Automatic tire superficial damage repair system using a robot and 3D vision sensor

Abstract

The invention relates to an automatic repair system for repairing the casing of an identified tire during retreading, comprising: - at least one repair device; - a detection system for gathering information on the physical environment around the repair device; - a communication network that manages the incoming data to the automatic repair system from each repair device; - a first neural network that allows the identification of casing areas containing damage to be treated; - a second neural network that allows the classification of each damage to be treated; and - a third neural network that allows the selection of at least one identified processing tool to perform a corresponding repair;so that the repair device is configured on one or more parameters of the identified tire to operate the processing tool in a carcass area containing detected and classified damage to be treated. Figure for the abbreviation: Fig. 10;

Description

Title of the invention: Automatic tire superficial damage repair system using a robot and 3D vision sensor. Technical field

[0001] The invention relates to an automatic repair system for a tire casing during retreading. More particularly, the invention relates to the use of a repair device carrying a processing tool that is capable of repairing the casings of tires identified for retreading. Context

[0002] In the field of tire manufacturing and maintenance, retreading is a process known to restore a worn tire to working order by renewing the tread compound and the ply(ies). During the retreading process, the old tread material is removed and replaced with new material. Retreading is a method that is known and regulated in several industries (including, without limitation, the transportation, aviation, mining, and agricultural industries). Damage could fall into one or more categories (or "classifications") of damage (including, without limitation, tread damage, tear damage, cut damage, and puncture damage).

[0003] Regardless of the type of tires retreaded, the steps of a retreading process are substantially the same from the entry of a tire into a retreading facility until its exit.

[0004] During the retreading process, a repair process is carried out in which the tires intended for retreading are examined so that existing damage can be identified and located. During the repair process, a visual inspection of the damage is performed (either by manual means or by automatic means (e.g., one or more cameras) or by a combination of both). If the focus is on repairing "tread" type damage, during the repair process, a cleaning step is carried out using compressed air and a processing tool (e.g., a soft brush). Then, a step of removing the remaining rubber is carried out using a processing tool (e.g., a hard brush) to expose the first top layer. The repair process continues with a visual inspection for the presence of corrosion on the wire cords (either by The inspection can be carried out manually, automatically (e.g., using one or more cameras), or a combination of both. If visual inspection confirms the presence of corrosion, a removal step is performed using a processing tool such as a grinding wheel. These corrosion detection / removal steps are repeated on successive layers of the tire as long as corrosion is detected. After the repair process is completed, the retreading process can proceed in a typical sequence of steps toward the tire exiting the retreading facility (the steps including, but not limited to, a tread application step, a curing step, an inspection step, and a shipping step). Up to this point, one or more steps in the damage repair process are performed manually.

[0005] There are ongoing efforts to automate at least part of the repair process. For example, European patent EP2456612B1 discloses an automatic spraying system for dissolving adhesive during tire retreading. The adhesive spraying occurs after the repair process and just before the tread is applied. The adhesive is sprayed into holes and / or craters automatically detected by acquiring a three-dimensional (or "3D") profile obtained using a profilometer. The sprayed adhesive is then applied to the holes and / or craters by an automatic applicator.

[0006] European patent EP3551443B1 discloses an automatic damage repair system for tire retreading. The disclosed system includes a remote, fixed laser profilometer that scans the entire tread by rotating the carcass. The system also includes a color camera mounted on a robot that takes pictures of damage once the covering rubber has been removed. These color images are used to perform corrosion detection by analyzing the colors in the image (for example, by looking for the color red on cords). The processing algorithm uses the three-dimensional (3D) information from the profilometer to detect damage. The processing used to detect damage includes constructing an average profile and comparing it to this profile. This processing would be sufficient if the carcass could be considered a rigid body.However, a frame is more accurately described as a deformable body that can exhibit numerous deformations, some more localized than others (for example, a more or less pronounced runout). These deformations can be considered as damage deviating from an average profile. Therefore, the disclosed system cannot adapt to all possible damage.

[0007] European patent EP2414153B1 discloses a machine that performs an automatic process for identifying and repairing defects on used tires. The disclosed machine includes a vision system based on a projector Infrared technology is used to obtain 3D information about the damage being analyzed. During the process, an automatic scan of the tire's working surface identifies the damage morphology and compares it to a reference of known damage. A job and a corresponding tool are then selected to perform the work on the identified damage.

[0008] There is nothing in the prior art that proposes establishing a link between the identification and localization of damage and the repair of damage using a processing tool in a completely automated manner. Thus, the disclosed invention relates to the automatic repair of all damage requiring repair on the surface of a carded carcass and / or on the surface of the first metal plies encountered under the rubber. An algorithmic process relies on training images so that the algorithm learns to recognize the points in the image (three-dimensional and color) belonging to a carcass area of ​​a tire identified for retreading. This approach is scalable and will become increasingly robust as it is possible to integrate images of undetected carcass into the training in order to extend the scope of the algorithm's application.Furthermore, carcass repair does not depend on any predefined recipe because the treatment trajectory is defined on the fly according to the shape of the carcass. Summary of the invention

[0009] The invention relates to an automatic repair system for repairing the casing of an identified tire during retreading, the automatic repair system comprising: - at least one repair device comprising a robot having a processing peripheral supported by an elongated pivoting arm and extending from the elongated arm to a free end which allows the installation of a processing tool along a common longitudinal axis; - a detection system to gather information on the physical environment around the repair device; - a communication network that manages incoming data to the automatic repair system from each repair device, the communication network including at least one communication server to execute programmed instructions stored in a memory of one or more processors of the automatic repair system to store an application for analyzing data representative of the carcasses of the imaged tires; - a first neural network configured to take as input color and three-dimensional (3D) images of the identified tire to determine which points represent carcass areas containing damage to be treated; - a second neural network configured to take as input images of carcass areas containing the damage to be treated and configured to output a classification of each type of damage to be treated; and - a third neural network configured to take as input images of carcass areas containing classified damage and configured to output at least one identified processing tool to perform a corresponding repair; so that the repair device is configured on one or more parameters of the identified tire calculated by an image processing module incorporated in the processor's memory to automatically indicate the identified repair tool to repair the damage present in each image and to set the repair device in motion to repair the identified tire in each carcass area containing damage to be treated.

[0010] In certain embodiments of the automatic repair system of the invention, the automatic repair system further comprises a safety system including: - a first electrode which is arranged to be placed opposite the carcass of the identified tire, at a distance from an electrically conductive insert of the identified tire, so as to form with the electrically conductive insert a first dipole, so that a first terminal is formed by the first electrode and a second terminal is formed by the electrically conductive insert; - a second electrode which is associated with the processing tool in such a way that, when the processing tool comes into contact with the electrically conductive insert, an electrical connection is established between the second electrode and the electrically conductive insert forming the second terminal of the first dipole; and - a control unit which is arranged to measure an impedance of a detection circuit containing the first dipole and to detect a variation in impedance of the detection circuit caused by the electrical connection of the first dipole with the second electrode caused by the entry of the processing tool with the electrically conductive insert; so that the repair device stops its movement where it contacts a metal layer of the tire identified during a retreading process.

[0011] In certain embodiments of the automatic repair system of the invention,

[0012] The repair device detection system includes one or more sensors configured to perform two-dimensional (2D) and / or three-dimensional (3D) image detection.

[0013] In certain embodiments of the automatic repair system of the invention, the detection system of the repair device includes at least one RGB-D type camera fixed to at least one of the extended arm and the processing device (102) of the repair device.

[0014] In certain embodiments of the automatic repair system of the invention, the data entering the automatic repair system includes general information concerning the identified tire.

[0015] In certain embodiments of the automatic repair system of the invention, the input data to the automatic repair system includes one or more CAD files of the identified tire to match the location of a carcass area of ​​the identified tire from the CAD file.

[0016] In certain embodiments of the automatic repair system of the invention, the detection data obtained from the detection system includes data representative of one or more continuous movements of the repair device before it stops to take one or more images of the tires identified during retreading.

[0017] In certain embodiments of the automatic repair system of the invention, the processing tool includes at least one tool selected from brushes, knives, rasps, carders, milling cutters and grinding wheels for repairing tires during the retreading process.

[0018] In certain embodiments of the automatic repair system of the invention, the communication network is configured to allow the execution of programmed instructions stored in memory to implement a repair process for the carcass of the identified tire, the repair process comprising the following steps; - a calibration step for the repair device; - a step of positioning the identified tire in a processing area where the repair device processes it; - a positioning step of at least one camera of the detection system at a reference distance from a carcass of the identified tire; a damage detection step including a learning phase in which images obtained are annotated to indicate where carcass areas containing damage to be treated are located; - a classification step for each type of damage to be treated, using a classification system associated with a corresponding treatment; - a step of choosing at least one processing tool suitable for addressing each type of damage in the images obtained; and - a step in carrying out a process of repairing the damage detected and classified in the images obtained.

[0019] In certain embodiments of the automatic repair system of the invention, the damage repair process of the repair method implemented by the automatic repair system includes the following steps: - a step of mounting the processing tool to be used to repair the identified and classified damage; - a step of defining a surface trajectory for the treatment tool based on the identified and classified damage; and - a step to repair the damage to be addressed.

[0020] The invention also relates to a retreading installation comprising the disclosed automatic repair system.

[0021] Other aspects of the invention will become evident from the following detailed description. Brief description of the drawings

[0022] The nature and various advantages of the invention will become more evident upon reading the following detailed description, together with the accompanying drawings, in which the same reference numbers designate identical parts throughout, and in which: [Fig.1] Fig.1 represents a schematic cross-sectional view of one embodiment of a known tire. [Fig.2] Fig.2 represents a schematic view of an embodiment of a repair device which is part of an automatic repair system of the invention. [Fig.3] Fig.3 represents an example of a visualization of a part of a tire obtained during a repair process of the invention. [Fig.4] Fig.4 represents a schematic view of the positioning of a camera of the automatic repair system of the invention relative to a carcass of an identified tire during retreading. [Fig.5] The [Fig.5] represents a schematic view of the input of color and 3D images to a neural network in determining the points that represent carcass areas containing damage to be treated. [Fig.6] Fig.6 represents a schematic view of input of color and 3D images containing damage to a neural network in the classification of the damage according to the processing to be carried out. [Fig.7] Fig.7 represents a schematic view of input of color and 3D images containing classified damage to a neural network in the choice of a processing tool to repair the damage. [Fig.8] The [Fig.8] represents an example of a carcass region containing detected and classified damage to be treated. [Fig.9] The [Fig.9] represents an example of cutting and shifting a carcass region containing detected and classified damage to be treated. [Fig. 10] The [Fig. 10] represents an example of a surface trajectory obtained to achieve a displacement of a processing tool. [Fig. 11] The [Fig. 11] represents a front overview of an embodiment of a safety system of the automatic repair system of the invention during a repair process. Detailed description

[0023] When considering the characteristics of a tire for retreading, its geometry must be taken into account. A tire is an object with a known geometry, generally comprising several superimposed layers of rubber (or "layers"), as well as a metallic or textile fiber structure constituting a carcass that reinforces the tire's structure. The type of rubber and the type of reinforcement are chosen according to the desired final characteristics. Figure 1 includes a schematic representation of a tire 10, which, conventionally, includes two circumferential beads designed to allow the tire to be attached to a rim. Each bead includes an annular reinforcing bead. The construction of a tire is typically described by a representation of its components in a meridian plane, that is, a plane containing the tire's axis of rotation.The radial, axial, and circumferential directions respectively refer to the directions perpendicular to the tire's axis of rotation, parallel to the tire's axis of rotation, and perpendicular to any meridian plane. The terms "radially," "axially," and "circumferentially" mean, respectively, "along a radial direction," "along the axial direction," and "along a circumferential direction" of the tire. The terms "radially inside" and "radially outside" mean "closer to, or farther from, the tire's axis of rotation, respectively, along a radial direction."

[0024] The tire 10 also includes a tread 12 intended to come into contact with a ground via a tread surface 12a. It is reinforced by a frame, or "carcass," which generally comprises a plurality of reinforcing layers, each having a plurality of reinforcing cables embedded in a layer of rubber-based material. More specifically, a top frame is provided, which includes a working frame 14 and a reinforcing frame 16. The working frame 14 has working layers represented by layers 14a and 14b.

[0025] The tire 10 also comprises two sidewalls (one sidewall 18 being shown in [Fig. 1]) and two reinforced ribs 20 with a bead 22. A radial carcass layer 24 extends from one rib to the other, surrounding the bead in a known manner. The tread 12 has reinforcements made up, for example, of superimposed layers having known reinforcing threads. In some embodiments, the tire may include a rubber compound 26 that dissipates the static electricity produced during rolling.

[0026] The tread 12 is bounded, in the radial direction, by two circumferential surfaces, the outermost of which is the tread surface 12a and the innermost of which is called the tread base surface. The tread base surface (or "bottom surface") is defined as the surface of the tread surface translated radially inward by a radial distance equal to the tread depth. It is common for this depth to decrease over the outermost axially circumferential portions (called "shoulders") of the tread 12.

[0027] Furthermore, the tread of a tire is delimited, along the axial direction, by two lateral surfaces. The tread is further constituted by one or more rubber compounds. The term "rubber compound" designates a rubber composition comprising at least one elastomer and a filler.

[0028] To achieve good grip on wet surfaces, cutouts are arranged in the tread 12. A cutout is defined as either a well, a groove, an incision, or a circumferential groove, and forms a space opening onto the tread surface 12a. The performance of a tread pattern must remain sufficiently consistent despite wear to ensure the tire's longevity. Consequently, it is necessary to maintain a certain thickness of rubber material between the bottom face of the cutouts (grooves or grooves) and the reinforcing elements to guarantee the tire's durability. For example, grooves must be wide enough to allow the evacuation of liquid present on the ground surface regardless of the stage of wear of the tread (12).

[0029] With reference to the figures, in which the same numbers identify identical elements, [Fig. 2] represents an embodiment of a repair device 100 that forms part of an automatic repair system (or "system") of the invention. The automatic repair system of the invention implements a method of the invention ("method") enabling the repair, by means of a processing tool, of the casing of an identified tire during retreading. The disclosed method incorporates a machine learning method based on data corresponding to images obtained of the identified tire. The algorithm used analyzes the casing of the identified tire to position and operate the processing tool within a processing area of ​​the identified tire. It is understood that the method of the invention can be incorporated into an existing retreading process to improve its repair process.

[0030] In one embodiment of the repair device 100 of the system of the invention, the repair device comprises a robot having a processing unit 102 supported by a pivoting extended arm 104. The processing unit 102 extends from the extended arm 104 to a free end 102a which allows the installation of a processing tool 106 along a common longitudinal axis. By way of example, the processing tool 106 shown in [Fig. 2] comprises a brush which is selected from commercially available industrial brushes for repairing tires during retreading processes (for example, industrial rotary brushes made of synthetic or metallic filaments). It is understood that the processing tool 106 may comprise one or more other equivalent processing tools (including, without limitation, brushes, knives, rasps, carding tools, milling cutters, grinding wheels, and their equivalents).

[0031] The processing tool 106 is fixed in such a way as to allow its rotation about the longitudinal axis and also to allow its spinning around a predefined axis of rotation during a repair process. It is understood that the processing tool 106 is fixed to the free end 102a of the processing device 102 by one or more known fastening means (including, without limitation, welding, bonding, and equivalent means). Thus, the robot facilitates the repair of a variety of carcasses without interrupting the rotation and / or spinning of the processing tool 106.

[0032] The repair device 100 is set in motion so that the processing device 102 can repair the casing of a tire identified P (see [Fig. 2]). The casing repair is carried out by the repair device during a repair process implemented by the automatic repair system of the invention (as described below). The term "identified tire" (in the singular or plural) is used here to make reference to a tire being retreaded and present in the physical environment of the automatic repair system incorporating the repair device 100 (the physical environment being, for example, a retreading facility incorporating the repair device 100).

[0033] It is understood that the configuration of the repair device 100 is given by way of example. For example, the repair device 100 may include a fixed robot installed at a retreading facility, fixed, for example, to a support relative to which the robot extends (for example, a base 110 as shown in [Fig. 2]). In this case, it is understood that the robot may be fixed to a ceiling, a wall, a floor, or any support that allows the process of the invention to be carried out during a retreading process. It is understood that the repair device 100 may include at least one mobile robot. By "mobile," it is understood that the gripping device may be set in motion either by integrated motion means (for example, one or more integrated motors) or by non-integrated motion means (for example, one or more mobile means, including autonomous mobile means).It is understood that the repair device 100 can be a conventional industrial robot, a collaborative robot, or even a delta or cable robot.

[0034] The carcasses that can be repaired by the repair device 100 during the repair process include grooves and channels (but it is understood that the repair device is also capable of handling slick-type tires). The grooves are the cutouts that allow water to be evacuated. A groove has a width such that the opposing material walls delimiting it do not come into contact with each other when the tread passes through the contact patch, when the tire is subjected to recommended inflation and load conditions as defined in particular by the European standards of the European Tyre and Rim Technical Organization (ETRTO) in its Standards Manual 2020 - Commercial Vehicle Tyres.The compression and shear deformations of the raised elements defining the groove determine the pressures in contact with the ground and therefore the wear.

[0035] A groove is a substantially circumferential channel, and the lateral faces are substantially circumferential in that their orientation can vary locally by plus or minus 45° around the circumferential direction. All the motifs belonging to the groove are found all around the tread, forming a substantially continuous whole (i.e., exhibiting discontinuities of less than 10% in length compared to the length of the motifs). The circumferential grooves delimit ribs, each rib of which is composed of the motifs of the tread pattern included between an axial edge of the tire and the nearest axially outermost circumferential groove, or included between two neighboring circumferential grooves.

[0036] The depth of the cut is the maximum radial distance between the rolling surface of the tire and the bottom of the cut.

[0037] The repair device 100 of the automatic repair system also includes a detection system (not shown) for gathering information about the physical environment around the repair device. The detection system includes one or more sensors (including one or more cameras) configured to perform two-dimensional (2D) and / or three-dimensional (3D) image detection, 3D depth detection, and / or other types of detection of the physical environment around the repair device (it is understood that the terms "sensor" and "camera" are used interchangeably). In embodiments of the repair device 100 of the type shown in [Fig. 2], the sensor(s) of the detection system are attached to at least one of the extended arm 104 and the processing unit 102 of the robot.In embodiments of the system of the invention, these sensors could be part of an overall detection system that uses these sensors together with one or more sensors positioned in the physical environment in which the repair device 100 operates.

[0038] In one embodiment of the repair device 100, the detection system includes at least one camera 200 that provides 3D images represented as a set of 3D points with X, Y, Z coordinates and red, green, and blue color values ​​(the “RGB” or “RGB-D” format) (referred to as “an RGB-D type camera”). In this embodiment, an RGB-D type camera is attached either to the extended arm 104 or to the processing unit 102 of the robot (the latter being shown by way of example in [Fig. 2]). Two or more RGB-D cameras can be oriented so as to obtain a predetermined overlap between the fields of view of the cameras. As used herein, the term “camera” includes one or more cameras.

[0039] RGB-D cameras generally provide depth information using depth maps, which are images where each pixel contains the distance between the camera and the corresponding point in space. Compared to traditional measurement methods such as manual measurement and other measurements based on electronic devices, 3D point cloud data from RGB-D cameras has a much higher measurement rate. By using a more sparse structure, a point cloud can be constructed from RGB-D images by calculating the real world (e.g., X, Y, Z coordinates) using the intrinsic data of a scanning camera. Thus, environmental information Physical data surrounding the repair device 100 is obtained from 3D point cloud data generated by sensing technologies capable of accurately and efficiently capturing the 3D surface geometries of tires. These sensing technologies could be selected from commercially available devices (for example, cameras sold under the PHOTONEO™ brand, cameras sold under the ENSENSO® brand from IDS, cameras sold under the ZIVID® brand from Zivid AS, computer vision systems sold by Cognex Corp., and their equivalents).

[0040] The term "point cloud" (in the singular or plural) is used here to refer to one or more collections of data points in space. One or more cameras (or equivalent devices) collect three-dimensional (3D) data and detect the surfaces of objects (for example, tires identified for processing during a retreading process, of which the repair process is a part) using a series of coordinates. Storing information as a collection of spatial coordinates can save space, since many objects do not occupy a large portion of the environment. Even though the information is not visual, interpreting the data as a point cloud helps to understand the relationship between several variables through classification and segmentation.

[0041] It is understood that one or more cameras may include one or more programming modes, including learning, for feeding, modifying, and training at least one neural network. Although the embodiments described here have regard to the use of one or more neural networks (for example, convolutional neural networks, or "CNNs") as a machine learning model, other types of machine learning models may be used.These include, but are not limited to, models using linear regression, logistic regression, decision trees, support vector machines, naive Bayes, nearest neighbor (knn), K stands for grouping, random forest, dimensionality reduction algorithms, gradient descent algorithms, neural networks (e.g., autoencoders, CNNs, RNNs, perceptrons, log short-term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative confrontation (GAN), etc.) and their complements and equivalents. The CNN(s) can be trained with ground truth data that is generated using sensor data representative of the movement of the repair device 100, including the positioning of the processing device 102 and the processing tool 106.

[0042] In certain embodiments of the repair device 100, the camera 200 is triggered when an identified segment of the tire enters the camera's field of view. In cases where a part of the tire is not visible in the image obtained by the detection system of the repair device 100 (for example, the camera 200), a mounting point can be placed at a known position relative to the sensor (for example, at a known horizontal distance and a known vertical distance from the camera position).

[0043] The detection system of the repair device 100 detects the presence of a carcass damage pattern within the field of view of the detection system (for example, the field of view of the camera 200), which triggers it to capture an image of a tread surface of the tire identified for retreading. In all embodiments of the system of the invention, the system "searches" in the image obtained by the detection system for the presence of the damage "seen" by the repair device 100. If no damage is detected, the detection system continues to acquire images until the search for the tire is exhausted.

[0044] The detection system can determine information about the physical environment around the identified tire, which can be used by a control system for the automatic repair system of the invention (the control system comprising, for example, motion planning software for the repair device 100). The control system could be located on the robot or it could be in remote communication with the robot. In embodiments of the automatic repair system, one or more 2D or 3D sensors mounted on the robot (including, without limitation, navigation sensors) can be integrated to constitute a digital model of the physical environment (including, where applicable, the side(s), the floor, and the ceiling).Using the data obtained, the control system can cause the robot to move to navigate between the positions where images of the tires were taken during repair processes.

[0045] To properly manage the handling of the repair device 100, which ensures clear image acquisition of the identified tire (for example, the handling of the robot and the positioning of the processing tool 106 as shown in [Fig. 2]), it is necessary to identify the tire being retreaded and detect the positioning of the relevant damage. Thus, the detection data refers to a plurality of representative records of the damage positions of at least one identified tire or a portion of an identified tire tracked over time. For example, the detection data may include one or more positions among records of the positions of a reference point on a portion of the tire (for example, the casing) over time or at time intervals. defined; sensor data captured over time; a video stream processed using computer vision; and / or data indicating the operating status of the repair device 100 over time. In some cases, the detection data may include representative data of one or more continuous movements of the repair device 100 before it stops to take one or more images of the tires being retreaded. The detection system is therefore configured to generate motion data for the repair device 100.

[0046] The detection system of the repair device 100 can receive a CAD file of the identified tire to match the location of a carcass area from the CAD file with the identified carcass area in real time to precisely locate and determine its coordinates. The detection system can receive the CAD file using data transmission methods known to those skilled in the art. The detection system may further include at least one camera and at least one sensor (not shown) to determine the location (i.e., the coordinates) of the damage based on the data collected in real time and / or the contour profile of the identified tire.

[0047] In embodiments of the invention, the detection system for the repair device 100 may also include a motion capture device selected from infrared sensors, ultrasonic sensors, accelerometers, gyroscopes, pressure sensors, and / or equivalent devices. In these embodiments, the system (and particularly the repair device 100) learns the movements that perform image capture of the tires without operator intervention during the retreading processes.

[0048] To implement the method of the invention by computer means, the automatic repair system of the invention comprises a communication network (or "network") that manages the data entering the system from various sources (for example, from at least one repair device 100 and its associated detection system). The communication network incorporates one or more communication servers (or "servers"), each comprising one or more processors operationally connected to a memory. The memory is configured to store an application for analyzing data representative of the carcass areas of the imaged tires. The processor(s) include an analysis execution module that performs image processing, and the processor(s) are capable of executing programmed instructions stored in the memory to implement the steps of the repair process (as described below).

[0049] The data entering the automatic repair system of the invention may include general information concerning the identified tire. The information General information includes stored data concerning the identification of the identified tire (including, without limitation, its place of production, distribution and / or storage, production date, retreading history if applicable, and its position and mounting history). General information may also include the retreading rank (if applicable) of the identified tire. Data corresponding to a retreading rank of an identified tire is usually managed by the entity that manages the use of one or more identified tires (e.g., one or more individuals and / or companies) and / or the manufacturer of such tires.

[0050] The term "processor" (or, alternatively, the term "programmable logic circuit") refers to one or more devices capable of processing and analyzing data and comprising one or more software programs for their processing (for example, one or more integrated circuits known to those skilled in the art as being included in a computer, one or more controllers, one or more microcontrollers, one or more microcomputers, one or more programmable logic controllers (or "PLCs"), one or more application-specific integrated circuits, one or more neural networks, and / or one or more other known equivalent programmable circuits).The processor includes one or more software programs for processing the data captured by the detection system (including the camera 200) of the repair device 100 (and the corresponding data obtained) as well as one or more software programs for identifying and locating variances and identifying their sources in order to correct them.

[0051] In the system of the invention, the memory may comprise both volatile and non-volatile memory devices. The non-volatile memory may include solid-state memories, such as NAND flash memory, keep-alive memory (or KAM) for saving various operating variables while the processor is powered off, magnetic and optical storage media, or any other suitable data storage device that retains data when the repair device is deactivated or loses its power supply. The volatile memory may include static and dynamic RAM that stores program instructions and data, including one or more learning applications.

[0052] In embodiments of the system of the invention, the processor can configure the repair device 100 (and in particular the processing device 102) on one or more parameters of the identified tire calculated by an image processing module incorporated in the processor's memory. The image processing module analyzes the images of the identified tire and, more Specifically, the images of the damage obtained by the detection system of the repair device 100.

[0053] The processor can also refer to a reference (for example, a table of various tire sizes) to perform a final determination of one or more parameters of the identified tire. The reference can include parameters of known tires corresponding to a plurality of commercially available known tires. For example, after the image processing module has calculated one or more parameters of the identified tire, the processor can compare the calculated parameters with the known parameters stored in the reference. The processor can retrieve the parameters of known tires corresponding to commercially available tires that most closely match the calculated parameters to configure the processing device 102 (and thus position the processing tool 106 precisely to repair damage in a corresponding area of ​​the carcass of the identified tire).The tire reference may include measurements corresponding to a plurality of commercially available tires. For example, for a tire size 225 / 50R17, the number "225" identifies the tire's cross-sectional area in millimeters, the number "50" indicates the aspect ratio of the sidewall, and the measurement "R17" represents the rim diameter in inches (being approximately 43.18 centimeters).

[0054] With further reference to [Fig. 2], and moreover to Figures 3 to 10, a detailed description is given by way of example of embodiments of a method of the invention (or "method") enabling the automatic repair of a tire casing during retreading. The method of the invention is implemented by the automatic repair system of the invention. It is understood that the system can implement the method in any physical environment without prior knowledge of the configuration of damage to be repaired on the tires identified for retreading.

[0055] As used herein, the term “method” or “process” may include one or more steps performed by at least one computer system comprising one or more processors to execute instructions that perform the steps. Unless otherwise indicated, any sequence of steps is given by way of example and does not limit the described methods to any particular sequence.

[0056] In the following description, embodiments of the method of the invention are described in which the accuracy of the information obtained by the detection system (for example, the camera 200) differs.

[0057] By implementing the process of the invention, the automatic repair system of the invention incorporates a combination of vision and learning techniques The system is designed to automatically and accurately reconstruct the observed scene from three-dimensional (or "3D") scattered point clouds derived from a view of the tire identified for retreading. The system thus achieves continuous improvement in the recognition of casing damage and its relative positioning along the tread surface of the identified tire.

[0058] By initiating an embodiment of the method of the invention, the method includes a calibration step for the repair device 100. Prior to this step, the robot is positioned to scan an identified tire. During this step, when a scan of the identified tire is performed with the camera 200, three-dimensional (3D) points are acquired, which are expressed by their positioning relative to the camera. In order to control the repair device 100 (for example, to indicate the position of each area to be treated on the carcass of the identified tire), these 3D points must be expressed at the robot's base (for example, see the base 110 of the repair device 100 in [Fig. 2]). The calibration step of the repair device 100 therefore includes a calibration phase to estimate the position of the camera 200 relative to its base 110.

[0059] In one embodiment of the method, the calibration step of the repair device 100 includes a step of positioning a calibration target in a processing area where the robot will work. In one embodiment, the calibration target comprises a checkerboard pattern positioned to allow for taking multiple photos by varying the viewing angle each time. During this step, an optimization algorithm (incorporated, for example, in a processor and executed remotely by the camera 200) is used to estimate the position of the camera 200 within the coordinate system of the processing device 102. Once the calibration is complete, the robot can be positioned as seen by the camera 200. This calibration must be performed as soon as the repair device 100 is installed in the processing area.

[0060] The method further includes a step of positioning a damaged, identified tire in the processing area where the repair device 100 will process it. The identified tire is positioned near the repair device 100 on a rotating drum (as known in the prior art). During the repair process of the retreading method, the drum rotates the identified tire so that it can be processed sector by sector. It is understood that the identified tire could be positioned on a worktable or equivalent support so that the system of the invention can process it. The support can be configured to move rotaryly, alternately vertically and / or alternately horizontally, thus allowing the processing of a variety of tires.

[0061] The "sector-by-sector" treatment refers to several embodiments of the process of the invention incorporating one or more scans followed by treatment of the carcass of the identified tire. In one embodiment of the process of the invention, the sector-by-sector treatment comprises, for each sector of the tire being treated, a scan of the sector followed by treatment of the scanned sector (for example, treatment of the carcass areas of the scanned sector performed by the treatment tool 106). In another embodiment of the process of the invention, the sector-by-sector treatment comprises a scan of all successive sectors of the tire being treated followed by treatment (for example, brushing, machining, and / or grinding) of all successive scanned sectors.In another embodiment of the invention, the sector-by-sector treatment comprises scanning the tire being treated (either a scan of individual sectors or a scan of all successive sectors) at a station different from the one where treatment (for example, brushing, machining, and / or grinding) of the scanned sectors is performed. In the latter case, at least one additional camera (not shown) would be used at the remote station to perform all the scans. Then, the camera 200 of the repair device 100 would be used to perform a single scan to locate the sector's position within the camera 200's field of view among all the scans performed at the remote station. In all these embodiments of the invention, it is understood that treatment would be performed on the sectors requiring treatment.Thus, if no damage requiring treatment is identified in a scan of a sector, the repair device 100 moves the sector in question to a scanned sector where one or more damages are identified for treatment.

[0062] The method further includes a step of positioning the camera 200 at a reference distance from the carcass of the identified tire (i.e., the damaged portion of the carcass). During this step, the camera 200 is centered with respect to the carcass of the identified tire (a right / left positioning). To do this, the robot first positions the camera 200 in front of the identified tire in a position that allows the camera to see all possible tire dimensions (including diameters and widths). A scan is then performed by the camera 200, and the scan is subsequently analyzed to verify that the camera 200 is correctly positioned.

[0063] With reference to [Fig. 3], the 3D points are expressed with respect to a coordinate system located at the center of the lens of the camera 200. If this coordinate system is represented in 3D by scanning a section of a tire (see "3D visualization" in [Fig. 3]), the Y-axis is directed towards the ground, and the Z-axis is directed towards the identified tire, and the X-axis is perpendicular to the Y and Z axes in order to form an orthonormal coordinate system. If we represent In this 2D coordinate system (see "2D visualization" in [Fig.3]), the origin of the coordinate system is at the center of the image: the X axis is oriented to the right, the Y axis is oriented downwards and the Z axis is oriented towards the tire.

[0064] With reference to [Fig. 4], the correct positioning of the camera 200 during this step is described. The zones 300 represent the points belonging to the carcass of the identified tire. First, a horizontal central zone of the scan is considered, which is used to verify that the distance between the identified tire and the camera 200 is correct. An average distance Dmoy of the points is calculated within this horizontal central zone, which is represented by the lines 300A. Then, the 3D points whose Z-distance belongs to an interval [Dmoy.margin, Dmoy+margin] are retained. These points represent the points belonging to the carcass of the identified tire (therefore, the relative distances vary according to the parameters of the identified tire).All these points can be represented as a binary image (identified as the "carcass mask" or "binary mask") where each retained point represents a point belonging to the carcass of the identified tire (these points are represented by the 300 zones in [Fig. 4]). Then, to obtain a mean surface distance Davg, the mean distance Davg is recalculated, considering only the points in the horizontal central zone that belong to the carcass of the identified tire. By comparing this distance with the reference distance of the camera 200 (because the camera 200 must always be positioned at the reference distance relative to the carcass of the identified tire), the frontal correction to be applied to the camera can be calculated.Next, the two vertical lines 300B are calculated, which delimit the beginning and end of the carcass mask and thus allow verification that the camera 200 is correctly centered with respect to the tread surface of the identified tire (see [Fig. 4]). The central vertical line located in the middle of the vertical lines 300B is calculated. If this central vertical line deviates too much from the center line of the image, then a lateral correction will be applied to the position of the camera 200 to recenter it with respect to the carcass of the identified tire.

[0065] Considering the repair device 100, its initial positioning (and, where applicable, the initial orientation of the processing device 102, and therefore the processing tool 106) is determined from data obtained through image acquisition of the automatic repair system and the physical environment in which the automatic repair system operates (for example, as shown in [Fig. 2]). The processor's analysis execution module can employ an adaptive automatic repositioning algorithm to find an ideal starting position for the repair device 100, thus enabling execution programmed instructions are stored in memory to perform image acquisition. The algorithm allows for continuous improvement across all image acquisitions, ensuring that the system (and particularly the repair device 100) improves based on the experience it acquires, especially regarding the selection of carcass areas to be treated by performing repair processes during tire retreading procedures.

[0066] The method further includes a damage detection step which is performed sector by sector for the identified tire. During this step, for each sector, the repair device 100 positions the 3D camera to scan the sector and acquire a color and three-dimensional (3D) image ("the color and 3D image") of the sector (it should be noted that sector-by-sector processing is involved during this step). At each point in the image, the color of the point is known, as well as its three-dimensional position relative to the camera 200.

[0067] Once the scan is complete, during the damage detection stage, a first neural network of the system (a "detection network") takes the color and 3D image as input to determine which points represent damage to be treated (as used here, the term "neural network" includes one or more neural networks). Before the first neural network can be used, a training phase is carried out to set its parameters. For this purpose, several input images are annotated (for example, manually annotated by experts in the field). For each input image, the annotation consists of indicating where the carcass areas containing damage to be treated are located (represented as an example in [Fig. 5] by the boxed regions 5A). This training phase is carried out once to set the parameters of the first neural network, after which the first neural network can be used on new images.

[0068] Once the first neural network outputs the carcass areas containing damage to be treated, the method of the invention further includes a step of classifying each damage into an associated classification. During this step, a second neural network of the automatic repair system (a "classification network") takes as input the image of the carcass area containing the damage to be treated (for example, the boxed regions 5A in [Fig. 5]), and it outputs a classification of each damage according to the treatment to be performed (as used here, the term "neural network" includes one or more neural networks). The second neural network operates regardless of the number of distinct classifications (represented by way of example in [Fig. 6] by classifications c1, c2, ...cN, or N, which varies according to the number of distinct classifications identified).The form of damage can be linked to its severity, with different damages varying according to distinct damage classes (which include, without limitation, . sculpture damage, tearing damage, cut damage and puncture damage).

[0069] By combining the results from the first neural network with those from the second neural network, in-depth knowledge is automatically obtained about the location and type of damage present on the tire casings. This knowledge is then very useful for guiding treatment choices for tires undergoing retreading.

[0070] The method of the invention further includes a step of selecting the most suitable repair tool for each type of damage in the images obtained. During this step, a third neural network of the system (a "tool selection network") takes as input the image of the carcass area containing the classified damage, and it outputs one or more repair tools identified to perform the corresponding repair (represented by way of example in [Fig. 7] by tools ol...oN or N varies depending on the number of distinct tools identified) (as used here, the term "neural network" includes one or more neural networks). Before the third neural network can be used, a training phase is carried out to set the parameters of this neural network. For this purpose, several input images have been annotated (for example, manually annotated by experts in the field).For each input image, the annotation consists of indicating the repair tool best suited to the damage present in the image (for example, repair tool 106 from [Fig. 2] represented as an example by a brush). This training phase is performed once to fix the parameters of the tool selection network, after which the third neural network can be used on new images. Thus, the third neural network operates regardless of the number of distinct tools intended for repairing the identified tire.

[0071] The method of the invention further comprises a step of carrying out a process for repairing detected and classified damage. The damage repair process includes a step of mounting the processing tool 106 chosen to repair the damage identified and classified during the preceding steps. During this step, one or more tools from the tool list generated by the third neural network are automatically installed on the robot (and particularly on the repair device 100). The various repair tools (which include, but are not limited to, cleaning, machining, and / or grinding tools) could be mounted on a tool holder (not shown). The robot automatically installs the chosen tool at the end of the extended arm 104 of the processing device 102.

[0072] The damage repair process further includes a step of defining the surface trajectory of the processing tool. The outputs of the first neural network (the detection network) and the third neural network (the selection network) (tool) provide a list of damages intended for treatment by the treatment tool 106 mounted on the robot. Referring to [Fig. 8], each damage in this list is characterized by a frame region 8A containing the damage to be treated. The 3D position of the frame region 8A relative to the base 110 of the repair device 100 is known.

[0073] Starting from this region 8A, the surface trajectory of the processing tool is defined during this step. This trajectory corresponds to the set of 3D points that the processing tool must follow to cover the entire region. To do this, a starting point and a direction of travel for the processing tool are defined first. As an example, in [Fig. 8], the starting point is assumed to be located at the bottom right of the frame region 8A, and the direction of travel is from bottom to top. It is understood that another starting point could be considered (i.e., bottom left, top right, and top left) and for another direction of travel (i.e., top to bottom, right to left, and left to right), depending on the intended orientation of the chosen processing tool. By defining the starting point and the direction of travel of the tool, we define the orientation of the tool along the entire trajectory.

[0074] Referring again to [Fig.8] and further to [Fig.9], the orientation of the processing tool represents the three (3) angles given to the processing tool, and these angles are given by the orientation of the tool frame whose: - The X axis is oriented along the direction vector starting from the starting point and directed in the chosen direction of travel (as represented in [Fig.8], from bottom to top); - The Z-axis is oriented along the normal to the starting point; and - The Y axis is automatically fixed according to the choices for the X and Z axes.

[0075] Once the orientation of the processing tool 106 is fixed, the surface path definition step includes a step for defining the set of 3D points that the processing tool must follow. During this step, the frame region 8A containing the damage is divided into strips 9A with a width equal to the width of the processing tool (this value is known). Referring to [Fig. 9], each strip 9A is defined by a starting point 9B and an ending point 9C. The width of the frame region 8A is not necessarily a multiple of the width of the chosen processing tool. The last strip of the frame region 8A may therefore extend beyond the region. To limit this extension, the geometric position of the strips 9A is offset so as to distribute this extension evenly on both sides of the frame region to be repaired. Once the cutting and offsetting has been done (see the [Fig.9]), it suffices to list all the starting and ending points of all the 9A bands to obtain the surface trajectory of the processing tool.

[0076] The damage repair process further includes a damage repair step during which the repair device 100 positions the selected processing tool 106 at the starting point of the frame region containing the damage. The repair device 100 then moves the processing tool along the inverse of the Z-normal over a distance determined by an offset (see [Fig. 10]). This offset is a fixed parameter that depends on the type of repair to be performed.

[0077] During this step, in addition to this offset, a safety system 400 of the automatic repair system of the invention is used to stop the repair device 100 from moving where the processing tool contacts a metal sheet (for example, during rubber removal from the carcass of the identified tire). Referring to [Fig. 11], an embodiment of the safety system 400 is shown with the processing device 100 during the repair of an identified tire P' in its processing area. In embodiments of the automatic repair system incorporating the safety system 400, the safety system comprises: - a first electrode 402 which is arranged to be placed opposite the carcass of the identified tire, at a distance from an electrically conductive insert 404 of the identified tire, so as to form with the electrically conductive insert a first dipole of which a first terminal is formed by the first electrode 402 and a second terminal is formed by the electrically conductive insert 404; - a second electrode 406 which is associated with the processing tool 106 (being, for example, a gum removal tool) such that, when the processing tool 106 comes into contact with the electrically conductive insert 404, an electrical connection is established between the second electrode 406 and the electrically conductive insert 404 forming the second terminal of the first dipole; and - a control unit 408 which is arranged to measure an impedance of a sensing circuit containing the first dipole and to detect a variation in impedance of the sensing circuit 410 caused by the electrical connection of the first dipole with the second electrode 406 caused by the contact of the processing tool 106 with the electrically conductive insert 404.

[0078] Such a security system is disclosed in the Applicant's application FR2213096.

[0079] Following the positioning of the processing tool 106 during the damage repair step, the tool is positioned at a starting point with an offset. The previously defined surface trajectory is then moved along the path of the repair device 100 until the point of The starting point of the surface trajectory coincides with the offset starting point. Thus, the offset starting point becomes the new starting point of the surface trajectory.

[0080] The repair device 100 then executes the repositioned surface path to repair the entire frame region 8A containing the damage to be treated (see [Fig. 10]). During a repair, the repair device 100 begins by bringing the processing tool 106 to the starting point of the surface path (see point 10A in [Fig. 10]). The repair device 100 then moves along the inverse of the normal (see arrow A in [Fig. 10]) over a distance fixed by an offset parameter or until it touches the first metal sheet encountered (the movement of the processing tool is represented by arrow B in [Fig. 10]). The initial surface trajectory is then moved according to the displacement made by the processing tool (see arrow B in [Fig. 10]) to superimpose the initial starting point (see point 10A in [Fig. 10]) on the starting point with offset (point 10B in [Fig. 10]).The processing tool 106 then follows the repositioned surface path to repair the frame region containing the damage (the repositioned frame region 8A is represented by the dotted region 8A').

[0081] The surface trajectory definition step and the damage repair step of the damage repair process are repeated for all damages to be repaired with the selected processing tool. For the next tool, the process restarts at the processing tool mounting step. The steps of the damage repair process are repeated for all processing tools exiting the tool selection network (the third neural network).

[0082] A person skilled in the art in this field will recognize that many image processing techniques can be used to select and determine the parameters of target tires. Several commercially available image processing systems can be used.

[0083] In a tire retreading facility, a vision system can be used to detect the presence of a tire in the field of vision of the camera 200, which triggers the camera to capture the image of the identified tire for processing.

[0084] In embodiments of the method of the invention, one or more steps of the method may further include a step of scanning the physical environment containing the repair device 100. In embodiments of the method, this step further includes a step of measuring the physical environment to achieve precise positioning of the processing tool 106. During this step, one or more sensors may be used to capture data corresponding to the processing tools and the tires in order to determine the shapes and / or positions of individual tires. This information is relevant for enabling accurate modeling of the invention's process in order to optimize the time of an associated retreading process.

[0085] A method of the invention can be performed by PLC control and may include pre-programmed management information. For example, a setting of the process can be associated with the parameters of the tire being retreaded, and / or the properties of the tires processed in a retreading plant incorporating the repair device 100 (e.g., aircraft tires, truck tires, etc.). The system of the invention (and / or a plant incorporating this system) can easily repeat one or more steps of the method of the invention in a predetermined order to ensure substantially complete repair of the carcass of the treated tire.

[0086] The automatic repair system of the invention (and / or a retreading plant incorporating this system) may include pre-programmed management information. For example, a process setting may be associated with the parameters of the typical physical environments in which the system operates. In embodiments of the invention, the automatic repair system (and / or a retreading plant incorporating this system) may receive voice commands or other audio data representing, for example, starting or stopping the repair and / or loading / unloading a drum dedicated to tire retreading. A request may be made that includes a request for the current status of an ongoing retreading process cycle. A generated response may be represented audibly, visually, tactilely (for example, using a haptic interface), and / or virtually and / or augmented.This response, along with the corresponding data, can be recorded in a neural network.

[0087] For all embodiments of the automatic repair system, a monitoring system could be implemented. At least part of the monitoring system can be provided in a portable device such as a mobile network device (e.g., a mobile phone, a laptop computer, one or more portable network-connected devices (including augmented reality and / or virtual reality devices), wearable network-connected clothing / jewelry, and / or any combination thereof). It is conceivable that detection and comparison steps could be performed iteratively.

[0088] In one embodiment, the method of the invention may include a step of training the automatic repair system to recognize representative values ​​of the tires to be retreaded (for example, values ​​of the internal diameter and external diameter) and to make a comparison with Targeted values ​​(for example, to fulfill a tire order incorporating retreaded tires). Each training step can include a classification generated by self-learning means. This classification can include, without limitation, the parameters of the selected tires, the durations of the process cycles, and the expected values ​​at the end of a process in progress (for example, the number of tires retreaded during a cycle to reach a desired level).

[0089] The wide variety of damage used during the training of the three neural networks makes the repair system of the invention robust against all existing damage. Thus, the three neural networks do not require any prior knowledge of all existing damage to operate.

[0090] All repair processes are independent of the number and type of damage present on the frame to be repaired. The repair system of the invention adapts in real time to the damage encountered. No predefined methods are used. Furthermore, the number of processing tools is unlimited, which makes the system scalable in the event of the addition or removal of certain types of repair.

[0091] The terms "at least one" and "one or more" are used interchangeably. The ranges presented as being "between a and b" encompass the values ​​"a" and "b".

[0092] Although particular embodiments of the disclosed apparatus have been illustrated and described, it will be understood that various changes, additions, and modifications can be made without departing from the spirit or scope of this disclosure. Therefore, no limitations should be imposed on the scope of the invention described except those set forth in the appended claims.

Claims

1. Demands An automated repair system enabling the repair of a tire casing identified during retreading; the automated repair system includes: - at least one repair device (100) comprising a robot having a processing device (102) supported by an elongated pivoting arm (104) extending from the elongated arm (104) to a free end (102a) which allows the installation of a processing tool (106) along a common longitudinal axis; - a detection system to collect information on the physical environment around the repair device (100); - a communication network that manages the data entering the automatic repair system from each repair device (100), the communication network including at least one communication server to execute programmed instructions stored in a memory of one or more processors of the automatic repair system to store an application for analyzing data representative of the carcasses of the imaged tires; - a first neural network configured to take as input color and three-dimensional (3D) images of the identified tire to determine which points represent carcass areas containing damage to be treated; - a second neural network configured to take as input images of carcass areas containing the damage to be treated and configured to output a classification of each type of damage to be treated; and - a third neural network configured to take as input images of carcass areas containing classified damage and configured to output at least one identified processing tool to perform a corresponding repair; so that the repair device (100) is configured on one or more parameters of the identified tire calculated by an image processing module incorporated in the processor's memory to automatically indicate the identified repair tool to repair the damage present in each image and to set the repair device (100) in motion to repair the tire identified in each carcass area containing damage to be treated.

2. The automatic repair system of claim 1, further comprising a safety system (400) comprising: - a first electrode (402) which is arranged to be positioned opposite the carcass of the identified tire, at a distance from an electrically conductive insert (404) of the identified tire, so as to form with the electrically conductive insert (404) a first dipole, such that a first terminal is formed by the first electrode (402) and a second terminal is formed by the electrically conductive insert (404); - a second electrode (406) which is associated with the processing tool (106) such that, when the processing tool (106) comes into contact with the electrically conductive insert (404), an electrical connection is established between the second electrode (406) and the electrically conductive insert (404) forming the second terminal of the first dipole;and - a control unit (408) which is arranged to measure an impedance of a detection circuit (410) containing the first dipole and to detect a variation in impedance of the detection circuit (410) caused by the electrical connection of the first dipole with the second electrode (406) caused by the contact of the processing tool (106) with the electrically conductive insert (404); so that the repair device (100) stops its movement where it contacts a metallic layer of the tire identified during a retreading process.;

3. The automatic repair system of claim 1 or claim 2, wherein the detection system of the repair device (100) comprises one or more sensors configured to perform two-dimensional (2D) and / or three-dimensional (3D) image detection.

4. The automatic repair system of claim 3, wherein the detection system of the repair device (100) comprises at least one RGB-D type camera (200) fixed to at least one of the extended arm (104) and the processing device (102) of the repair device (100).

5. The automatic repair system of any one of claims 1 to 4, wherein the input data to the automatic repair system includes general information concerning the identified tire.

6. The automatic repair system of any one of claims 1 to 4, wherein the input data to the automatic repair system includes one or more CAD files of the identified tire to match the location of a carcass area of ​​the identified tire from the CAD file.

7. The automatic repair system of any one of claims 1 to 6, wherein the detection data obtained from the detection system includes data representative of one or more continuous movements of the repair device (100) before it stops to take one or more images of the tires identified during retreading.

8. The automatic repair system of any one of claims 1 to 7, wherein the processing tool (106) comprises at least among brushes, knives, rasps, carders, milling cutters and grinding wheels for repairing tires during the retreading process.

9. The automatic repair system of any one of claims 1 to 8, wherein the communication network is configured to execute programmed instructions stored in memory to implement a method for repairing the carcass of the identified tire, the repair method comprising the following steps: - a calibration step of the repair device (100); - a positioning step of the identified tire in a processing space where the repair device (100) processes it; - a positioning step of at least one camera (200) of the detection system at a reference distance from a carcass of the identified tire; a damage detection step comprising a learning phase in which images obtained are annotated to indicate where carcass areas containing damage to be treated are located; - a step of classifying each damage to be treated into a classification associated with a corresponding treatment; - a step of choosing at least one treatment tool (106) suitable for treating each damage in the images obtained; and - a step of carrying out a repair process for the damage detected and classified in the images obtained.

10. The automatic repair system of claim 9, wherein the damage repair process of the repair method implemented by the automatic repair system comprises the following steps: - a step of mounting the processing tool (106) to be used to repair the identified and classified damage; - a step of defining a surface trajectory of the processing tool (106) based on the identified and classified damage; and - a step of repairing the damage to be treated.

11. A retreading installation comprising the automatic repair system of any one of claims 1 to 10.

Images