralized approach to machine health management. This new technology thus helps improve efficiency in operations and also promotes a predictive maintenance culture where decisions are made based on data not on emotion after the fact, reducing costs and increasing operational capability for industries.
BEST METHOD OF WORKING
Monitoring node consists of control unit, wireless RF communication module, vibration sensor, current sensor, displacement sensor, and indicator display device with an independent power supply unit, which makes possible capturing of machine parameter on real time basis. This node provides accurate capture and transmission of machine health-related data to boost the industrial swivel head milling monitoring practice on a proactive level.
Receiving node has a central control unit, wireless RF communication module, touch interface display, and does not rely to any external power source allowing real time display and interaction. This node allows the operators to receive up to date developments of the machine conditions as well as transfer data to the cloud for analysis to improve their availability and situational awareness in industrial settings.
The Wireless RF Communication Module is an integral part of both Monitoring and Receiving Nodes and enables efficient and low latency data exchange between the nodes. This module allows efficient communication of the data from the machine to the receiving interface for effective monitoring and data integrity in the industrial environment.
Cloud Analytics Platform which receives and utilizes data from the Receiving Node employs predictive analytics on operational data through machine learning algorithms. Based on the analyzed trends, the platform also offers maintenance recommendations and useful insights, addressing existing issues and avoiding potential problems in machine operation.
Monitoring Node's Vibration, Current and Displacement Sensing Components acquire the crucial information related to machine's dynamical and operational stability. These parameters allow mechanical and electrical parameters to be tight and quite high in measurements of precision and also of real time monitoring so that the faults can be detected and machine health can be maintained and increased in time.
ADVANTAGES OF THE INVENTION
1. Operating in real time with machine learning, the system facilitates predictive maintenance which allows operators to resolve problems before they escalate thus minimizing unanticipated outages.
2. In this regard, continuous monitoring of vital parameters such as vibration and displacement reduces the risk of extended exposure to any detrimental operating conditions and therefore improves the durability of machine components as well as overall equipment effectiveness.
3. The ability of the system to generate actionable results in real time assists in the timely decision making by operators which in turn makes it possible to produce finished products with relatively shorter machine idle times and greater manufacturing output.
4. Regular monitoring and regular maintenance avoid shifts in the performance of machines, which could adversely affect the quality of the products being manufactured. The system, as a result, performs a role in ensuring more uniform and accurate manufacturing outcomes by sustaining nay operational parameters that are within the designed levels.
5. The innovation cuts back on the expenses incurred for maintenance by reducing the prevalence of unscheduled repair work, while leveraging machine learning and cloud analytics smartly translates into eliminating manual diagnostics, as well as the associated losses of operating time, and the net effect is significant cost savings to industrial enterprises.
, Claims:1. A machine learning and xbee-enabled centralized recommendation system for vibration and shaking analytics in industrial swivel head milling machines comprises Monitoring node consists of control unit, wireless RF communication module, vibration sensor, current sensor, displacement sensor, and indicator display device with an independent power supply unit, which makes possible capturing of machine parameter on real time basis, this node provides accurate capture and transmission of machine health-related data to boost the industrial swivel head milling monitoring practice on a proactive level.
2. The system as claimed in claim 1, wherein receiving node has a central control unit, wireless RF communication module, touch interface display, and does not rely to any external power source allowing real time display and interaction, this node allows the operators to receive up to date developments of the machine conditions as well as transfer data to the cloud for analysis to improve their availability and situational awareness in industrial settings.
3. The system as claimed in claim 1, wherein the Wireless RF Communication Module is an integral part of both Monitoring and Receiving Nodes and enables efficient and low latency data exchange between the nodes, this module allows efficient communication of the data from the machine to the receiving interface for effective monitoring and data integrity in the industrial environment.
4. The system as claimed in claim 1, wherein cloud Analytics Platform which receives and utilizes data from the Receiving Node employs predictive analytics on operational data through machine learning algorithms, based on the analyzed trends, the platform also offers maintenance recommendations and useful insights, addressing existing issues and avoiding potential problems in machine operation.
5. The system as claimed in claim 1, wherein monitoring Node's Vibration, Current and Displacement Sensing Components acquire the crucial information related to machine's dynamical and operational stability, these parameters allow mechanical and electrical parameters to be tight and quite high in measurements of precision and also of real time monitoring so that the faults can be detected and machine health can be maintained and increased in time.

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