Integrated system, laser machine, method and computer program

Abstract

An integrated system for dynamic optimization and advanced control of a laser machine is described, comprising: a plurality of sensors, a dynamic parameter optimization module for real-time adaptation of cutting parameters, a power saving module for efficient management of energy consumption and a modular software architecture. Furthermore, a method for dynamic optimization of laser cutting is described, comprising the steps of: acquiring operational data, analyzing by means of a machine learning algorithm, dynamically adjusting parameters, continuously monitoring the process and detecting defects. Such a method is performed by means of the aforementioned system, ensuring improvements in precision, energy efficiency and overall quality of the product. The present application also provides a laser machine and a method for dynamic optimization of laser cutting on a laser machine. The present application also provides an integrated system for optimization and advanced control of laser cutting. The present application also provides a computer program.

Description

Technical Field

[0001] This invention relates to an integrated system and method for dynamic optimization and advanced control of industrial laser machines.

[0002] Generally, the present invention relates to systems and methods for the control and optimization of industrial processes, and in particular to systems and methods for the control and optimization of industrial processes involving laser technology and artificial vision.

[0003] In particular, the present invention relates to an integrated system that combines image analysis, machine learning and process control to improve efficiency and accuracy in laser cutting and marking operations. Background Technology

[0004] The existing technology, patent BRPI1003924B1, describes a high-precision intelligent artificial vision system designed for controlling laser cutting and marking processes. This system uses optical sensors and a computerized controller. The optical sensors are coupled to an optical guide perpendicular to the material to be cut, and the computerized controller manages the cutting process, the material to be fed, and the processing conditions. This patent focuses on improving cutting accuracy through direct optical control but does not integrate advanced features such as dynamic parameter optimization, predictive maintenance, or modularity to adapt to different machines.

[0005] Furthermore, prior art, as indicated by patent US11351630B2, relates to an apparatus for monitoring the processing status during a laser process. This system includes a sound collection unit for recording sounds generated during cutting, and an analysis module for interpreting the acoustic signals to detect operational anomalies, such as deterioration in cutting quality or defects in the material. While this system introduces an innovative method for monitoring via acoustic signals, it does not include features using human vision, machine learning, or for real-time optimization.

[0006] Ultimately, the prior art is also represented by patent WO2021234876A1, which relates to a system for creating and training machine learning models used in the analysis of processing status. The proposed technology collects environmental and operational data during the production process to train a machine learning model, thereby improving the production process. However, this technology does not integrate real-time applications for dynamic optimization of parameters or autonomous management of the production process, nor does it address compatibility with different types of machines or energy-saving issues.

[0007] Despite the advancements highlighted in existing technologies, current techniques available for controlling and optimizing laser cutting processes on industrial machines have significant limitations. Specifically: Patent BRPI1003924B1 describes an artificial vision system for precision control in laser cutting and marking processes. However, this artificial vision system is limited to using optical sensors to manage static parameters and does not include dynamic optimization based on real-time data, nor does it integrate advanced functions such as predictive maintenance or energy saving. The lack of interoperability with different machines further reduces its operational flexibility.

[0008] Patent US11351630B2 describes an acoustic monitoring system for detecting anomalies during laser processing. While innovative in its use of acoustic signals, this acoustic monitoring system does not consider integrating other data sources (such as human vision or temperature sensors) for more comprehensive and predictive management of the process. Furthermore, this acoustic monitoring system does not address issues related to energy optimization or multi-machine compatibility.

[0009] Patent application WO2021234876A1 proposes a machine learning-based system for analyzing data collected during the production process and improving its efficiency. However, the described method primarily focuses on creating a learning model rather than applying it in real-time to optimize complex processes such as laser cutting. Furthermore, it lacks direct integration with laser machines and quality control systems.

[0010] In short, existing systems offer partial solutions to specific problems, but lack an integrated platform for coordinating parameter optimization, predictive maintenance, real-time quality control, and energy efficiency. Furthermore, most of these solutions are designed for use with proprietary machines, limiting their applicability and scalability. Summary of the Invention

[0011] The purpose of this invention is to address the aforementioned problems of the prior art by providing an integrated system and method for dynamic optimization and advanced control of the laser cutting process, which can coordinate the aforementioned functions within a single scalable and modular platform.

[0012] The above-mentioned objectives and advantages, as well as other objects and advantages of the invention that will become apparent from the following description, are achieved by the integrated system and method for dynamic optimization and advanced control of industrial laser machines described in the embodiments.

[0013] It should be understood that all appended claims form part of this specification.

[0014] It is obvious that countless variations and modifications can be made to the above description (e.g., relating to shape, size, arrangement, and components with equivalent functions) without departing from the scope of protection of the present invention.

[0015] Beneficial effects of the present invention This invention has several technical and operational advantages, including: - Reduced setup time: Setup time is reduced by up to 40% due to dynamic parameter optimization; - Improve product quality: The quality control module identifies and corrects defects in real time, reducing waste by 25%; - Reduced downtime: Predictive maintenance modules predict faults, reducing downtime by 30%; - Energy saving: The system optimizes consumption during processing, reducing overall energy costs by 20%; - Compatibility and modularity: The system is scalable and compatible with different laser and CNC (Computer Numerical Control) machines, reducing implementation costs and increasing operational flexibility. Detailed Implementation

[0016] Integrated systems for dynamic optimization and advanced control of industrial laser machines include: Multiple sensors are used to detect data during the operation of the laser machine, including ambient temperature and the temperature of the material being processed, and optionally, the data includes vibration of the material being processed. The dynamic parameter optimization module is configured to receive the data in real time and analyze the data through a convolutional neural network to automatically adapt to the cutting parameters of the laser machine, including laser power, gas velocity, and gas flow rate. The energy-saving module is configured to: monitor the operating load and standby phase of the laser machine, dynamically adjust the cutting parameters of the laser machine using linear optimization and proportional-integral-derivative (PID) adjustment algorithms, and generate a detailed energy report on the energy saving situation obtained for each production cycle. The modular software architecture is configured to allow the system to be integrated with lasers of various formats and power levels, as well as CNC controls from different vendors, through open and customizable application programming interfaces (APIs).

[0017] Preferably, the modular software architecture allows for integration with laser machines with power ranging from 1 kW to 80 kW, more preferably from 1 kW to 40 kW.

[0018] Advantageously, modular software architecture can support future updates and the addition of new features without rewriting the entire software.

[0019] Advantageously, such an integrated system also includes an advanced artificial vision module configured to process one or more images of the cut cross-section of the material being processed using deep learning algorithms to identify defects such as burrs, cracks, and deformation, and to generate recommendations on the cutting parameters of the laser machine.

[0020] Advantageously, the integrated system also includes a predictive maintenance module configured to: receive the laser machine's data and energy consumption data from the energy consumption meter, analyze the data and energy consumption data using machine learning algorithms based on predictive regression and recurrent neural networks, predict impending failures, send notifications to the operator, and automatically program maintenance interventions without interrupting the operation of the laser machine.

[0021] Advantageously, the integrated system also includes an interactive user interface configured to display the laser machine's cutting parameters, status, and energy consumption data in real time, send automatic notifications to the operator, and allow the laser machine's cutting parameters to be configured via customizable tools.

[0022] Preferably, the interactive user interface includes automatic notifications to report operational anomalies or maintenance needs, and provides customization options for less experienced operators.

[0023] According to the implementation method, the modular software architecture can be executed on an external computer, and the parameter optimization module is configured to process data locally. In other words, the software is standalone software installed on a computer outside the laser machine.

[0024] According to the implementation, the modular software architecture can be executed in a cloud environment, where the system is configured to receive data from the laser machine via a cloud server, process the data locally via edge computing capabilities and one or more client devices.

[0025] According to the implementation method, the modular software architecture can be executed in an embedded environment integrated into the laser machine, preferably within the CNC control range of the laser machine.

[0026] Advantageously, the artificial vision module is capable of operating in manual mode, where one or more images of the cut cross-section of the material being processed are manually acquired by the operator.

[0027] Therefore, in this embodiment, the operator acquires one or more images and decides to apply recommendations generated by an advanced artificial vision module.

[0028] Advantageously, the artificial vision module can operate in a semi-automatic mode, wherein the advanced artificial vision module includes an industrial camera and light-emitting diode (LED) illumination, and is configured to automatically acquire one or more images of the cut cross-section of the material being processed. In this embodiment, whether to apply the recommendations generated by the advanced artificial vision module is always determined by the operator.

[0029] According to the implementation, the artificial vision module is capable of operating in automatic mode, wherein the advanced artificial vision module includes an industrial camera and LED lighting, and the artificial vision module is configured to automatically acquire one or more images of the cut cross-section of the material being processed, and automatically adjust the cutting parameters of the laser machine according to the generated recommendations.

[0030] A method for dynamic optimization of laser cutting on industrial machines, comprising an integrated system of dynamic optimization and advanced control of an industrial laser machine according to the present invention, the method comprising the following steps: Data is collected in real time, including ambient temperature and the temperature of the material being processed, and optionally, the data includes vibration of the material being processed. The data is analyzed using convolutional and recurrent neural networks integrated into the system modules; Based on the analyzed data, the cutting parameters of the laser machine are dynamically adjusted, including laser power, gas velocity, and gas flow rate. High-resolution industrial cameras are used to monitor the cutting process and detect defects such as burrs and deformation. Generate detailed reports on energy consumption, energy savings achieved, and optimized parameters; The machine learning model is continuously updated based on operating parameters and detected defects, thereby gradually improving process efficiency and quality.

[0031] Advantageously, the method for dynamic optimization of laser cutting on industrial machines further includes the following steps: Real-time collection of energy consumption data from the laser machine; The data and energy consumption data of the laser machine are analyzed by convolutional and recurrent neural networks to identify operational anomalies and predict impending failures.

[0032] According to an implementation, the method further includes: Generate detailed reports on energy consumption, energy savings achieved, and optimized parameters; Based on the cutting parameters of the laser machine and the detected defects, the machine learning model is continuously updated to gradually improve the efficiency and quality of the cutting process.

[0033] This method allows the operation of the parameter optimization module, predictive maintenance module, and artificial vision module of the integrated system to achieve dynamic optimization and advanced control of the industrial laser machine of the present invention, thereby ensuring consistent product quality, energy efficiency, and operational continuity.

[0034] Furthermore, an integrated system for optimized and advanced control of laser cutting is described, comprising: A memory configured to store a machine learning model, operational data collected by sensors, and a computer program adapted to perform all or part of the steps of the above method. A processor configured to execute the steps of the method described above in real time from a computer program contained in the memory. The database is configured to store historical data related to the laser machine's cutting parameters, detected defects, and energy savings achieved. Temperature and energy consumption sensors, and optional vibration sensors; A high-resolution industrial camera connected to an artificial vision module; The energy-saving module is configured to optimize energy consumption during the cutting process and standby phase.

[0035] Advantageously, the processor is configured to process the data in real time using machine learning algorithms based on convolutional and recurrent neural networks.

[0036] Advantageously, the database is configured to support continuous updates to the learning model by integrating new data collected by sensors and cameras.

[0037] Advantageously, the memory is configured to contain generated reports related to energy consumption, optimized parameters, and detected defects.

[0038] In addition, a computer program is disclosed that contains instructions that, when the program is run by a computer, cause the computer to perform the steps of the above-described method.

[0039] Industrial applicability The technology of this invention is applicable to multiple industrial sectors requiring high precision, high efficiency, and high automation in laser cutting and processing. Reference areas include metal processing, aerospace, electronics, automotive, composite materials processing, and custom manufacturing of consumer goods. In particular, this invention is characterized by its ability to integrate adaptive control, predictive maintenance, energy efficiency, and artificial vision into a single modular and scalable platform.

[0040] The fundamental technical benefits include the synergistic effects of integrating these vertical features in laser cutting applications. This synergy allows for measurable advantages, such as significant time savings and substantial reductions in operating costs during production. The integrated approach ensures higher overall efficiency, increased productivity, and optimized resource utilization.

[0041] Another unique element of this invention is its user-friendly interface, designed to be intuitive and accessible even to operators with lower technical skills. Unlike existing technologies that typically require highly specialized personnel, this solution is designed for adoption by a wider range of customers, delivering advanced technology at a simplified level of use. This makes the invention particularly suitable for small and medium-sized enterprises that want to adopt innovative solutions without the need for significant investments in personnel training.

[0042] From a practical standpoint, this system allows for reduced configuration time, improved cutting accuracy, and optimized resource management, making it ideal for high-performance applications. Furthermore, the related processes coordinate energy efficiency, production quality, and operational continuity, effectively responding to the demands of Industry 4.0.

[0043] With its open architecture and ability to integrate with Enterprise Resource Planning (ERP) and Internet of Things (IoT) systems, this technology represents a fundamental step toward achieving full industrial automation and enhances the company’s competitiveness in the global market.

[0044] Preferred embodiments of the invention have been described, but it is apparent that further modifications and variations are readily available within the same inventive concept. In particular, some variations and modifications that are functionally equivalent to those described above will be immediately apparent to those skilled in the art, and these variations and modifications fall within the scope of protection of the invention as emphasized in the appended claims. Furthermore, the word "comprising" does not exclude the presence of elements and / or steps other than those listed in the claims. The article "the" or "a" preceding an element does not exclude the presence of a plurality of such elements. The fact that some features are listed in different dependent claims does not indicate that combinations of these features cannot be used advantageously.

Claims

1. An integrated system for dynamic optimization and advanced control of a laser machine, characterized in that, The integrated system includes: - Multiple sensors for detecting data during operation of the laser machine, including workpiece temperature and ambient temperature; o Dynamic parameter optimization module, the dynamic parameter optimization module being configured to: o Receive the data in real time; and o The data is analyzed by a convolutional neural network to automatically adapt the cutting parameters of the laser machine, the cutting parameters including laser power and / or gas velocity and / or gas flow rate and / or duty cycle and / or focal length; - Energy-saving module, the energy-saving module being configured to: o Monitor the operating load and standby phase of the laser machine; o The cutting parameters of the laser machine are dynamically adjusted using a linear optimization algorithm and PID control; and o Generate a detailed energy report on the energy savings achieved in each production cycle; - Modular software architecture, which is configured to allow the system to be integrated with laser machines of various sizes and powers, as well as CNC systems from different vendors, through open and customizable APIs.

2. The integrated system for dynamic optimization and advanced control of a laser machine according to claim 1, characterized in that, The integrated system also includes an advanced machine vision module, which is configured to: o Use deep learning algorithms to process one or more images of the cut cross-section of the workpiece to identify defects; and o Generate suggestions on the cutting parameters of the laser machine.

3. The integrated system for dynamic optimization and advanced control of a laser machine according to claim 1 or 2, characterized in that, The integrated system also includes a predictive maintenance module, which is configured to: o Receive the data and energy consumption data of the laser machine from the energy consumption meter; o The data and energy consumption data are analyzed using machine learning algorithms based on predictive regression and recurrent neural networks; o Predicting impending failures; o Send notifications to operators; and o Automatically schedule maintenance interventions without interrupting the operation of the laser machine.

4. The integrated system for dynamic optimization and advanced control of a laser machine according to claim 1, characterized in that, The integrated system also includes an interactive user interface, which is configured to: o Real-time display of the laser machine's cutting parameters, laser machine status, and energy consumption data; o Send automatic notifications to operators; and o Allows the cutting parameters of the laser machine to be configured via customizable tools.

5. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 1 to 4, characterized in that, The modular software architecture can be executed on an external computer, and the parameter optimization module is configured to process data locally.

6. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 1 to 4, characterized in that, The modular software architecture is capable of running in a cloud environment, and the system is configured to: - Receive data from the laser machine via a cloud server; - Process data locally using edge computing capabilities and one or more client devices.

7. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 1 to 4, characterized in that, The modular software architecture can be executed in an embedded environment integrated into the laser machine.

8. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 2 to 7, characterized in that, The machine vision module is capable of operating in manual mode, wherein the one or more images of the cut cross-section of the workpiece are acquired manually by the operator.

9. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 2 to 7, characterized in that, The machine vision module is capable of operating in a semi-automatic mode, wherein the advanced machine vision module includes an industrial camera and LED lighting, and is configured to automatically acquire one or more images of the cut cross-section of the workpiece.

10. The integrated system for dynamic optimization and advanced control of a laser machine according to any one of claims 2 to 7, characterized in that, The machine vision module is capable of operating in automatic mode, wherein the advanced machine vision module includes an industrial camera and LED lighting, and the advanced machine vision module is configured to: - Automatically acquire one or more images of the cut cross-section of the workpiece; and - The cutting parameters of the laser machine are automatically adjusted based on the generated suggestions.

11. The integrated system for dynamic optimization and advanced control of a laser machine according to claim 1, characterized in that, The data includes vibrations of the material being processed.

12. The integrated system for dynamic optimization and advanced control of a laser machine according to claim 2, characterized in that, The defects are burrs, cracks, or deformations.

13. A laser machine comprising an integrated system for dynamic optimization and advanced control of the laser machine according to any one of claims 1 to 12.

14. A method for dynamic optimization of laser cutting on a laser machine, characterized in that, The method includes using an integrated system for dynamic optimization and advanced control of an industrial laser machine according to any one of claims 1 to 12, wherein the method includes the following steps: - Collect data in real time, including the temperature of the workpiece and the ambient temperature; - The data is analyzed using convolutional and recurrent neural networks integrated into the modules of the system; - Based on the analyzed data, the cutting parameters of the laser machine are dynamically adjusted, including laser power, gas velocity, and gas flow rate; - Monitor the cutting process and detect defects using high-resolution industrial cameras; - Generate detailed reports related to energy consumption, energy savings achieved, and optimized parameters; - The machine learning model is continuously updated based on the operating parameters and detected defects, thereby gradually improving the efficiency and quality of the process.

15. The method for dynamic optimization of laser cutting on a laser machine according to the preceding claim, characterized in that, The method includes the following steps: - Collect energy consumption data of the laser machine in real time; - The data and the energy consumption data of the laser machine are analyzed by convolutional and recurrent neural networks to identify operational anomalies and predict impending failures.

16. The method for dynamic optimization of laser cutting on a laser machine according to any one of claims 14 to 15, characterized in that, The method includes the following steps: - Generate detailed reports related to energy consumption, energy savings achieved, and optimized parameters; - Based on the cutting parameters of the laser machine and the detected defects, the machine learning model is continuously updated to gradually improve the efficiency and quality of the cutting process.

17. The method for dynamic optimization of laser cutting on a laser machine according to claim 12, characterized in that, The data includes vibrations of the material being processed.

18. The method for dynamic optimization of laser cutting on a laser machine according to claim 12, characterized in that, The defects are burrs, cracks, or deformations.

19. An integrated system for optimized and advanced control of laser cutting, characterized in that, The integrated system includes: - A memory configured to store a machine learning model, runtime data collected by sensors, and a computer program adapted to perform all or part of the steps in the method according to any one of claims 14 to 18. - A processor configured to execute a computer program contained in the memory and to implement the steps of the method; - A database configured to store historical data related to the laser machine's cutting parameters, detected defects, and energy savings achieved; - Temperature and energy consumption sensors; - A high-resolution industrial camera connected to the machine vision module; - An energy-saving module configured to optimize energy consumption during the cutting process and standby phase.

20. A computer program comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 14 to 18.