Description:FORM 2
The Patents Act, 1970
(39 of 1970)
&
The Patents Rules, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
A SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE (AI) BASED TOOL MONITORING AND BREAKAGE DETECTION
by
MANLEO DESIGNS PVT LTD.,
an
Indian Organization
of
888, 5th Main, Kempegowda Main Road, BHEL Layout
2nd Stage Extension Rajarajeshwari Nagar
Bangalore - 560 098
Karnataka State, India
Cell: +91-98861 27932; E-mail: abhijith@manleo.com
The following specification describes and ascertains the invention and the manner in which it is to be performed.
BACKGROUND OF THE PRESENT INVENTION
TECHNICAL FIELD OF THE PRESENT INVENTION DISCLOSURE
[0001] The present invention relates generally to the optimization of manufacturing processes and, more particularly, to the detection of tool breakage during production. More specifically, it pertains to the application of computer vision technology in combination with advanced machine learning algorithms for real-time tool condition monitoring in a manufacturing environment.
BACKGROUND OF THE INVENTION AND DESCRIPTION OF THE RELATED ART
[0002] In high-precision manufacturing settings, especially those that utilize computer numerical control (CNC) machining systems, the state of cutting tools has a direct impact on product quality, machine availability, and overall production efficiency. Tools employed in CNC operations endure significant mechanical stress, thermal loads, and wear due to the high-speed and high-force characteristics of machining processes. If not identified, tool degradation or breakage can result in defective components, damage to the work piece or machine spindle, and expensive unplanned downtime.
[0003] Traditional methods for detecting tool wear or breakage have relied on indirect measures such as monitoring spindle load, acoustic emissions, vibration, and even force sensors. While some of these methods can provide some indication of tool condition, many lack the sensitivity or specificity required to identify distortions or the initial stages of wear. These sensor-driven systems may also encounter environmental noise or machine-specific factors, causing them to issue false alarms or miss crucial detections.
[0004] For many years, visual inspection has proven to be one of the most effective tool condition assessment methods. While the manual approach is accurate, it is also labor-intensive and non-scalable for automated systems. Attempts have been made toward integrating computer numerical control (CNC) fixtures with machine vision systems aimed at automated tool monitoring, but these systems tend to rely on pre-defined algorithms that are extremely over calibrated and very sensitive to changes in lighting, coolant spray, metal chips, dust and even vibrational turbulence-traits commonplace in industrial machining environments.
[0005] Traditional monitoring systems based on vision lack immediate response capabilities and cannot adjust to different tools and machining situations. These systems operate reactively instead of proactively which leads to late detection of tool degradation in its late stages. The above limitation brings about a higher probability of part defects along with rework requirements and machine damage risks.
[0006] The swift development of artificial intelligence (AI) and deep learning technologies presents expanding possibilities to improve conventional machine vision systems through intelligent adaptive features. Image recognition models that utilize artificial intelligence technology can detect subtle variations in tool design and surface characteristics which helps to identify early signs of wear and potential breakage. The models are designed to maintain optimal performance in challenging machining environments that cause traditional systems to fail.
[0007] To overcome these limitations, there is a need for a system and method for AI-based tool monitoring and breakage detection which is proactive, adaptive, and highly accurate for ensuring tool integrity in modern computer numerical control (CNC) machining applications and which further integrates a high-resolution industrial camera module with an AI processing unit using deep learning models and a computer numerical control (CNC) machine controller that responds to real-time alerts. The system supports contactless monitoring of machining tools during active use while functioning properly under tough conditions like coolant presence and high vibration.
[0008] Further to the foregoing, the proposed system and method for AI-based tool monitoring and breakage detection is enabled to facilitate immediate anomaly detection for wear, degradation, and breakage situations while providing options for corrective measures that include halting machining operations or starting backup tools and fail-safe procedures. The invention improves tool management efficiency through robust hardware design integrated with intelligent software analytics which simultaneously minimizes unplanned downtime and boosts manufacturing productivity.
OBJECTS OF THE PRESENT INVENTION
[0009] Few of the objects of the instant invention are as stated below:
 an object of the present invention is to provide a system and method for real-time, non-contact monitoring of the condition of cutting tools in computer numerical control (CNC) machining environments using high-resolution imaging technology;
 another object of the present invention is to implement an artificial intelligence (AI) processing unit that utilizes deep learning models for the adaptive, accurate and immediate detection and examination of tool anomalies;
 yet another object of the present invention is to enable the integration with computer numerical control (CNC) machine controllers to facilitate automated decision-making and immediate corrective measures;
 an additional object of the present invention is to enhance manufacturing productivity by reducing unplanned downtime and minimizing tool-related defects;
 a further object of the present invention is to provide periodic and event-driven image capturing controlled by user-defined parameters for flexible and adaptive tool monitoring; and
 another object of the present invention is to improve overall tool management efficiency through the combination of robust hardware design and intelligent software analytics.
[0010] Other objects, aspects, features and goals of the instant invention will be better understood from the following detailed description taken in conjunction with the accompanying drawings.
DETAILED DESCRIPTION OF THE PRESENT INVENTION DISCLOSURE
SUMMARY OF THE PRESENT INVENTION
[0011] The present invention relates to a The present invention presents a system and method for monitoring tools and detecting breakage using artificial intelligence (AI), specifically aimed at improving the reliability, efficiency, and safety of machining operations within computer numerical control (CNC) settings. The system of the present invention combines sophisticated hardware components with intelligent software analytics to facilitate real-time, non-contact monitoring of cutting tools during active use.
[0012] At the centre of the system, there is a computer numerical control (CNC) machine controller that manages the operational parameters of the entire configuration. This computer numerical control (CNC) controller is connected to a camera module housed in a protective casing, strategically placed to capture high-resolution images of the tool in operation. The camera is designed to operate reliably even in challenging industrial conditions such as exposure to coolant, accumulation of metal chips, dust, and high vibrations-typical obstacles in computer numerical control (CNC) machining environments.
[0013] The images captured are either taken at regular intervals or triggered by specific events, ensuring thorough monitoring of the tool’s condition throughout its operational life. These images are subsequently analyzed by an artificial intelligence (AI) processing unit, which utilizes a range of deep learning models trained to identify anomalies such as wear, degradation, or complete breakage of the tool. This AI-driven analysis allows for immediate detection of potential problems, significantly minimizing the risk of unnoticed tool failure.
[0014] When an anomaly is detected, the system sends an alert through a dedicated communication interface to the CNC machine controller. In response to the real-time data received, the controller executes pre-programmed corrective measures, which may involve stopping the machining process, switching to a backup tool, or activating fail-safe protocols. These automated actions assist in preventing further damage to the tool or work piece and help reduce unplanned downtime.
[0015] The disclosed method of the present invention provides a systematic workflow that starts with the acquisition of images, followed by user-defined control over the settings for image capture. It incorporates AI-driven image analysis, the generation of alerts, real-time decision-making by the CNC controller, and the implementation of corrective measures. This guarantees that the system is flexible enough to adapt to different machining situations while ensuring a high level of responsiveness and precision.
[0016] In conclusion, the present invention signifies a notable progress in the technology of tool condition monitoring. By merging strong industrial design with advanced AI analytics, it offers manufacturers a proactive approach to preserving tool integrity, enhancing production uptime, and improving overall manufacturing quality.
[0017] The following description is illustrative in nature and is not intended to be in any way limiting. In addition to the aforementioned illustrative aspects, embodiments, and features of the instant invention, further aspects, embodiments and features will become apparent by reference to the following detailed description.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0018] The non-limiting and non-exhaustive embodiments of the instant invention are described with reference to the figures in the accompanying drawings wherein like reference letters and numerals indicate the corresponding parts in various figures unless otherwise specified. It will be appreciated that for simplicity and clarity of illustration, parts and elements illustrated in figures of the drawings have not necessarily been drawn to scale. Further, the accompanying drawings illustrate the best mode for carrying out the invention as presently contemplated and set forth herein after. The present invention may be more clearly understood from a consideration of the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings in which:
[0019] Fig.1 is an illustration of a schematic diagram of the system for using artificial intelligence (AI) based tool monitoring and breakage detection. In accordance with the most preferred embodiment of the present invention; and
[0020] Fig.2 is an illustration of a flowchart depicting the method for monitoring tools and detecting breakage using artificial intelligence (AI), in accordance with the most preferred embodiment of the present invention.
MOST PREFERRED AND ILLUSTRATED EMBODIMENTS OF THE PRESENT INVENTION
[0021] In order to ameliorate the prior art and to overcome one or more drawbacks and disadvantages associated therewith, and in order to provide additional advantages, a laser tool setter sensor assembly for use in computer numerical control (CNC) machining applications is provided and illustrated herein.
[0022] The following detailed description sets forth in detail various preferred embodiments of the invention, which are intended to be illustrative and not limiting.
[0023] The present invention relates to a system and method for monitoring tools and detecting breakage using artificial intelligence (AI), specifically aimed at improving the reliability, efficiency, and safety of machining operations within computer numerical control (CNC) settings. The embodiments presented offer a comprehensive contact surveillance of machining tools, consequently averting expensive downtime, material loss, and potential harm to machinery resulting from tool wear, deterioration, or breakage.
[0024] The disclosed invention relates to an advanced system and method for monitoring machining tools in real-time, using computer vision and artificial intelligence (AI) techniques to identify abnormalities like wear, deterioration, or breakage. Because it allows for proactive corrective actions, the system is built to function in demanding manufacturing environments, guaranteeing high reliability and safety. The embodiments discussed here show different configurations and modes of operation that demonstrate the claimed inventive concepts.
[0025] The core architecture of the proposed system designated as system 100 integrates multiple specialized modules that collectively enable continuous, non-contact, AI-driven tool monitoring during CNC machining operations. This architecture includes the CNC machine controller, a protective camera module, AI processing units, and communication interfaces, all coordinated to deliver a thorough and self-sufficient monitoring solution.
[0026] Fig.1 illustrates a schematic diagram of the system 100 for AI-based tool monitoring and breakage detection. The system 100 consists of a computer numerical control (CNC) machine controller 102, a camera module 104, a minimum of one artificial intelligence (AI) processing unit 108, and a communication interface 110. These elements collaborate to deliver a thorough solution for real-time assessment of tool conditions and proactive corrective measures. The system 100 is engineered to integrate smoothly into current CNC machining settings, thereby improving operational efficiency and safety.
[0027] At the core of the manufacturing system lies the CNC machine controller 102, which manages the accurate functioning of the machining process. Traditionally, the CNC controller 102 carries out pre-programmed commands for tool movements, feed rates, spindle speeds, and other essential parameters. However, in the present invention, the CNC controller 102 is additionally configured through specialized software to connect with the monitoring system, facilitating real-time decision-making based on AI analysis results. The software integrated within the CNC controller 102 is configured to receive alerts, analyze anomaly data, and implement pre-established corrective actions such as halting operations, changing tools, or activating safety protocols. This integration guarantees that the control system reacts immediately to identified problems, thereby minimizing downtime and averting damage.
[0028] The CNC machine controller 102 acts as the primary control unit for the machining process. It is set up with at least one software application to manage a variety of operational parameters of the entire system 100. This encompasses, but is not restricted to, spindle speed, feed rate, tool path, and coolant flow. In relation to the current invention, the CNC machine controller 102 is specifically designed to receive notifications from the AI processing unit 108 concerning the real-time status of the tool 106. Upon receiving such notifications, the CNC machine controller 102 is programmed to make appropriate decisions based on the data pertaining to the real-time condition of the tool and to commence the execution of pre-defined corrective measures. These measures are essential for averting damage to the tool 106, the work piece, and the machine itself, thereby allowing the operator to address tool-related problems in a proactive manner.
[0029] The camera module 104 serves as an essential element for gathering visual data regarding the tool 106 during its operation. It is housed within a sturdy protective casing that has been specifically engineered to guard against the detrimental impacts of various machining environmental conditions. These demanding environments may encompass, but are not limited to, the presence of coolant, metal shavings, dust, and considerable vibrations.
[0030] The protective casing guarantees the durability and reliable performance of the camera. Inside this casing, a high-resolution industrial camera is strategically placed to capture both periodic and event-driven images of the tool 106 in action. The positioning of the camera is optimized to deliver an unobstructed high-resolution view of the cutting edge of the tool 106 and its surrounding area, thereby reducing blind spots or obstructions caused by the work piece or machine components.
[0031] Additionally, the camera is designed to operate effectively in a variety of challenging environments, including low-light conditions frequently found within machining enclosures and high-vibration scenarios typical of machining processes. The capability to obtain clear and precise images in these rigorous settings is crucial for enabling accurate monitoring of the condition of the tool 106. The images captured can be either periodic, meaning they are taken at set intervals, or event-driven, which means they are triggered by specific occurrences such as alterations in machining parameters or anomalies detected by other sensors (if available). This dual methodology ensures thorough data collection for real-time monitoring and detection.
[0032] The AI processing unit 108 serves as the intelligent core of the system 100. It is designed to employ multiple deep learning models to recognize and assess the captured images for the immediate detection of anomalies in the tool 106. These deep learning models are trained on extensive datasets of images that illustrate various tool conditions, such as new tools, tools exhibiting different levels of wear, degradation, and breakage. This training enables the AI to understand complex patterns and characteristics linked to each condition, allowing it to precisely classify the state of the tool 106 in real-time.
[0033] The AI processing unit 108 consists of high-performance hardware, including graphics processing units (GPUs) or dedicated AI accelerators, which facilitate real-time inference. This hardware enables the swift processing of high-resolution images, guaranteeing that anomaly detection is performed with minimal latency.
[0034] The system 100 utilizes a collection of deep learning models, such as convolutional neural networks (CNNs), which have been trained on large datasets of tool images captured under diverse conditions. These models are proficient in assessing the health status of tools, detecting subtle indications of wear, and forecasting potential failures. The models undergo continuous enhancement through incremental learning and data acquisition, allowing them to adapt to new environmental variables and types of tools.
[0035] A plurality of advanced image processing algorithms combined with deep learning models are used by the AI processing unit 108. These algorithms can execute functions such as noise reduction, image enhancement, feature extraction (for instance, detecting edges, cracks, or material accumulation), and pattern recognition. The instantaneous detection feature emphasizes the rapidity and effectiveness with which the AI processing unit 108 can evaluate incoming image data and recognize anomalies in standard tool performance. This real-time evaluation is essential for averting catastrophic failures and reducing production losses.
[0036] The communication interface 110 (not shown in the sketches of any of the drawings of Fig.1 through Fig.2) provided in the system 100 enables the rapid and dependable transfer of data related to identified anomalies. At the instant the AI processing unit 108 detects wear or failure of the tool 106, the system 100 transmits an alert to the CNC machine controller 102 through this communication interface 110. This communication interface 110 may be wired or wireless, depending on the particular industrial setting and the requirements for data transmission speed and reliability. Ensuring the reliability of this communication interface 110 is crucial to guarantee that essential alerts reach the CNC machine controller 102 promptly, allowing for immediate response.
[0037] Upon detection of an anomaly, the AI processing unit 108 sends a structured alert that includes pertinent information such as the type of anomaly, the confidence level, and contextual details, thereby allowing the CNC controller 102 to make well-informed decisions..
[0038] The system 100 is configured to function efficiently in demanding machining environments that are marked by low light, significant vibration, coolant sprays, dust, and metal chips. The design considerations include the sealing of the protective housing of the camera to guard against dust, coolant, and debris, equipping the mountings with vibration isolators to avert image blurring and hardware wear, adjustable illumination of the camera, including integrated LED arrays or infrared lighting in order to preserve the image quality in low-light situations, modifying the camera exposure settings according to the surrounding light conditions, minimizing the motion blur resulting from vibrations using optical stabilization mechanisms and applying infrared or thermal imaging to improve the detection capabilities in extreme lighting or temperature environments.
[0039] The system 100 functions as a continuous feedback loop. While the tool 106 executes its machining operation, the camera module 104 intermittently and/or in response to events captures unobstructed high-resolution images. These images are subsequently transmitted to the AI processing unit 108. The AI processing unit 108 evaluates these images utilizing its trained deep learning models.
[0040] The focus of the analysis is on detecting any anomalies that may indicate the condition of the tool 106. The real-time monitoring of the condition of the tool 106 encompasses, but is not limited to, wear (such as flank wear and crater wear), degradation (including chipping and micro-cracks), and complete breakage of the tool. The non-contact characteristic of this monitoring system 100 presents a significant advantage, as it prevents any physical interference with the machining process, thereby eliminating potential sources of error or damage that could arise from contact-based sensors. Upon the immediate detection of wear or failure, an alert containing information regarding the real-time condition of the tool 106 is promptly sent to the CNC machine controller 102 via the communication interface 110. The swiftness of this alert is crucial, as even a brief delay can result in considerable damage.
[0041] Once the CNC machine controller 102 receives the alert, it processes the data related to the real-time condition of the tool 106 and initiates the execution of pre-programmed corrective actions. These corrective actions are configured to immediately address the tool-related issues and prevent further damage. Examples of such corrective actions include:
- stopping the machining operation-this is often the most immediate and critical action to prevent further damage to the tool, work piece, and machine;
- switching to a backup tool-in automated tool changers, the CNC can be programmed to automatically switch to a pre-loaded backup tool, minimizing downtime;
- fail-safes -these can include activating emergency stops, sounding alarms, or illuminating warning lights to alert the operator.
[0042] The ability of the system 100 to enable the operator to fix tool-related issues immediately upon detection of wear or failure offers substantial benefits. It significantly reduces the risk of unplanned downtime, which is often very costly in industrial settings. It also minimizes material waste by preventing defective parts from being produced due to a compromised tool. Furthermore, by proactively addressing tool issues, the system helps extend the lifespan of tools and machine components, leading to overall cost savings and improved operational efficiency. The continuous, real-time and non-contact monitoring provides a higher level of precision and reliability compared to traditional, often manual, inspection methods and intelligent approach to meet the essential requirement for real-time, non-
[0043] Fig. 2 presents a flowchart depicting the method 200 for monitoring tools and detecting breakage using artificial intelligence (AI). The method 200 consists of a sequence of steps that correspond to the operational capabilities of the outlined system 100. The method includes:
[0044] Step 202: Capturing images of a tool 106 in operation at regular intervals and during specific events.
[0045] The step 202 entails the active acquisition of visual data from the tool 106 while it executes its machining functions. As detailed for the camera module 104, these images are unobstructed and of high resolution, ensuring that the tool's essential features are clearly visible. The image capture can occur either periodically (for instance, every 5 seconds or after every 10 machining cycles) or in response to specific events (such as a sudden rise in motor current, a change in the material being machined, or a particular phase of the machining process).
[0046] Step 204: Controlling the image capture in accordance with the specifications set by a user of said tool 106.
[0047] The step 204 highlights the flexibility and adaptability of the system 100. The user (such as a machine operator or production manager) has the ability to specify the parameters for image capture. This may include determining the frequency of periodic captures, identifying specific events that initiate image capture, or modifying camera settings (such as exposure and focus) to enhance image quality for certain machining conditions or tool types. This level of user control facilitates the customization of the monitoring process to meet specific operational requirements and tool attributes.
[0048] Step 206: Employing multiple deep learning models for the identification and analysis of numerous captured images of said tool 106 for the immediate detection of anomalies in said tool 106.
[0049] The step 206 represents the fundamental step in AI processing. The images of the tool 106 that have been captured are input into the AI processing unit 108, which utilizes its pre-trained deep learning models. These models conduct advanced image analysis to detect and assess the tool 106 for any discrepancies from its typical, healthy condition.
[0050] Step 208: Real-time detection of tool anomalies
[0051] The aim is to achieve immediate identification of anomalies such as wear, chipping, cracking, or total breakage in real-time by comparing current tool condition against trained model parameters. The variety of models may encompass those tailored for various types of wear, different tool geometries, or varying lighting conditions, thereby improving the overall precision and reliability of the detection.
[0052] Step 210: Notifying a computer numerical control (CNC) machine controller 102 by said system 100 at the moment of identifying wear or failure of said tool 106 in question.
[0053] Upon the instantaneous detection of an anomaly (such as wear or breakage)of the tool 106 by the AI processing unit 108, the system 100 promptly generates and sends an alert to the CNC machine controller 102 via the communication interface 110. The necessity of this alert is essential for averting additional damage and facilitating timely intervention.
[0054] Step 210: Making accurate and appropriate decisions by said computer numerical control (CNC) machine controller 102 utilizing real-time data on the condition of said tool 106.
[0055] Upon receiving the notification, the CNC machine controller 102 analyzes the real-time data on the condition of the tool 106 that accompanies the alert.
[0056] Step 212: Commencing the implementation of a pre-programmed corrective action for the immediate resolution of tool-related issues to avert damage to said tool 106.
[0057] Following the decision, the CNC machine controller 102 initiates the implementation of a pre-programmed corrective action. This step 212 is intended for the immediate resolution of tool-related issues and is crucial for averting damage to the tool 106, work piece, and machine. As previously outlined, these corrective actions can include:
- Ceasing the machining process: Stopping the machine to avert additional damage;
- Switching to a secondary tool: Automatically substituting the faulty tool with a new one to maintain production with minimal disruption;
- Safety measures: Engaging safety protocols such as alarms or emergency shutdowns.
[0058] This approach ensures a proactive and intelligent reaction to tool wear and failure, greatly enhancing the efficiency, safety, and cost-effectiveness of CNC machining operations. By incorporating AI-driven visual inspection alongside real-time control, the system and method signify a notable progression in industrial automation and predictive maintenance.
[0059] From the foregoing description, it will be seen that the instant invention is well adapted to attain all ends and objects herein above set forth together with other advantages that are obvious and which are inherent to the structure.
TECHNICAL ADVANTAGES AND ECONOMIC SIGNIFICANCE
[0060] In accordance with the invention as disclosed in the present invention disclosure, a plurality of advantages over the prior art is provided. The system and method of the present invention for monitoring and detecting breakage in AI-based tools present numerous advantages compared to traditional methods. These advantages encompass:
 Real-time, non-contact monitoring of the tool wherein the necessity for physical interaction with the tool is removed, thereby preventing sensor wear and interference with the machining process and additionally continuous monitoring of the tool during operation is facilitated.
 Proactive maintenance wherein the identification of wear and degradation of the tool prior to catastrophic failures is identified thereby enabling scheduled tool replacements and averting unplanned downtime.
 Reduced downtime and costs wherein the unexpected tool breakages and the resulting damage are averted by which the system of the present invention significantly minimizes machine downtime, material waste, and repair expenses.
 Improved product quality wherein the maintenance of a consistent tool condition leads to higher quality and more precise machined components, thereby decreasing scrap rates.
 Enhanced safety wherein the system of the present invention alleviates the risk of machine damage and potential hazards to operators resulting from tool failures.
 Data-driven decision making wherein the artificial intelligence (AI) processing unit delivers critical data on tool wear patterns, which can be utilized for further optimization of machining parameters and tool design.
 Adaptability to challenging environments wherein the durable design of the camera module ensures reliable performance in harsh industrial settings.
[0061] The industrial relevance of the present invention extends across various manufacturing sectors that employ CNC machining, including but not limited to automotive, aerospace, medical device manufacturing, and general precision engineering. The present invention is especially advantageous in high-volume production settings where even minor interruptions can result in significant financial repercussions. The incorporation of artificial intelligence (AI) introduces an intelligent layer of automation that greatly improves the reliability and efficiency of contemporary manufacturing processes.
[0062] The explanation of the present invention serves as an example, and those knowledgeable in the field will recognize that the invention can be readily adapted into various detailed forms without altering the underlying technical concept or any of its essential characteristics. Therefore, it should be understood that the embodiments outlined above are meant to be illustrative in every aspect, rather than limiting. For instance, each component identified as a singular type may be configured to be distributed, and conversely, components described as distributed may also be arranged in a related form. The extent of the present invention is defined by the appended claims and their equivalents, rather than by the detailed description, and it should be understood that the interpretation of the meaning and scope of the claims, along with all alterations or modified forms derived from their equivalents, fall within the purview of the present invention.
, Claims:WE CLAIM
1. A system (100) for artificial intelligence (AI) based tool monitoring and breakage detection, comprising:
- a computer numerical control (CNC) machine controller (102) configured to execute machining operations and receive real-time tool condition notifications;
- a camera module (104) housed within a sealed protective casing and positioned to capture high-resolution images of a tool (106) during operation, wherein said protective casing is configured to withstand machining environmental conditions including coolant, metal shavings, dust, and vibrations;
- at least one artificial intelligence (AI) processing unit (108) configured with a plurality of deep learning models trained to analyze captured images and detect anomalies in said tool (106) including wear, degradation, and breakage in real-time; and
- a communication interface (110) configured to transmit alerts from said AI processing unit (108) to said CNC machine controller (102) upon detection of tool anomalies, and wherein said CNC machine controller (102) is programmed to execute pre-defined corrective actions based on received real-time tool condition data.
2. The system (100) of claim 1, wherein said artificial intelligence (AI) based tool monitoring and breakage detection is a non-contact monitoring and detection.
3. The system (100) of claim 1, wherein said camera module (104) comprising:
- vibration isolators mounted to prevent image blurring and hardware wear;
- adjustable illumination to maintain image quality in low-light conditions, wherein said adjustable illumination selected from the group consisting of integrated LED arrays and infrared lighting;
- optical stabilization mechanisms to minimize motion blur from vibrations; and
- automatic adjustment of exposure settings responsive to ambient lighting conditions.
4. The system (100) of claim 1, wherein said camera module (104) further comprising:
- a high-resolution industrial camera positioned within said sealed protective casing to provide an unobstructed view of a cutting edge of said tool (106);
- at least one environmental adaptation feature selected from the group consisting of infrared imaging capabilities and thermal imaging for extreme lighting or temperature conditions;
- mounting hardware with integrated vibration dampening to maintain image stability; and
- at least one programmable capture mode selected from the group consisting of periodic image capture at predetermined intervals and event-driven capture triggered by machining parameter changes.
5. The system (100) of claim 1, wherein said AI processing unit (108) comprising:
- high-performance hardware including at least one of graphics processing units (GPUs) and dedicated AI accelerators for real-time inference;
- convolutional neural networks (CNNs) trained on extensive datasets of tool images representing a plurality of conditions of said tool (106);
- advanced image processing algorithms configured to perform noise reduction, image enhancement, feature extraction, and pattern recognition; and
- incremental learning capabilities to adapt to new environmental variables and tool types.
6. The system (100) of claim 1, wherein said CNC machine controller (102) is configured with a specialized software to:
- receive and analyze anomaly data from said AI processing unit (108);
- implement corrective actions selected from the group consisting of: halting machining operations, switching to a backup tool, activating safety protocols, engaging emergency stops, and activating warning systems;
- process structured alerts containing anomaly type, confidence level, and contextual details; and
- maintain continuous feedback communication with said AI processing unit (108) during machining operations.
7. The system (100) of claim 1, wherein said corrective action is selected from the group consisting of stopping the machining operation, switching to a backup tool, and fail-safes.
8. A method (200) for artificial intelligence (AI) based tool monitoring and breakage detection, comprising:
a) capturing high-resolution images of a tool during CNC machining operations using a camera module positioned within a protective housing (202);
b) controlling image capture parameters according to user-specified requirements including capture frequency and event triggers (204);
c) analyzing said captured images using multiple deep learning models trained to identify tool anomalies including wear, degradation, and breakage (206);
d) detecting anomalies in real-time by comparing current tool condition against trained model parameters (208);
e) transmitting an alert containing real-time tool condition data to a CNC machine controller immediately upon anomaly detection (210);
f) processing the tool condition data by said CNC machine controller to determine appropriate corrective action (212); and
g) executing pre-programmed corrective actions to address detected tool-related issues and prevent damage (214).
9. The method (200) of claim 8, wherein said step (214) of executing pre-programmed corrective action further includes the steps of stopping the machining operation, switching to a backup tool, and fail-safes.
Dated this 27TH day of June, 2025
Signature of the Applicants’ Patent Agent/Attorney
For HOLLA ASSOCIATES

(Ms. SUMA. H
IN/PA No. 4709