AI-BASED RECOMMENDATION SYSTEM FOR HORIZONTAL PRODUCTION MILLING MACHINE IN THE STEEL INDUSTRY USING 3-AXIS VIBRATION ANALYSIS

Abstract

An ai-based recommendation system for horizontal production milling machine in the steel industry using 3-axis vibration analysis comprises DAIBR_HPSNode (1000), which is outfitted with a BeagleBone Processor Board (1000G), GPU Stick (1000B), GSM Modem (1000A), 3 Axis MEMS Accelerometer (1000F), HMI Display (1000G), RTC Module (1000E), Indicator (1000C), and Power Supply (1000D) the BeagleBone Processor Board, which is integrated into the DAIBR_HPSNode, is utilized to enable real-time monitoring and analysis through Internet of Things technology by collecting and transmitting sensor data from Horizontal Production Milling Machines to the customized cloud server.

Specification

Description:FIELD OF THE INVENTION
This invention relates to ai-based recommendation system for horizontal production milling machine in the steel industry using 3-axis vibration analysis.
BACKGROUND OF THE INVENTION
This ground-breaking development transforms the monitoring and maintenance of horizontal production milling machines, which has a considerable positive impact on the steel industry. The system uses the most recent IoT, cloud, and artificial intelligence technologies to collect real-time data from sensors built into the milling machine, specifically through the use of a 3-axis accelerometer. This breakthrough provides a thorough and sophisticated approach to machine health monitoring, reducing downtime, increasing operational efficiency, and enabling data-driven decision-making for industry professionals.
One of the biggest challenges facing the steel industry is ensuring that Horizontal Production Milling Machines operate at optimal performance and are maintained. Traditional methods of maintenance and monitoring are ineffective and fail to take use of new technologies. Production disruptions and increased operating costs are caused by issues including unplanned downtime, inefficient resource allocation, and neglected maintenance.
CN102161159B: The invention relates to a numerically controlled machine tool, in particular to a vertical-horizontal combined machining centre, which is mainly used for solving the technical problems of the prior art that: a grinding head and a planer tool frame are arranged on a stand column of the machine tool, the machine tool cannot perform a feed movement transversely; a milling boring head, a tool holder, a grinding head and a planer tool rest arranged on a beam cannot carry out a precise feed movement up and down; and the machine tool structure cannot meet the requirement of efficient and precise processing. The machining centre comprises a base, wherein a fixed workbench is arranged in the centre of the base; the two ends of the base are movably connected with two sliding seats capable of moving horizontally through a base sliding device; the two sliding seats are movably connected with stand columns through sliding devices of the sliding seats, wherein one stand column is movably connected with a horizontal sliding saddle which is fixed with a horizontal spindle, while the other stand column is movably connected with a vertical sliding saddle which is fixed with a vertical spindle device; and the base sliding device, the sliding devices of the sliding seats and the stand column sliding devices are connected with a numerically controlled electric device.
RESEARCH GAP: AI based Recommendation system for Horizontal Production Milling Machine helps to maintain the health of machine is the novelty of the system.
CN101112745B: The present invention relates to a milling and drilling machine, including: a rigidity machine frame, which comprises an upper crossbeam; a processing unit, which can move by means of a motor along at least two coordinates axes at the front of the crossbeam, and is arranged with a working main axis of a rotary drive for exchanging tool; as well as a working table, which is installed at the front of the machine frame and moves horizontally along at least one coordinates axis by means of a motor, also comprises a table-board for gripping at least one working piece. According to the present invention, the machine frame comprises two rigidity side walls connecting with each other through the upper crossbeam and a front end.
RESEARCH GAP: AI based Recommendation system for Horizontal Production Milling Machine helps to maintain the health of machine 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 innovative development smoothly incorporates into the steel industry's current workflow in addition to using state-of-the-art technologies for data collecting, analysis, and communication. The invention adheres to a methodically planned sequence of actions. Its main function is to monitor and optimize milling machine performance through the seamless integration of IoT, cloud computing, and artificial intelligence. The milling machine provides data during the first operating period. Positioned strategically on the machine, the 3-axis MEMS Accelerometer records vibrations and provides important information on the operational dynamics of the machine. Then, this data is wirelessly sent to a customized cloud server by means of parts like the GPU Stick, GSM modem, BeagleBone Processor Board, and other pertinent components. Once on the cloud server, the data is processed and analyzed by a pre-programmed machine learning algorithm.
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 innovative development smoothly incorporates into the steel industry's current workflow in addition to using state-of-the-art technologies for data collecting, analysis, and communication. The invention adheres to a methodically planned sequence of actions. Its main function is to monitor and optimize milling machine performance through the seamless integration of IoT, cloud computing, and artificial intelligence. The milling machine provides data during the first operating period. Positioned strategically on the machine, the 3-axis MEMS Accelerometer records vibrations and provides important information on the operational dynamics of the machine. Then, this data is wirelessly sent to a customized cloud server by means of parts like the GPU Stick, GSM modem, BeagleBone Processor Board, and other pertinent components. Once on the cloud server, the data is processed and analyzed by a pre-programmed machine learning algorithm.
The machine's activity is analyzed by the algorithm using advanced approaches to find patterns, trends, and anomalies. This information is then used to generate real-time statistics and detailed trending data charts that show the machine's performance over time. AI-based suggestions are based on processed data and are distributed via multiple channels. The milling machine's HMI Display gives on-site operators instantaneous access to information on the machine's condition, facilitating well-informed decisions regarding its operation. In addition, a specialized mobile app and an internet-based online dashboard provide a more comprehensive user interface for remote milling machine monitoring and control. User accounts are used to control system access. Operators can log in to examine historical and real-time data, and AI-generated recommendations and suggestions are sent straight to the maintenance teams and authority via email notifications.
BEST METHOD OF WORKING
Real-time insights, maintenance recommendations for predictive maintenance, and remote monitoring for Horizontal Production Milling Machines in the steel industry are provided by cloud-based analytics and artificial intelligence through the DAIBR_HPSNode, which is outfitted with a BeagleBone Processor Board, GPU Stick, GSM Modem, 3 Axis MEMS Accelerometer, HMI Display, RTC Module, Indicator, and Power Supply.
The BeagleBone Processor Board, which is integrated into the DAIBR_HPSNode, is utilized to enable real-time monitoring and analysis through Internet of Things technology by collecting and transmitting sensor data from Horizontal Production Milling Machines to the customized cloud server.
The DAIBR_HPSNode's GPU Stick is integrated to speed up the AI algorithms' processing power, guaranteeing effective analysis of 3-axis vibration data from horizontal production milling machines and facilitating the prompt generation of actionable insights for the steel industry's optimal performance and maintenance.
The integrated GSM modem in DAIBR_HPSNode is used to enable real-time monitoring, enable timely AI-driven recommendations for the maintenance and operational efficiency of the steel industry, and facilitate the wireless transmission of sensor data from Horizontal Production Milling Machines to the customized cloud server.
In the steel industry's quest for improved operational efficiency and maintenance, the 3-axis MEMS Accelerometer, which is connected to DAIBR_HPSNode, is used to capture vibration data from Horizontal Production Milling Machines. This allows for precise and real-time machine behavior monitoring and serves as a crucial input for the AI-based recommendation system.
The HMI Display, which is interfaced on DAIBR_HPSNode, serves as an interface for on-site operators and gives them real-time insights into the operation of Horizontal Production Milling Machines. By displaying actionable data and recommendations based on artificial intelligence, it helps the steel industry make well-informed decisions and run more efficiently.
ADVANTAGES OF THE INVENTION
1. This innovation's central processing unit, the DAIBR_HPSNode, coordinates the smooth fusion of artificial intelligence, cloud-based analytics, and data from Internet of Things sensors. The objective of this integration is to provide the steel industry with real-time insights, remote monitoring capabilities, and recommendations for predictive maintenance for horizontal production milling machines.
2. The GPU Stick is essential to increasing the AI algorithms' processing capacity in this invention. Its main goal is to make sure that 3-axis vibration data from Horizontal Production Milling Machines is efficiently analyzed so that timely actionable insights may be generated for the steel industry's optimal performance and maintenance.
3. This innovation relies heavily on the GSM Modem as a communication link to enable wireless sensor data transmission from Horizontal Production Milling Machines to the customized cloud server. Its goal is to provide real-time monitoring and facilitate quick AI-driven recommendations, which will eventually improve maintenance and operational effectiveness in the steel sector.
4. A key component in gathering vibration data from horizontal production milling machines is the 3-axis MEMS Accelerometer. Because of its accuracy, machine behavior can be monitored in real time, which is an essential input for the AI-based recommendation system. This makes a major contribution to the steel industry's goal of improved maintenance and operating efficiency.
5. The HMI Display provides real-time insights into the performance of the Horizontal Production Milling Machines to operators on-site, acting as an interface. The steel sector benefits from its proactive contribution to informed decision-making and streamlined operations through the display of relevant data and AI-based recommendations.
, Claims:1. An ai-based recommendation system for horizontal production milling machine in the steel industry using 3-axis vibration analysis comprises DAIBR_HPSNode (1000), which is outfitted with a BeagleBone Processor Board (1000G), GPU Stick (1000B), GSM Modem (1000A), 3 Axis MEMS Accelerometer (1000F), HMI Display (1000G), RTC Module (1000E), Indicator (1000C), and Power Supply (1000D).
2. The system as claimed in claim 1, wherein the BeagleBone Processor Board, which is integrated into the DAIBR_HPSNode, is utilized to enable real-time monitoring and analysis through Internet of Things technology by collecting and transmitting sensor data from Horizontal Production Milling Machines to the customized cloud server.
3. The system as claimed in claim 1, wherein the DAIBR_HPSNode's GPU Stick is integrated to speed up the AI algorithms' processing power, guaranteeing effective analysis of 3-axis vibration data from horizontal production milling machines and facilitating the prompt generation of actionable insights for the steel industry's optimal performance and maintenance.
4. The system as claimed in claim 1, wherein the integrated GSM modem in DAIBR_HPSNode is used to enable real-time monitoring, enable timely AI-driven recommendations for the maintenance and operational efficiency of the steel industry, and facilitate the wireless transmission of sensor data from Horizontal Production Milling Machines to the customized cloud server.
5. The system as claimed in claim 1, wherein the steel industry's quest for improved operational efficiency and maintenance, the 3-axis MEMS Accelerometer, which is connected to DAIBR_HPSNode, is used to capture vibration data from Horizontal Production Milling Machines, this allows for precise and real-time machine behavior monitoring and serves as a crucial input for the AI-based recommendation system.
6. The system as claimed in claim 1, wherein the HMI Display, which is interfaced on DAIBR_HPSNode, serves as an interface for on-site operators and gives them real-time insights into the operation of Horizontal Production Milling Machines, by displaying actionable data and recommendations based on artificial intelligence, it helps the steel industry make well-informed decisions and run more efficiently.

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