EP-4742151-A1 - METHOD FOR DETECTING FAILURE OF RUBBER STRIP FOR VEHICLE TIRE

EP4742151A1EP 4742151 A1EP4742151 A1EP 4742151A1EP-4742151-A1

Abstract

Method for detecting defects in a rubber strip for the manufacture of a tire, implemented by a computer and comprising the following steps: - a preservation (110) of at least one data element that is representative of a surface quality of at least one sub-area of the strip, - a detection (120) of at least one fault on the sub-area of the strip by analyzing the data element, and for each detected error, - determining (130) a position of the defect on the strip, - a determination (140) of a degree of criticality of the defect at least depending on the position of the defect on the strip.

Inventors

SLIWA, Fabien
KESSLER, Mélodie

Assignees

Continental Reifen Deutschland GmbH

Dates

Publication Date: 20260513
Application Date: 20251006

Claims (15)

Method for detecting defects in a rubber strip for the manufacture of a tire, implemented by a computer and comprising the following steps: - a preservation (110) of at least one data element that is representative of a surface quality of at least one sub-area of the strip, - a detection (120) of at least one fault on the sub-area of the strip by analyzing the data element, and for each detected error, - determining (130) a position of the defect on the strip, - a determination (140) of a degree of criticality of the defect at least depending on the position of the defect on the strip.
Method according to claim 1, wherein the data element that is representative of a surface quality of a sub-area of the strip is an image of the surface of the strip or a three-dimensional profile of the surface of the strip.
The method of claim 1 or 2, wherein the method is repeated over a series of data representative of the surface quality of a continuous stripe, such that the data cover the entire stripe.
The method according to claim 3, further comprising providing each occurrence of a defect on the strip with a timestamp, a statistical analysis of the occurrence of defects on the strip, and wherein the detection (120) of a defect and the determination (140) of its criticality degree are further carried out on the basis of the statistical analysis.
Method according to any one of claims 1 to 4, wherein the detection (120) is performed by a machine learning algorithm trained to detect the defects of a rubber strip using a database of images with and without defects, each labelled with or without defects, which at least partially includes images of rubber strips with defects.
Method according to one of claims 1 to 4, wherein the detection (120) and determination (140) of the criticality degree are performed by a machine learning algorithm trained to detect the critical defects of a rubber strip using a database of images with and without critical defects, each labelled with a criticality degree.
Method according to any one of claims 1 to 4, wherein the detection (120) and determination (140) of the criticality degree are performed by an anomaly detection machine learning algorithm trained to detect the critical defects of a rubber strip using a database of images with non-critical defects and without defects.
Method according to one of the preceding claims, wherein determining (130) the position of the defect comprises determining (140) at least one of a lateral and longitudinal position of the defect on the strip.
Method according to one of the preceding claims, comprising determining (130) a longitudinal position of the defect, and, if the criticality level determined for this defect exceeds a predetermined threshold, additional steps of storing the longitudinal position of the defect and separating a section of the strip having the defect from the rest of the rubber strip based on the longitudinal position.
Method according to one of the preceding claims, wherein the criticality level of a defect is determined at least on the basis of its lateral position on the strip and its shape.
A method according to any of the preceding claims, wherein the detection (120) comprises classifying different types of defects on the rubber strip, such as clumps, torn edges or cracks, wherein the The machine learning algorithm for classification is trained to classify the different types of defects based on a database of images of rubber strips with different defect types and markings according to the defect type.
Computer program that includes instructions for implementing the method according to any one of claims 1 to 9 when this program is executed by a processor.
A computer-readable non-volatile storage medium on which a program for implementing the method according to one of claims 1 to 9 is stored when this program is executed by a processor.
Computing unit, which includes the following: - an input interface for receiving at least one image of a rubber strip, - a memory containing at least the instructions of a computer program according to claim 10, - a processor with access to memory for reading the instructions and executing the method according to any one of claims 1 to 9, - an output interface to provide a criticality level of a defect present in the image.
An extruder suitable for continuously extruding a rubber strip, for example on a conveyor, and comprising an image acquisition device and/or a 3D profilometer suitable for capturing at least one data element representative of a surface quality of at least one sub-area of the extruded rubber strip, and connected to a computing unit according to the preceding claim.

Description

Technical field The present disclosure relates to the field of tires for vehicles such as automobiles or trucks, or any other wheeled vehicles. In particular, it relates to the field of rubber treads for such tires. Most especially, it relates to the field of quality control of such rubber treads. State of the art The extrusion of a rubber strip for a tire is a crucial process in tire manufacturing. It begins with mixing raw rubber with various additives such as vulcanizing agents, reinforcing agents, and fillers. This mixture is then heated and pressed in an extrusion machine. In this machine, the mixture is forced through a nozzle, which is a strip-shaped opening corresponding to the desired cross-section of the rubber strip. Under high pressure, the material is forced through this opening, giving it its final shape and precise dimensions. During this process, temperature and pressure are precisely controlled to guarantee the quality and properties of the extruded material. After extrusion, the rubber strip is cooled and can undergo further manufacturing processes, such as lamination and calendering, to achieve the exact specifications required for use in a tire. However, defects can be detected on the rubber strip at the exit of the extrusion machine. In particular, "knots" or "lumps" may be present. Streaks may be present. These are accumulations of material of various shapes and sizes that prevent the rubber strip from being perfectly uniform. Once the tire is formed with a section of the rubber strip, a lump present on this section can unfortunately lead to rapid tire damage: a lump can be the cause of premature tearing of the strip and the tire itself. Such a defect can endanger the safety of a vehicle equipped with such a tire and its occupants. Because clumps vary widely in shape and size, only some of them, depending on their shape, size, and location on the strip's surface, can cause a tear. Therefore, characterizing a clump, and detecting a dangerous one, is very difficult and time-consuming, even more so before the tire is formed from the strip. Paradoxically, certain large clumps can be harmless, while smaller clumps can be dangerous. Furthermore, an extruder can include a series of dies through which the material is forced, each die having a specific shape to produce a final product with precise geometry. This allows the dies to be changed according to production needs, enabling versatility in manufacturing different products from the same base material. However, the final product can also be composed of multiple base materials: instead of a single material passing through all the dies, each material is fed to its own dedicated die. Each die is then designed to shape a specific material according to the requirements of the final product. This process enables the production of composite or multilayer products, where different parts of the final product are made of different materials. This can offer advantages such as specific properties for each component or a combination of properties from different materials in a single product. However, this variety of materials used in the production of the final product also introduces a variety of... the errors and their consequences, as well as the complexity of identifying the errors that actually pose a risk. Brief description The present disclosure improves the situation. A method for detecting defects in a rubber strip used in tire manufacturing is proposed, which is implemented by a computer and comprises the following steps: obtaining at least one data element that is representative of a surface condition of at least one sub-area of the strip, detecting at least one fault on the sub-area of the strip by analyzing the data element, and for each detected error, determining the position of the defect on the strip, Determining the criticality level of the error, at least depending on the position of the error on the strip. Advantageously, the data element that is representative of a surface texture of a sub-area of the strip can be an image of the surface of the strip or a three-dimensional profile of the surface of the strip. In one embodiment of the invention, the method can be repeated over a series of data that are representative of the surface properties of a continuous strip, so that the data cover the entire strip. Advantageously, the method can further include timestamping each occurrence of a defect on the tape, a statistical analysis of the occurrence of defects on the tape, and the detection of a Furthermore, the detection of errors and the determination of their criticality level can be carried out using statistical analysis. The detection can be performed by a machine learning algorithm trained to detect the defects of a rubber strip using a database of images with and without defects, each labeled with or without defects, which at least partially includes images of rubber strips with defects. The detection of the defect and the deter