Description:Field of the Invention
This invention relates to Vision-enabled Quality Control System for Label Inspection on Pallet Labeling Machine with Cloud Alert and Notification.
Background of the Invention
This innovative system effortlessly integrates state-of-the-art technologies with pallet labeling machines, transforming quality control operations. Real-time video analysis employs machine learning algorithms to precisely detect label flaws, hence ensuring superior product quality. With the help of this technology, manufacturers may improve overall operational performance by reducing losses related to quality concerns, increasing production efficiency, and maintaining rigorous quality standards.
The problem of guaranteeing effective and trustworthy quality control in pallet labeling machines utilized in manufacturing facilities is addressed by this innovation. The precision and adaptability required for quickly detecting label flaws are often lacking in conventional quality control methods. This deficiency may lead to waste, product recalls, and reputational harm to the brand.
JP4799050B2 The present invention relates to a printing apparatus having a function of performing printing on an RFID label incorporating an RFID (Radio Frequency Identification) tag capable of reading and writing information without contact, and a read / write function of performing writing / reading of RFID information. .

RESEARCH GAP: Vision based edge technology for Label QC for Pallet Labeling Machine is the novelty of the system.
JP2006341883A In a label pasting apparatus, when the RFID label is formed of a mountless label and an RFID tape stuck by a pasting roller, the mount is turned in the conveyance direction by a turning pin and taken up around a shaft of a mount take-up part. The RFID label is subjected to data write by an RFID read/write part and printing by a thermal head while conveyed by the rotation of a platen, and thereafter, it is cut by a cutter unit. The rear end of the cut piece is held by a feed roller, and pasted on an article by air discharged from an air hole of a suction plate. When only the mountless label is to be carried, the cut piece is moved by keeping sucked by the plate, pressed against the article and pasted.
RESEARCH GAP: Vision based edge technology for Label QC for Pallet Labeling Machine is the novelty of the system.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Vision-enabled Quality Control Solution for Label Inspection on Pallet Labeling Machine with Cloud Alert and Notification.
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.
By combining cutting-edge technologies, the VQCTD_PLMote offers a strong, proactive, and networked solution for pallet labeling quality control. This is a revolutionary approach to pallet labeling quality control. The BeagleBone Processor Board, which acts as the central hub coordinating all operations, is at the heart of everything. Using preinstalled datasets, this board runs machine learning algorithms that are essential for label inspection, analyzing live video feeds that are recorded by the Camera Module. The camera records live video as pallets moves through the labeling process, and the BeagleBone's machine learning algorithms watch and process the footage. Based on patterns that have been learned, these algorithms carefully examine labels for deviations or flaws, allowing the system to quickly detect quality problems. The system promptly alerts operators and pertinent staff after detecting something. It then starts a sequence of activities.
The Buzzer's auditory cues serve as the first line of alert, guaranteeing adjacent operators' quick response. Simultaneously, the HMI Display shows visual alerts that give operators immediate feedback on quality faults that are identified. By ensuring that operators receive fast notifications, this dual-alert system reduces the possibility that defective products would go unnoticed. Furthermore, the system makes use of GSM Modem connectivity to increase its reach outside of the manufacturing floor. By use of this connectivity, the VQCTD_PLMote can rapidly notify important stakeholders of any quality deviations by sending SMS notifications to selected persons. This quick communication system makes it possible to respond and intervene quickly, minimizing the risk of production interruptions and quality-related losses.
Furthermore, advanced features are unlocked by the system's interaction with IoT-based cloud technologies. The VQCTD_PLMote may send data to a customized web dashboard located on a cloud server by connecting to the internet. With protected login credentials, this dashboard offers operators a remote, full overview of ongoing quality control procedures. In order to maximize production efficiency and product quality, operators can evaluate historical data, keep an eye on label quality in real-time, and take proactive steps. Furthermore, the technology enables smooth data archiving and storage by sending films and photos with identified quality problems to the cloud server. In addition to recording quality control activities, this visual evidence repository is a useful tool for retrospective analysis and ongoing improvement programs.
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.
By combining cutting-edge technologies, the VQCTD_PLMote offers a strong, proactive, and networked solution for pallet labeling quality control. This is a revolutionary approach to pallet labeling quality control. The BeagleBone Processor Board, which acts as the central hub coordinating all operations, is at the heart of everything. Using preinstalled datasets, this board runs machine learning algorithms that are essential for label inspection, analyzing live video feeds that are recorded by the Camera Module. The camera records live video as pallets moves through the labeling process, and the BeagleBone's machine learning algorithms watch and process the footage. Based on patterns that have been learned, these algorithms carefully examine labels for deviations or flaws, allowing the system to quickly detect quality problems. The system promptly alerts operators and pertinent staff after detecting something. It then starts a sequence of activities.
The Buzzer's auditory cues serve as the first line of alert, guaranteeing adjacent operators' quick response. Simultaneously, the HMI Display shows visual alerts that give operators immediate feedback on quality faults that are identified. By ensuring that operators receive fast notifications, this dual-alert system reduces the possibility that defective products would go unnoticed. Furthermore, the system makes use of GSM Modem connectivity to increase its reach outside of the manufacturing floor. By use of this connectivity, the VQCTD_PLMote can rapidly notify important stakeholders of any quality deviations by sending SMS notifications to selected persons. This quick communication system makes it possible to respond and intervene quickly, minimizing the risk of production interruptions and quality-related losses.
Furthermore, advanced features are unlocked by the system's interaction with IoT-based cloud technologies. The VQCTD_PLMote may send data to a customized web dashboard located on a cloud server by connecting to the internet. With protected login credentials, this dashboard offers operators a remote, full overview of ongoing quality control procedures. In order to maximize production efficiency and product quality, operators can evaluate historical data, keep an eye on label quality in real-time, and take proactive steps. Furthermore, the technology enables smooth data archiving and storage by sending films and photos with identified quality problems to the cloud server. In addition to recording quality control activities, this visual evidence repository is a useful tool for retrospective analysis and ongoing improvement programs.
The present invention introduces a novel Vision-enabled Quality Control Solution for Label Inspection, specifically designed for pallet labeling machines. This system, referred to as VQCTD_PLMote, integrates advanced machine learning algorithms, vision technology, and cloud-based communication to provide a proactive approach to label quality control.
Components:
VQCTD_PLMote (100):
RTC Module (100B)
Power Supply (100C)
Buzzer (100D)
Camera Module (100E)
HMI Display (100F)
GSM Modem (100G)
BeagleBone Processor Board (100H)
Operation:
The VQCTD_PLMote is a comprehensive system designed to ensure the accuracy and quality of labels on pallets as they pass through a labeling machine. At the core of the system is the BeagleBone Processor Board (100H), which serves as the central processing unit, orchestrating the entire operation.
The Camera Module (100E) captures live video footage of the pallets as they move through the labeling process. This footage is then fed into the BeagleBone Processor Board, where pre-installed machine learning algorithms analyze the video in real-time. These algorithms, trained on a dataset of correct and incorrect labels, inspect the labels for any deviations, defects, or errors.
If a defect is detected, the system initiates a sequence of alerts:
The Buzzer (100D) provides an immediate auditory alert, ensuring that nearby operators are quickly informed of the issue.
Simultaneously, the HMI Display (100F) shows visual alerts, offering detailed feedback on the nature of the detected fault. This dual-alert mechanism enhances the likelihood of a rapid operator response, reducing the chance that defective products will proceed further down the production line.
For broader communication, the system employs a GSM Modem (100G). Upon detecting a quality issue, the system sends SMS notifications to preselected stakeholders, ensuring that relevant personnel are informed even if they are not on the manufacturing floor. This rapid notification system allows for swift intervention, minimizing potential disruptions in production and mitigating quality-related losses.
In addition to real-time alerts, the VQCTD_PLMote leverages IoT and cloud technologies to enhance its functionality. The system is capable of transmitting data to a secure, cloud-based web dashboard. Authorized users can access this dashboard remotely via the internet, where they can monitor the quality control process in real-time, review historical data, and take proactive measures to improve labeling quality.
The system also supports data archiving and storage. When a quality defect is identified, the corresponding video footage or images are uploaded to the cloud server. This visual evidence serves as a valuable resource for retrospective analysis and continuous improvement initiatives, ensuring that quality control processes are continually refined.
ADVANTAGES OF THE INVENTION
1. The VQCTD_PLMote integrates machine learning algorithms, Internet of Things connectivity, and cloud-based data management to optimize quality control procedures in pallet labeling machines. This connection improves production productivity and product quality by enabling quick identification of label flaws, timely operator intervention, and remote monitoring.
2. The VQCTD_PLMote records live video footage of pallet labels by utilizing the Camera Module. After that, machine learning algorithms examine the video to identify any flaws quickly and precisely. This helps pallet labeling machines maintain exact quality control.
3. The VQCTD_PLMote can deliver SMS alerts to specified personnel with the help of the GSM Modem. In order to minimize production disruptions and enable prompt action, this guarantees that any quality faults discovered by the system in pallet labeling machines are immediately notified.
4. The HMI Display is essential because it gives operators visual alerts in real time. Pallet labeling machine operators can intervene immediately and ensure effective quality control procedures by receiving these notifications, which notify them of found label problems.
, Claims:1. A vision-enabled quality control system for pallet labeling machines, comprising:
A VQCTD_PLMote unit including a BeagleBone Processor Board, a Camera Module, a Buzzer, an HMI Display, a GSM Modem, an RTC Module, and a Power Supply;
Machine learning algorithms running on the BeagleBone Processor Board, trained to analyze video footage of labels for defects and deviations;
An alert system that includes both auditory and visual alerts for notifying operators of detected labeling faults.
2. The system as claimed in claim 1, Vision-enabled Quality Control Solution for Label Inspection on Pallet Labeling Machine with Cloud Alert and Notification comprises of VQCTD_PLMote (100), RTC Module (100B), Power Supply (100C), Buzzer (100D), Camera Module (100E), HMI Display (100F), GSM Modem (100G) and BeagleBone Processor Board (100H).
3. The system as claimed in claim 1, wherein the Camera Module captures real-time video footage of pallets passing through a labeling machine, and the BeagleBone Processor Board processes this footage to detect label defects.
4. The system as claimed in claim 1, wherein the GSM Modem sends SMS notifications to selected stakeholders, ensuring prompt communication of detected labeling faults.
5. The system as claimed in claim 1, wherein the system is configured to transmit data to a cloud-based web dashboard, allowing remote monitoring and historical analysis of labeling quality.
6. The system as claimed in claim 1, wherein the system archives video footage and images of detected labeling faults to a cloud server, providing a resource for retrospective analysis and continuous improvement initiatives.