WORKFORCE EFFICIENCY MONITORING OF CRACKER MILLS MACHINES IN THE TIRE SHREDDER INDUSTRY USING ML ALGORITHMS WITH AI SUGGESTIONS

Abstract

Workforce efficiency monitoring of cracker mills machines in the tire shredder industry using ml algorithms with ai suggestions comprises WEMC_MMCNode (50), Banana Pi Router Board (100), Neural Stick (55), Touch HMI Display (60), Actuator (95), RTC Module (65), Temperature Sensor (80), Pressure Sensor (85), Vibration Sensor (90), Feedback Sensor (70), and Power Supply (75) is used as a comprehensive monitoring and control node to maximize worker productivity in the tire shredder industry, it does this by recording sensor parameters, logging machine schedules, and delivering AI-driven recommendations through user interfaces such as the Touch HMI Display, Tailored Web Dashboard, and Mobile App. The through cloud servers and user interfaces, the Banana Pi Router Board integrated into the WEMC_MMCNode facilitates data transfer and networking, enabling the WEMC_MMCNode to track machine schedules, communicate sensor data, and receive AI-driven suggestions.

Specification

Description:FIELD OF THE INVENTION
This invention relates to workforce efficiency monitoring of cracker mills machines in the tire shredder industry using ml algorithms with ai suggestions.
BACKGROUND OF THE INVENTION
This state-of-the-art system, which continuously monitors the operating schedules of cracker mill equipment, is essential to increasing staff productivity within the tire shredder business. The system records large amounts of data into a customized cloud server by using a series of sensors to measure important characteristics such as temperature, pressure, and vibration. Sophisticated algorithms for machine learning examine this data, estimating work hours and total productivity while accounting for extra input from authorized operators. AI-driven recommendations are generated by the system to maximize performance. These recommendations provide useful information for both automated and manual improvements.
The tire shredder sector faces a significant difficulty in effectively managing and optimizing worker productivity on cracker mill machines. Full operational performance optimization in the sector is hampered by the lack of a well-organized system for tracking machine on-off schedules, as well as by the absence of real-time data analysis and actionable insights. It is often difficult to correctly calculate working hours and assess overall efficiency due to the lack of full data integration in current monitoring solutions. Human mistake and inefficiencies can occur during manual data entry and decision-making procedures, which can lead to inefficient use of resources.
US20130193245A1: The apparatus and method produces fine mesh crumb rubber and provides for independently driving two parallel rolls, with one roll turning at tip speeds far above conventional cracker mills. Turning the roll at a rate (rpms) that results in "hyper" outer roll surface speeds between 1000 ft/min and 1300 ft/min., which is four and a half times the normal maximum speed of conventional mills yields several unexpected and beneficial results. The previously expected effects of operating at surface speeds about 400 ft/min are reduced or eliminated.
RESEARCH GAP: AI based Recommendation system for Workforce Efficiency Monitoring of Cracker Mills Machines is the novelty of the system.
WO1995004640A1: A process for the separation of metal from a rubber tire, which comprises: a) cutting the tire into two pieces; b) pressing the pieces between rollers to devulcanize the rubber; c) subjecting the devulcanized rubber to a magnetic field while holding the devulcanized rubber stationary to draw the larger metal inclusion pieces from the rubber; and d) grinding the rubber into pieces for recycling the same; and an apparatus for treating vulcanized rubber.
RESEARCH GAP: AI based Recommendation system for Workforce Efficiency Monitoring of Cracker Mills Machines 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.
The WEMC_MMCNode serves as the hub of a complex system architecture that powers this breakthrough. The Banana Pi Router Board, Neural Stick, Touch HMI Display, Actuator, RTC Module, Temperature Sensor, Pressure Sensor, Vibration Sensor, Feedback Sensor, and Power Supply are among the hardware components that this node, which serves as the central component, combines. The tire shredder industry's cracker mill machines' on-off schedules are monitored by the system to commence its operation. An array of sensors, including temperature, pressure, vibration, and machine schedules, record data into a customized cloud server with the goal of fully capturing operational parameters to serve as the basis for further analytics. The computation of working hours is predicated on machine schedules that have been logged, and a thorough dataset is augmented with extra parameters provided by authorized operators. Then, machine learning algorithms evaluate the effectiveness of the task, taking into account variables such as the quantity of work, machine usage, and other performance indicators.
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 WEMC_MMCNode serves as the hub of a complex system architecture that powers this breakthrough. The Banana Pi Router Board, Neural Stick, Touch HMI Display, Actuator, RTC Module, Temperature Sensor, Pressure Sensor, Vibration Sensor, Feedback Sensor, and Power Supply are among the hardware components that this node, which serves as the central component, combines. The tire shredder industry's cracker mill machines' on-off schedules are monitored by the system to commence its operation. An array of sensors, including temperature, pressure, vibration, and machine schedules, record data into a customized cloud server with the goal of fully capturing operational parameters to serve as the basis for further analytics. The computation of working hours is predicated on machine schedules that have been logged, and a thorough dataset is augmented with extra parameters provided by authorized operators. Then, machine learning algorithms evaluate the effectiveness of the task, taking into account variables such as the quantity of work, machine usage, and other performance indicators.
The system is noteworthy for producing AI-powered suggestions that maximize productivity. Drawn from data analysis, these recommendations offer practical ideas for both human and automated improvements. Processed and stored, this important data is centrally located on the tailored cloud server. With a Touch HMI Display linked to the WEMC_MMCNode, the user interface is available to all and is quite varied. A collaborative approach to improving operational performance is fostered by the ability to display machine efficiency indicators and AI ideas through the use of a Tailored Web Dashboard and Tailored Mobile App, which also enable remote monitoring and control. The solution guarantees real-time data sharing by connecting the gadget to the internet, which enables quick decision-making. By combining IoT, ML, and AI technologies, a holistic solution is produced that actively promotes continuous improvement in the tire shredder industry while simultaneously tracking staff productivity.
BEST MOTHDO OF WORKING
1. Utilizing a range of sensors and machine learning algorithms, the WEMC_MMCNode-which includes a Banana Pi Router Board, Neural Stick, Touch HMI Display, Actuator, RTC Module, Temperature Sensor, Pressure Sensor, Vibration Sensor, Feedback Sensor, and Power Supply-is used as a comprehensive monitoring and control node to maximize worker productivity in the tire shredder industry. It does this by recording sensor parameters, logging machine schedules, and delivering AI-driven recommendations through user interfaces such as the Touch HMI Display, Tailored Web Dashboard, and Mobile App.
2. Through cloud servers and user interfaces, the Banana Pi Router Board integrated into the WEMC_MMCNode facilitates data transfer and networking, enabling the WEMC_MMCNode to track machine schedules, communicate sensor data, and receive AI-driven suggestions.
3. The WEMC_MMCNode's Neural Stick is utilized to improve processing power, allowing machine learning algorithms to be executed efficiently for real-time sensor data analysis and the creation of AI-driven recommendations that maximize worker productivity in the tire shredder sector.
4. The Touch HMI Display interfaced in WEMC_MMCNode is used to give operators and authorities real-time visibility into sensor data, machine schedules, and AI-generated recommendations, making it easier to monitor and make decisions for the tire shredder industry's optimization of workforce efficiency.
5. The RTC Module that is integrated in WEMC_MMCNode, is used to makes sure accurate time management, while the Temperature, Pressure, Vibration, and Feedback Sensors collectively record crucial the environment. and machine-specific data, enabling comprehensive monitoring for the WEMC_MMCNode to determine working hours, log parameters, and facilitate the machine learning algorithms in suggesting improvements for workforce efficiency in the Tire Shredder Industry.
6. The Power Supply included in this innovation is utilized to supply vital electrical energy to maintain the WEMC_MMCNode's continuous operation, guaranteeing unbroken data logging, sensor monitoring, and AI-driven workforce efficiency optimization in the tire shredder sector.
ADVANTAGES OF THE INVENTION
1. Using a variety of sensors and machine learning techniques, the WEMC_MMCNode serves as an all-encompassing monitoring and control node that improves worker productivity in the tire shredder sector. By capturing sensor parameters, documenting machine schedules, and providing AI-driven recommendations via user interfaces like the Touch HMI Display, Tailored Web Dashboard, and Mobile App, it accomplishes this.
2. The Neural Stick is integrated to enhance the WEMC_MMCNode's processing power, allowing machine learning algorithms to run more smoothly. This enhances worker productivity in the tire shredder industry by enabling real-time sensor data analysis and the creation of AI-driven recommendations.
3. The Touch HMI Display serves as an easy-to-use user interface that provides authorities and operators with real-time access to sensor data, machine schedules, and AI-generated recommendations. This makes it easier to analyze and make decisions that optimize worker efficiency in the tire shredder industry.
4. Accurate timekeeping is guaranteed by the RTC Module, and vital environmental and machine-specific data is collected by the Temperature, Pressure, Vibration, and Feedback Sensors taken together. The WEMC_MMCNode can compute working hours, log parameters, and use machine learning algorithms to suggest improvements to increase worker efficiency in the tire shredder industry thanks to this thorough monitoring.
, Claims:1. A system of Workforce efficiency monitoring of cracker mills machines in the tire shredder industry using ml algorithms with ai suggestions comprises WEMC_MMCNode (50), Banana Pi Router Board (100), Neural Stick (55), Touch HMI Display (60), Actuator (95), RTC Module (65), Temperature Sensor (80), Pressure Sensor (85), Vibration Sensor (90), Feedback Sensor (70), and Power Supply (75) is used as a comprehensive monitoring and control node to maximize worker productivity in the tire shredder industry, it does this by recording sensor parameters, logging machine schedules, and delivering AI-driven recommendations through user interfaces such as the Touch HMI Display, Tailored Web Dashboard, and Mobile App.
2. The system as claimed in claim 1, wherein through cloud servers and user interfaces, the Banana Pi Router Board integrated into the WEMC_MMCNode facilitates data transfer and networking, enabling the WEMC_MMCNode to track machine schedules, communicate sensor data, and receive AI-driven suggestions.
3. The system as claimed in claim 1, wherein the WEMC_MMCNode's Neural Stick is utilized to improve processing power, allowing machine learning algorithms to be executed efficiently for real-time sensor data analysis and the creation of AI-driven recommendations that maximize worker productivity in the tire shredder sector.
4. The system as claimed in claim 1, wherein The Touch HMI Display interfaced in WEMC_MMCNode is used to give operators and authorities real-time visibility into sensor data, machine schedules, and AI-generated recommendations, making it easier to monitor and make decisions for the tire shredder industry's optimization of workforce efficiency.
5. The system as claimed in claim 1, wherein The RTC Module that is integrated in WEMC_MMCNode, is used to makes sure accurate time management, while the Temperature, Pressure, Vibration, and Feedback Sensors collectively record crucial the environment, and machine-specific data, enabling comprehensive monitoring for the WEMC_MMCNode to determine working hours, log parameters, and facilitate the machine learning algorithms in suggesting improvements for workforce efficiency in the Tire Shredder Industry.
6. The system as claimed in claim 1, wherein The Power Supply included in this innovation is utilized to supply vital electrical energy to maintain the WEMC_MMCNode's continuous operation, guaranteeing unbroken data logging, sensor monitoring, and AI-driven workforce efficiency optimization in the tire shredder sector.

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