MACHINE LEARNING-DRIVEN FLAP CONTROL SYSTEM FOR AUTOMATED BAG PRODUCTION

Abstract

Abstract The present disclosure provides an intelligent flap deployment system for a bag-making machine. The system includes a bottom folding drum to support and rotate a flat cylinder and guide plates arranged to unfold front and rear flaps of said flat cylinder. Predictive maintenance sensors monitor said guide plates to ensure continuous operational precision. A machine learning unit is operatively connected to said sensors to analyze data collected from the sensors. Based on the analyzed data, said guide plates dynamically adjust to maintain optimal flap positioning during production. Dated 11 November 2024 Jigneshbhai Mungalpara IN/PA- 2640 Agent for the Applicant

Specification

Description:Machine Learning-Driven Flap Control System for Automated Bag Production
Field of the Invention
[0001] The present disclosure generally relates to automated bag-making machinery. Further, the present disclosure particularly relates to an intelligent flap deployment system for controlling flap positioning in a bag-making machine.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] In the field of bag-making machines, automated systems have been developed to streamline the process of folding, positioning, and deploying flaps on bags during production. Conventional bag-making systems often rely on mechanical components to control flap folding and positioning. Such systems include rotating drums and stationary or fixed guide plates to control the movement and alignment of bag flaps. Further, these systems are typically operated by mechanical controls or basic automated adjustments to manage the folding process. However, traditional mechanical-only systems experience several challenges, including frequent wear and tear, high maintenance requirements, and difficulty in achieving consistent flap positioning under varied operating conditions. Such limitations often result in inconsistent output quality and lower production efficiency, particularly in high-speed operations.
[0004] Various prior art bag-making systems attempt to address these challenges by incorporating basic sensors that monitor the position and alignment of components. Said sensors may be placed to ensure correct folding alignment and minimize jamming or other mechanical disruptions. Nevertheless, such sensors in prior art systems are often limited to monitoring a fixed set of parameters, such as rotation speed and component positioning, without real-time adaptability to variations in machine performance or operational conditions. Consequently, such systems remain limited in terms of real-time response to changes in operating conditions, causing inaccuracies in flap alignment that can lead to operational inefficiencies and frequent interruptions.
[0005] Further, recent developments in automation for bag-making systems have included rudimentary data analysis capabilities to provide basic diagnostics and predictive maintenance. Such systems attempt to reduce downtime by identifying potential maintenance needs before component failure. However, said data analysis functions generally remain limited to basic threshold-based monitoring, often lacking advanced capabilities to dynamically adjust operational parameters based on real-time data. As a result, conventional systems experience substantial downtimes and frequent manual interventions for recalibration and alignment. Further, predictive maintenance in such conventional systems remains largely reactive, often failing to adjust component settings proactively in response to minor variations that can otherwise optimize performance.
[0006] Other prior art approaches have integrated limited machine learning technologies to enhance system adaptability. Such machine learning integrations focus on analyzing historical data trends or recognising specific repetitive patterns. However, in conventional systems, machine learning remains primarily diagnostic, limited in predictive capability, and rarely enables the automated adjustment of machine components. Furthermore, traditional machine learning techniques in bag-making systems often lack sufficient precision in data analysis, resulting in limited adaptability to varied operating environments, which restricts continuous optimization of the system. Consequently, conventional systems fail to maintain optimal alignment of flaps, resulting in substantial material wastage, increased wear of machine components, and compromised productivity.
[0007] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for flap deployment and positioning in bag-making machines.
[0008] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[0009] It also shall be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00010] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] The present disclosure generally relates to automated bag-making machinery. Further, the present disclosure particularly relates to an intelligent flap deployment system for controlling flap positioning in a bag-making machine.
[00013] An objective of the present disclosure is to provide an intelligent flap deployment system to enhance flap positioning accuracy and maintenance efficiency in bag-making machines. The system of the present disclosure aims to streamline flap deployment operations while reducing misalignments and downtime.
[00014] In an aspect, the present disclosure provides an intelligent flap deployment system for a bag-making machine. Said system comprises a bottom folding drum to support and rotate a flat cylinder, guide plates to unfold front and rear flaps of said flat cylinder, predictive maintenance sensors to monitor said guide plates, and a machine learning unit operatively connected to said sensors to analyze sensor data. Based on analyzed data, said guide plates dynamically adjust to maintain optimal flap positioning during production.
[00015] Further, the system enables synchronized alignment of the bottom folding drum and guide plates, promoting precise flap deployment. Tangential placement of said guide plates relative to said bottom folding drum facilitates a smooth transition of flap unfolding, reducing resistance and maintaining alignment. Peripheral positioning of said predictive maintenance sensors provides cohesive monitoring of guide plate positioning, ensuring operational consistency. Additionally, said machine learning unit is in positional association with said sensors, enabling prompt guide plate adjustments to minimize downtime and streamline maintenance.
[00016] Furthermore, the guide plates are adjustably mounted on a radial axis relative to the bottom folding drum, enhancing positioning accuracy based on sensor-detected operational conditions. Angular positioning detectors in the bottom folding drum convey rotational data to the machine learning unit, optimizing drum precision for consistent flap alignment. A damping mechanism within said guide plates mitigates vibrations during high-speed operations, ensuring uninterrupted deployment. Real-time adjustment parameters in said machine learning unit allow sensor recalibration to improve operational accuracy based on machine dynamics. Said sensors include thermal regulators to monitor temperature variations, thereby preventing deformation in the guide plates that could affect flap positioning.
[00017]
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 illustrates an intelligent flap deployment system (100) for a bag-making machine, in accordance with the embodiments of the present disclosure.
[00020] FIG. 2 illustrates a class diagram of the intelligent flap deployment system (100) for a bag-making machine, in accordance with the embodiments of the present disclosure.
Detailed Description
[00021] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00022] The use of the terms "a" and "an" and "the" and "at least one" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term "at least one" followed by a list of one or more items (for example, "at least one of A and B") is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00023] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00024] The present disclosure generally relates to automated bag-making machinery. Further, the present disclosure particularly relates to an intelligent flap deployment system for controlling flap positioning in a bag-making machine.
[00025] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00026] As used herein, the term "intelligent flap deployment system" refers to a system integrated within a bag-making machine to automate and optimize the process of unfolding and aligning flaps on bags during production. Such a system incorporates advanced components to monitor and control the positioning of the flaps on bags, ensuring accuracy and consistency in the deployment of each flap. The system typically responds dynamically to operational data, allowing for real-time adjustments to accommodate changes in machine speed or material properties. The purpose of the intelligent flap deployment system lies in maintaining optimal flap positioning without frequent manual interventions, thereby streamlining production processes and reducing the chances of misalignment. Additionally, the intelligent flap deployment system is designed to sustain high-speed operations in bag-making machines while preserving accuracy and enhancing the production output quality. The system can also minimize maintenance interruptions through real-time monitoring and adjustment of its components, thereby extending the operational lifespan of the machine.
[00027] As used herein, the term "bottom folding drum" is used to refer to a component within the intelligent flap deployment system that serves as the primary support and rotation mechanism for the flat cylinder of the bag-making machine. The bottom folding drum is adapted to rotate at a specific speed, controlling the movement of the flat cylinder as the material progresses through the machine. The bottom folding drum assists in stabilizing the material by maintaining consistent rotation and alignment, thereby enabling a smooth and uninterrupted bag-making process. Furthermore, the bottom folding drum synchronizes with other components of the system to prevent fluctuations that may disrupt flap alignment. The rotation of the bottom folding drum is essential in creating a stable surface for accurate unfolding and deployment of flaps, making it a fundamental aspect of the intelligent flap deployment system in achieving uniformity and precision in the final bag product.
[00028] As used herein, the term "guide plates" refers to mechanical components in the intelligent flap deployment system that are responsible for unfolding the front and rear flaps of the flat cylinder as it moves through the bag-making machine. Said guide plates are positioned to interact with the material at specific points along its path, allowing the controlled deployment of each flap without misalignment. The guide plates operate by directing the movement of the flat cylinder at predetermined angles, ensuring a seamless and consistent transition of flap positions. Further, the guide plates are capable of making minor adjustments during operation based on real-time sensor data to maintain correct positioning despite variations in material thickness or machine speed. By aligning with the bottom folding drum, the guide plates prevent jamming or resistance that could impede the bag-making process, contributing to an efficient and accurate production cycle in high-speed settings.
[00029] As used herein, the term "predictive maintenance sensors" refers to sensors integrated within the intelligent flap deployment system to continuously monitor and assess the condition and alignment of the guide plates. Said predictive maintenance sensors are positioned strategically to detect changes in the operational state of the guide plates, providing real-time data on any deviations or wear that may occur during production. The predictive maintenance sensors are connected to a data analysis unit, allowing the collection and analysis of performance metrics to predict potential issues before they disrupt the production process. The sensors further enable the system to make adjustments to the guide plates as necessary to maintain consistent alignment and avoid misalignment or material wastage. The sensors are essential in maintaining smooth operations, particularly in high-speed production settings, by alerting the system to any need for adjustments or maintenance to prevent interruptions and extend component longevity.
[00030] As used herein, the term "machine learning unit" is used to refer to an analytical component in the intelligent flap deployment system that processes data received from the predictive maintenance sensors. Said machine learning unit is responsible for interpreting sensor data, identifying patterns, and determining adjustments required to maintain optimal guide plate positioning. The machine learning unit functions by continuously analyzing sensor data in real-time, enabling it to detect anomalies or shifts in performance that may affect flap deployment. Based on the analyzed data, the machine learning unit prompts adjustments in the guide plates to ensure consistent alignment and accuracy during production. The machine learning unit further aids in predicting maintenance needs by identifying trends in operational performance that may indicate wear or potential misalignment, allowing for proactive management of the system's components.
[00031] FIG. 1 illustrates an intelligent flap deployment system (100) for a bag-making machine, in accordance with the embodiments of the present disclosure. In an embodiment, a bottom folding drum 102 supports and rotates a flat cylinder within the intelligent flap deployment system 100 for a bag-making machine. The bottom folding drum 102 may comprise a cylindrical body with an external surface adapted to engage with the flat cylinder, thereby stabilizing and rotating said cylinder as it passes through the machine. The bottom folding drum 102 may be structured to rotate at a predetermined speed, where said speed may be variable based on the operational requirements of the bag-making machine. Additionally, the bottom folding drum 102 maintains the flat cylinder in a steady alignment as it rotates, providing an axis around which other components can function in synchrony. To facilitate this interaction, the bottom folding drum 102 may be positioned to allow the flat cylinder to rotate smoothly along its path without deviation. Further, said bottom folding drum 102 may include adjustable mounting points that allow for minor positional shifts to maintain alignment with guide plates 104. In an embodiment, the bottom folding drum 102 may be coupled with drive mechanisms or gears that regulate rotational forces, which synchronize with other components, including guide plates 104. Such synchronization allows the bottom folding drum 102 to prevent jamming, slippage, or fluctuations in movement that could otherwise impede the proper unfolding of flaps on the bags. The bottom folding drum 102 may be constructed from a durable material capable of withstanding high-speed rotation while preserving alignment and rotational consistency across multiple cycles, ensuring smooth, uninterrupted rotation of the flat cylinder during bag production.
[00032] In an embodiment, guide plates 104 are disposed within the intelligent flap deployment system 100 to unfold front and rear flaps of the flat cylinder as it rotates on the bottom folding drum 102. Said guide plates 104 may be arranged at strategic points along the rotational path of the flat cylinder to engage the material and initiate controlled unfolding of the flaps. The guide plates 104 may be mounted in alignment with the bottom folding drum 102, creating a synchronized path for the material to follow as it progresses through the machine. In an embodiment, the guide plates 104 may be tangentially positioned relative to the flat cylinder, providing a smooth transition point that facilitates accurate unfolding of each flap while preventing misalignment. The guide plates 104 may also be adjustably mounted on a radial axis, allowing for real-time adjustments based on the sensor data received from predictive maintenance sensors 106, further enabling said guide plates 104 to respond dynamically to changes in material properties, thickness, or machine speed. In an embodiment, the guide plates 104 may further include a damping mechanism that absorbs operational vibrations, thus maintaining stability even during high-speed production runs. The material and construction of the guide plates 104 may be selected to offer durability and minimal friction against the flat cylinder, allowing for consistent flap positioning and preventing wear during prolonged machine use. Guide plates 104 may additionally feature a contour or angled surface to guide the flaps into the unfolded position without resistance, ensuring that each flap maintains the correct alignment as it transitions along the path.
[00033] In an embodiment, predictive maintenance sensors 106 are integrated into the intelligent flap deployment system 100 to monitor guide plates 104 during the flap unfolding process. Predictive maintenance sensors 106 may be positioned peripherally around guide plates 104 and aligned along the rotational path of the bottom folding drum 102, allowing comprehensive monitoring of guide plate alignment, position, and operational stability. Said predictive maintenance sensors 106 may detect changes in alignment, resistance, or vibration levels associated with guide plates 104, providing real-time data regarding the operational state of the guide plates 104. In an embodiment, predictive maintenance sensors 106 are capable of measuring multiple parameters, including displacement, thermal variations, and vibrational frequencies. Such data may then be relayed to machine learning module 108 for analysis, providing critical insight into minor deviations in the operational performance of guide plates 104. Predictive maintenance sensors 106 may further feature self-calibrating capabilities that automatically adjust sensitivity based on production requirements, allowing for consistent monitoring under varied operational conditions. Additionally, predictive maintenance sensors 106 may incorporate thermal regulators that monitor and adjust to temperature fluctuations that could impact the material stability of guide plates 104, thereby minimizing thermal deformation risks during high-speed operations. Said predictive maintenance sensors 106 provide a basis for prompt adjustments to guide plates 104, ensuring alignment accuracy and continuity in flap deployment by identifying maintenance requirements before operational issues arise.
[00034] In an embodiment, machine learning module 108 is operatively connected to predictive maintenance sensors 106 within the intelligent flap deployment system 100 to analyze sensor data and adjust guide plates 104 based on analyzed data. Said machine learning module 108 interprets data from predictive maintenance sensors 106 to detect patterns, operational trends, or anomalies, thus identifying areas where adjustments to guide plates 104 may enhance alignment accuracy. In an embodiment, machine learning module 108 continuously processes real-time sensor data, allowing the intelligent flap deployment system 100 to respond proactively to operational changes and to perform adjustments on guide plates 104 dynamically. Machine learning module 108 may further employ data-driven analysis techniques that determine optimal positioning or adjustment parameters for guide plates 104 based on historical data trends or live operational metrics. In another embodiment, machine learning module 108 may recalibrate predictive maintenance sensors 106 periodically based on data fluctuations, optimizing sensor accuracy and reliability in detecting guide plate performance. Machine learning module 108 may further support real-time analysis of thermal data and positional data obtained from predictive maintenance sensors 106, thereby allowing for corrective measures that minimize potential misalignment and reduce wear on guide plates 104. The interaction between machine learning module 108 and predictive maintenance sensors 106 facilitates a streamlined, proactive maintenance process that minimizes production interruptions, providing dynamic, data-based responses that enhance the reliability and output consistency of the bag-making machine.
[00035] In an embodiment, the bottom folding drum 102 is structured to maintain strict axial alignment with guide plates 104, thereby creating a synchronized interaction that supports accurate flap deployment in the bag-making machine. Said bottom folding drum 102 operates in a manner that aligns rotational forces directly with the unfolding mechanism of the guide plates 104. This axial alignment allows the bottom folding drum 102 to engage smoothly with the flat cylinder, ensuring that as the drum rotates, each movement contributes directly to the unfolding process executed by guide plates 104. Rotational forces from the bottom folding drum 102 are transferred along the aligned axis, creating a unified movement that prevents deviations that could otherwise lead to misaligned flaps. The design of the bottom folding drum 102 incorporates mechanisms that stabilize rotation even under varying operational speeds, allowing the drum to retain positional integrity while in motion. Furthermore, the placement and orientation of the bottom folding drum 102 minimize any lateral movement that could disrupt the guide plates 104, instead channeling rotational forces consistently along the designated axis. In an embodiment, the bottom folding drum 102 also includes mounting adjustments to maintain its alignment over prolonged operational cycles, allowing recalibration to ensure continuous synchronicity with the guide plates 104. The axial alignment between the bottom folding drum 102 and guide plates 104 is fundamental to achieving synchronized unfolding, where every incremental rotation of the drum corresponds to a precise unfolding movement, thus reducing the potential for misalignment or material stress during high-speed production.
[00036] In an embodiment, guide plates 104 are tangentially positioned in relation to bottom folding drum 102, establishing a placement that optimally promotes a seamless transition during the flap unfolding process. Tangential positioning of guide plates 104 enables each flap to transition smoothly along the rotational path of the bottom folding drum 102, minimizing resistance and ensuring continuous flap unfolding without disruptions. The tangential placement creates a specific angle that facilitates entry and exit points for each flap, reducing contact friction between the material and guide plates 104. This angular configuration is particularly beneficial for high-speed operations, where maintaining consistent unfolding without material lag is essential. Guide plates 104 are strategically arranged so that each contact point aligns with the moment of rotation on the bottom folding drum 102, promoting a fluid transfer of energy between the drum and the flaps. Additionally, in an embodiment, guide plates 104 may be contoured to support a tangential flow, providing a rounded or angled edge that guides each flap into the correct position with minimal resistance. This configuration is highly effective in ensuring that each flap remains aligned during deployment, particularly when variations in material thickness or density occur. The tangential position of guide plates 104 thus allows for a controlled unfolding process that retains continuity in flap placement, providing a consistent, smooth transition across each cycle of the machine's operation.
[00037] In an embodiment, predictive maintenance sensors 106 are strategically positioned peripherally around guide plates 104 and are aligned along the rotational path of bottom folding drum 102, forming a cohesive monitoring system that continually assesses the unfolding positions of flaps. This peripheral placement enables predictive maintenance sensors 106 to detect even the slightest deviations in flap positions as they unfold along the path created by guide plates 104. The alignment of predictive maintenance sensors 106 allows for consistent monitoring throughout the rotation, ensuring that any positional changes or misalignments are identified promptly. By positioning predictive maintenance sensors 106 peripherally, the system captures data from multiple angles and positions, enhancing the accuracy of measurements related to guide plate movement and alignment. This configuration provides a full-spectrum view of unfolding dynamics, allowing predictive maintenance sensors 106 to detect anomalies that may affect production quality. In an embodiment, predictive maintenance sensors 106 may be calibrated to monitor parameters such as displacement, resistance, and speed, which influence the positioning of flaps. Further, predictive maintenance sensors 106 may detect external variables that could impact unfolding, such as temperature variations or vibrations, providing data to adjust guide plates 104 as needed. The comprehensive arrangement of predictive maintenance sensors 106 around the guide plates 104 enhances the machine's capacity to maintain production consistency, detecting fluctuations before they impact flap alignment.
[00038] In an embodiment, machine learning module 108 is positioned in calibrated association with predictive maintenance sensors 106, allowing real-time data from predictive maintenance sensors 106 to be accurately conveyed to machine learning module 108. This positional association ensures that data transfer is seamless and that machine learning module 108 receives current, uninterrupted sensor data related to guide plate positioning and unfolding dynamics. The placement of machine learning module 108 relative to predictive maintenance sensors 106 is designed to optimize data flow, enabling rapid analysis and adjustments. In this configuration, machine learning module 108 evaluates real-time data to identify patterns, discrepancies, or adjustments required to maintain precise unfolding. By continuously analyzing data from predictive maintenance sensors 106, machine learning module 108 facilitates immediate, responsive adjustments to guide plates 104, allowing for a proactive approach to maintenance and alignment. This integration enables predictive maintenance sensors 106 and machine learning module 108 to function as a unified system, reducing production downtime by addressing minor fluctuations before they result in misalignment or material stress. Further, machine learning module 108 may process historical data to predict future adjustments, aligning guide plates 104 to maintain consistent accuracy in unfolding based on data trends.
[00039] In an embodiment, guide plates 104 are adjustably mounted on a radial axis relative to bottom folding drum 102, allowing for flexible positioning adjustments that respond dynamically to operational conditions detected by predictive maintenance sensors 106. Said adjustable mounting provides guide plates 104 with the capacity to shift radially, adapting to variables such as material thickness or rotational speed, which influence the unfolding process. The radial axis mounting further enables guide plates 104 to make precise adjustments without requiring a full repositioning, allowing the system to optimize flap alignment with minimal interruption to production. In an embodiment, the adjustable mounting mechanism may be based on sliding or pivoting components that facilitate smooth, controlled radial shifts. This adjustability

reduces the likelihood of material stress, as guide plates 104 can modify their position to accommodate different operating conditions without exerting excessive force on the material. By mounting guide plates 104 on a radial axis, the system enhances accuracy in flap deployment, ensuring consistent positioning even when conditions fluctuate within the production environment.
[00040] In an embodiment, bottom folding drum 102 includes integrated angular positioning detectors that are operatively engaged with machine learning module 108, allowing for continuous analysis of angular variations in drum rotation. Said angular positioning detectors monitor the rotational speed, angle, and alignment of bottom folding drum 102, providing essential data to machine learning module 108 for maintaining consistent rotational precision. The integration of angular positioning detectors enables bottom folding drum 102 to adjust rotation based on detected angular deviations, preventing variations that could disrupt the synchronization between the drum and guide plates 104. Angular positioning detectors may operate through sensors placed along the drum's circumference, each calibrated to detect specific angular metrics. By analyzing rotation through angular positioning detectors, machine learning module 108 can optimize drum movement, enhancing the precision of flap positioning along the rotational path.
[00041] In an embodiment, guide plates 104 include a damping mechanism aligned with data obtained from predictive maintenance sensors 106, mitigating operational vibrations that could affect flap positioning, especially during high-speed operations. The damping mechanism within guide plates 104 serves to absorb mechanical oscillations or minor impacts that may occur as the machine operates, stabilizing guide plates 104 and preserving alignment. In high-speed operations, where vibrations are frequent, said damping mechanism reduces the transmission of vibration to the unfolding mechanism, preventing fluctuations in flap deployment. The damping mechanism may consist of vibration-absorbing materials or mechanical structures that isolate movement, allowing guide plates 104 to remain stable and accurately aligned with bottom folding drum 102. Data from predictive maintenance sensors 106 may guide adjustments within the damping mechanism, enabling real-time adaptation to vibration levels detected during production.
[00042] In an embodiment, machine learning module 108 incorporates a real-time data adjustment parameter to recalibrate predictive maintenance sensors 106, optimizing sensor data accuracy based on detected machine dynamics. The real-time data adjustment parameter enables machine learning module 108 to interpret fluctuations in sensor readings and recalibrate predictive maintenance sensors 106 accordingly. This recalibration addresses changes in operational conditions that could otherwise impact the accuracy of predictive maintenance sensors 106, ensuring reliable sensor output throughout production. Machine learning module 108 may use adjustment parameters to refine sensor sensitivity, allowing for heightened accuracy in detecting positional deviations or misalignments in guide plates 104. Recalibration based on real-time adjustments provides predictive maintenance sensors 106 with the capability to adapt to fluctuating dynamics, preserving data integrity and supporting precise alignment adjustments.
[00043] In an embodiment, predictive maintenance sensors 106 include thermal regulators that operate in conjunction with machine learning module 108 to monitor and adjust for temperature variations affecting guide plates 104. Said thermal regulators within predictive maintenance sensors 106 detect fluctuations in temperature that may cause material expansion or deformation in guide plates 104, thereby protecting against thermal-related misalignment. The thermal regulators are calibrated to respond to temperature changes in real time, providing data to machine learning module 108 to make corresponding adjustments to guide plate positioning. Thermal regulation within predictive maintenance sensors 106 aids in maintaining stability during high-speed production, where heat generated by machine movement may otherwise impact unfolding accuracy.
[00044] FIG. 2 illustrates a class diagram of the intelligent flap deployment system (100) for a bag-making machine, in accordance with the embodiments of the present disclosure. The intelligent flap deployment system 100 for a bag-making machine includes key components designed for seamless and accurate operation. The bottom folding drum 102 supports and rotates a flat cylinder, providing the base rotation required for bag formation. Guide plates 104 are positioned to unfold front and rear flaps of the flat cylinder, ensuring each flap aligns correctly for consistent bag structure. Predictive maintenance sensors 106 monitor guide plates 104, collecting real-time data on their performance to detect any misalignments or wear that could affect flap positioning. This data is then transmitted to a machine learning module 108, which analyzes sensor input to make necessary adjustments to guide plates 104. This continuous feedback loop enables guide plates 104 to adapt dynamically based on real-time data, allowing the system to maintain optimal flap positioning without requiring manual intervention, thus ensuring smooth, uninterrupted bag production with minimized misalignment and downtime risks.
[00045] In an embodiment, the intelligent flap deployment system 100 includes a bottom folding drum 102 that supports and rotates a flat cylinder, guide plates 104 that unfold front and rear flaps of said flat cylinder, predictive maintenance sensors 106 that monitor the guide plates 104, and a machine learning module 108 that analyzes sensor data. The bottom folding drum 102 provides stability and consistent rotational force to the flat cylinder, ensuring that each bag maintains alignment during production. Guide plates 104 are strategically placed to unfold flaps with precision, ensuring that each bag's structure is maintained. Predictive maintenance sensors 106 monitor the condition of guide plates 104 in real time, identifying deviations that could impact the production quality. The machine learning module 108 processes data from predictive maintenance sensors 106 and prompts adjustments to guide plates 104, enabling adaptive control and reducing the need for manual intervention.
[00046] In an embodiment, the bottom folding drum 102 is aligned axially with guide plates 104, ensuring that rotational forces are synchronized with the unfolding motion of the guide plates 104. This axial alignment enables the bottom folding drum 102 to coordinate its rotational speed and position with the guide plates 104, creating a cohesive movement that facilitates the precise deployment of front and rear flaps. By maintaining axial alignment, the system minimizes misalignment risks, which can lead to inconsistencies in bag quality. This synchronized movement allows the unfolding of flaps to occur seamlessly, maintaining the intended structure and alignment of each bag as it moves through the production line. The alignment also reduces the likelihood of mechanical strain on components, as synchronized movement between the drum and guide plates 104 decreases the need for abrupt adjustments or corrections, promoting smoother operation.
[00047] In an embodiment, guide plates 104 are positioned tangentially relative to the bottom folding drum 102, promoting a smooth and controlled transition for flap unfolding. The tangential placement allows guide plates 104 to interact with the flat cylinder at an optimal angle, which minimizes physical resistance and supports continuous movement along the drum's rotational path. This alignment reduces the stress exerted on both the material and the guide plates 104, as it prevents abrupt directional changes that could disrupt the unfolding process. Tangential positioning also aids in preserving the shape and alignment of the flaps as they are deployed, contributing to uniformity in each bag. By promoting a streamlined transition, the tangential placement of guide plates 104 enhances the overall efficiency of flap unfolding, reducing potential for mechanical resistance and material wear.
[00048] In an embodiment, predictive maintenance sensors 106 are arranged peripherally around guide plates 104 and aligned with the rotational path of the bottom folding drum 102, providing comprehensive monitoring of the flap unfolding process. The peripheral arrangement enables predictive maintenance sensors 106 to detect minor deviations in the position and alignment of guide plates 104, allowing for quick identification of potential issues that could affect production consistency. By monitoring guide plates 104 in real time, predictive maintenance sensors 106 help maintain a stable unfolding process, as they can detect variations that may require adjustments. This alignment also allows predictive maintenance sensors 106 to track positional changes as guide plates 104 move with the bottom folding drum 102, ensuring that each flap is unfolded accurately. The peripheral placement creates a cohesive monitoring system that supports continuous production with minimal downtime due to misalignment.
[00049] In an embodiment, machine learning module 108 is in calibrated positional association with predictive maintenance sensors 106, enabling the direct transfer of real-time sensor data for immediate analysis. The calibrated association allows machine learning module 108 to receive data without delays, ensuring prompt adjustments to guide plates 104 based on detected operational variations. By analyzing data from predictive maintenance sensors 106, machine learning module 108 can detect patterns or anomalies that may affect flap positioning, facilitating proactive adjustments to maintain alignment. This immediate data processing capability allows for streamlined maintenance, as adjustments can be made in real time without interrupting production. The calibrated association between machine learning module 108 and predictive maintenance sensors 106 promotes a more responsive maintenance process, helping to reduce downtime and maintain production flow.
[00050] In an embodiment, guide plates 104 are adjustably mounted on a radial axis relative to the bottom folding drum 102, allowing for dynamic positioning based on feedback from predictive maintenance sensors 106. The radial mounting enables guide plates 104 to shift incrementally as needed, adjusting to variations in material thickness, production speed, or other operational conditions. This adaptability allows guide plates 104 to maintain optimal contact with the flat cylinder, reducing material stress and improving the accuracy of flap placement. Radial adjustments prevent excessive strain on guide plates 104, enhancing their durability by allowing them to respond dynamically to real-time data. The adjustable mounting configuration also ensures that each flap is unfolded in alignment with the rotating drum, contributing to consistent bag quality without manual realignment.
[00051] In an embodiment, the bottom folding drum 102 includes integrated angular positioning detectors that are operatively engaged with machine learning module 108. The angular positioning detectors continuously measure and transmit data on the rotation angle of bottom folding drum 102, providing detailed information on any variations in rotational precision. By analyzing this data, machine learning module 108 can optimize the rotational movement of the drum, ensuring alignment with guide plates 104 and maintaining consistent flap positioning. The data from angular positioning detectors supports the identification of any minor shifts in drum rotation, which can be addressed before they impact production. This feedback loop enhances the accuracy of flap deployment by aligning the rotational movement of bottom folding drum 102 with the positional requirements of guide plates 104, supporting the continuous, uniform production of bags.
[00052] In an embodiment, guide plates 104 include a damping mechanism that aligns with data from predictive maintenance sensors 106 to mitigate vibrations during high-speed operations. The damping mechanism absorbs oscillations that may arise from rapid movement, reducing the impact of vibrations on flap unfolding. This vibration control promotes stability in guide plates 104, preventing fluctuations that could disrupt the unfolding process or misalign the flaps. The damping mechanism operates in tandem with predictive maintenance sensors 106, adjusting its resistance based on detected vibration levels. By minimizing vibrations, the damping mechanism supports consistent flap deployment even at high operational speeds, preserving the alignment and integrity of each bag.
[00053] In an embodiment, machine learning module 108 includes a real-time data adjustment parameter that allows for recalibration of predictive maintenance sensors 106 based on changing machine dynamics. The real-time data adjustment parameter enables machine learning module 108 to periodically recalibrate predictive maintenance sensors 106 to ensure measurement accuracy. This recalibration accounts for variations in temperature, speed, and material properties, which could otherwise impact sensor readings. By adjusting sensor calibration in response to operational changes, machine learning module 108 maintains the reliability of data used to adjust guide plates 104. The recalibration function allows predictive maintenance sensors 106 to adapt to different conditions without manual intervention, supporting precise flap positioning and stable production.
[00054] In an embodiment, predictive maintenance sensors 106 include thermal regulators that monitor temperature variations in guide plates 104, preventing thermal deformation that could impact flap positioning. The thermal regulators operate in conjunction with machine learning module 108, which uses temperature data to make adjustments that account for thermal expansion or contraction. By maintaining optimal temperature levels, the thermal regulators help prevent fluctuations that could misalign the guide plates 104. The integration of thermal regulation ensures that guide plates 104 remain structurally stable across a range of operating temperatures, maintaining the accuracy of flap unfolding.
[00055] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00056] Throughout the present disclosure, the term 'Artificial intelligence (AI)' as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00057] Throughout the present disclosure, the term 'processing means' or 'microprocessor' or 'processor' or 'processors' includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00058] The term "non-transitory storage device" or "storage" or "memory," as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00059] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00060] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.













Claims
I/We Claim:
1. An intelligent flap deployment system (100) for a bag-making machine, comprising:
a bottom folding drum (102) configured to support and rotate a flat cylinder;
guide plates (104) disposed to unfold front and rear flaps of said flat cylinder;
predictive maintenance sensors (106) configured to monitor said guide plates (104); and
a machine learning module (108) operatively connected to said sensors (106) for analyzing sensor data, wherein said guide plates (104) adjust based on analyzed data to maintain optimal flap positioning during production.
Claim 2:
The intelligent flap deployment system (100) of claim 1, wherein said bottom folding drum (102) is configured to maintain axial alignment with said guide plates (104), such that rotational forces exerted by said bottom folding drum (102) synchronize the unfolding movement of said guide plates (104), facilitating precise deployment of front and rear flaps to prevent misalignment.
Claim 3:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) are positioned tangentially relative to said bottom folding drum (102), such that tangential placement promotes a smooth transition of flap unfolding, minimizing physical resistance, and ensuring continuity in flap deployment along the rotational axis of said bottom folding drum (102).
Claim 4:
The intelligent flap deployment system (100) of claim 1, wherein said predictive maintenance sensors (106) are positioned peripherally around said guide plates (104) and aligned along the rotational path of said bottom folding drum (102), providing a cohesive monitoring system that detects minute deviations in flap unfolding positions, thus enhancing production consistency.
Claim 5:
The intelligent flap deployment system (100) of claim 1, wherein said machine learning module (108) is in calibrated positional association with said predictive maintenance sensors (106), such that real-time sensor data is conveyed to said machine learning module (108) to enable prompt adjustments to said guide plates (104), promoting a streamlined maintenance process and minimizing downtime.
Claim 6:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) are adjustably mounted on a radial axis in relation to said bottom folding drum (102), allowing dynamic positioning adjustments based on operational conditions detected by said predictive maintenance sensors (106), thereby enhancing accuracy in flap placement and reducing material stress on said guide plates (104).
Claim 7:
The intelligent flap deployment system (100) of claim 1, wherein said bottom folding drum (102) includes integrated angular positioning detectors operatively engaged with said machine learning module (108), such that angular variations in drum rotation are analyzed, providing data to optimize rotational precision and maintain flap consistency.
Claim 8:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) comprise a damping mechanism that operates in alignment with data from said predictive maintenance sensors (106), mitigating vibrations in said guide plates (104) during high-speed operations to ensure uninterrupted flap deployment.
Claim 9:
The intelligent flap deployment system (100) of claim 1, wherein said machine learning module (108) further comprises a real-time data adjustment parameter that allows recalibration of said predictive maintenance sensors (106), thereby enhancing operational accuracy by calibrating sensor data specific to changing machine dynamics.
Claim 10:
The intelligent flap deployment system (100) of claim 1, wherein said predictive maintenance sensors (106) include thermal regulators configured to operate in conjunction with said machine learning module (108) to monitor temperature variations in said guide plates (104), preventing thermal deformation that could affect the flap positioning accuracy.




Dated 11 November 2024 Jigneshbhai Mungalpara
IN/PA- 2640
Agent for the Applicant



Machine Learning-Driven Flap Control System for Automated Bag Production
Abstract
The present disclosure provides an intelligent flap deployment system for a bag-making machine. The system includes a bottom folding drum to support and rotate a flat cylinder and guide plates arranged to unfold front and rear flaps of said flat cylinder. Predictive maintenance sensors monitor said guide plates to ensure continuous operational precision. A machine learning unit is operatively connected to said sensors to analyze data collected from the sensors. Based on the analyzed data, said guide plates dynamically adjust to maintain optimal flap positioning during production.


Dated 11 November 2024 Jigneshbhai Mungalpara
IN/PA- 2640
Agent for the Applicant




, Claims:Claims
I/We Claim:
1. An intelligent flap deployment system (100) for a bag-making machine, comprising:
a bottom folding drum (102) configured to support and rotate a flat cylinder;
guide plates (104) disposed to unfold front and rear flaps of said flat cylinder;
predictive maintenance sensors (106) configured to monitor said guide plates (104); and
a machine learning module (108) operatively connected to said sensors (106) for analyzing sensor data, wherein said guide plates (104) adjust based on analyzed data to maintain optimal flap positioning during production.
Claim 2:
The intelligent flap deployment system (100) of claim 1, wherein said bottom folding drum (102) is configured to maintain axial alignment with said guide plates (104), such that rotational forces exerted by said bottom folding drum (102) synchronize the unfolding movement of said guide plates (104), facilitating precise deployment of front and rear flaps to prevent misalignment.
Claim 3:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) are positioned tangentially relative to said bottom folding drum (102), such that tangential placement promotes a smooth transition of flap unfolding, minimizing physical resistance, and ensuring continuity in flap deployment along the rotational axis of said bottom folding drum (102).
Claim 4:
The intelligent flap deployment system (100) of claim 1, wherein said predictive maintenance sensors (106) are positioned peripherally around said guide plates (104) and aligned along the rotational path of said bottom folding drum (102), providing a cohesive monitoring system that detects minute deviations in flap unfolding positions, thus enhancing production consistency.
Claim 5:
The intelligent flap deployment system (100) of claim 1, wherein said machine learning module (108) is in calibrated positional association with said predictive maintenance sensors (106), such that real-time sensor data is conveyed to said machine learning module (108) to enable prompt adjustments to said guide plates (104), promoting a streamlined maintenance process and minimizing downtime.
Claim 6:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) are adjustably mounted on a radial axis in relation to said bottom folding drum (102), allowing dynamic positioning adjustments based on operational conditions detected by said predictive maintenance sensors (106), thereby enhancing accuracy in flap placement and reducing material stress on said guide plates (104).
Claim 7:
The intelligent flap deployment system (100) of claim 1, wherein said bottom folding drum (102) includes integrated angular positioning detectors operatively engaged with said machine learning module (108), such that angular variations in drum rotation are analyzed, providing data to optimize rotational precision and maintain flap consistency.
Claim 8:
The intelligent flap deployment system (100) of claim 1, wherein said guide plates (104) comprise a damping mechanism that operates in alignment with data from said predictive maintenance sensors (106), mitigating vibrations in said guide plates (104) during high-speed operations to ensure uninterrupted flap deployment.
Claim 9:
The intelligent flap deployment system (100) of claim 1, wherein said machine learning module (108) further comprises a real-time data adjustment parameter that allows recalibration of said predictive maintenance sensors (106), thereby enhancing operational accuracy by calibrating sensor data specific to changing machine dynamics.
Claim 10:
The intelligent flap deployment system (100) of claim 1, wherein said predictive maintenance sensors (106) include thermal regulators configured to operate in conjunction with said machine learning module (108) to monitor temperature variations in said guide plates (104), preventing thermal deformation that could affect the flap positioning accuracy.




Dated 11 November 2024 Jigneshbhai Mungalpara
IN/PA- 2640
Agent for the Applicant

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