Patent ID: 12187002

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

FIG.1shows one exemplary embodiment of a tire heating press100in accordance with the present disclosure. Here, only some of the essential parts of such a tire heating press100are shown inFIG.1for purposes of simplicity and clarity. Reference may be made to the associated specialist literature for further details on such tire heating presses100.

The tire heating press100comprises two identical vulcanization stations for parallel vulcanization of a first green tire116and a second green tire126. To vulcanize the green tires116,126, the tire heating press100comprises two lower tire molds112,122and two upper tire molds110,120, which surround the respective green tire116,126during the vulcanization process.FIG.1illustrates the upper tire molds110,120and the lower tire molds112,122in an open state that is used to load the green tires116,126. During the vulcanization process of the green tires116,126, the upper tire molds110,120each lie on the lower tire molds112,122and thus form a closed state.

To assist the vulcanization of the green tires116,126, the tire heating press100comprises two bladders114,124, which are illustrated in a de-aerated or deflated state inFIG.1. In the course of the vulcanization process of the green tires116,126, the bladders114,124are filled with hot vapor under a high pressure, expand within the respective green tires116,126and thereby press them against the lower tire molds112,122and the upper tire molds110,120. In addition to the actual vulcanization of the tire material, for example, a tire profile and also other relief-type structures of the vehicle tire116,126are thereby also impressed into the green tire116,126.

The hot vapor mentioned here for filling the bladder114,124is just one example of the wide variety of gases or liquids able to be used in such tire heating presses100. During the vulcanization process, these may, for example, have temperatures of up to 100 degrees Celsius, advantageously of up to 200 degrees Celsius and more advantageously up to 250 or 300 degrees Celsius.

The bladder and the corresponding media system may furthermore be configured such that air, steam, hydrogen and/or other gases, inter alia, including having the abovementioned temperatures, can be used as gases. If, for example, a liquid is to be used instead, then water is often used.

The bladder114,124may in this case be configured such that it can, at least inside a green tire, withstand a pressure of up to 30 bar or more, or a negative pressure of down to −1 bar. The corresponding media system may then also be established for these pressure ranges.

FIG.2shows a steam media system102with the bladder114from the right-hand part of the tire heating press100illustrated inFIG.1. Here, a temperature sensor450is installed in the region of the bladder114and may be used to ascertain the temperature of the bladder114and/or inside the bladder114. A first pressure sensor410, which is established to measure a pressure of between 0 and 4 bar, is located at the output region of the bladder, on the right-hand side of the media system102. Also located there is a second pressure sensor420, which is set to measure a pressure of between −1 and 32 bar. Using the two pressure sensors410,420and the temperature sensor450, it is possible to determine the state of the steam in the bladder114with good level of accuracy in the various pressure and temperature ranges.

The right-hand region of the steam media system102is configured for the inflow of steam. On the far left there is located an inflow channel210for the inflow of steam at a relatively low pressure of between 2 and 8 bar and temperatures of 110 to 180° C. In order to regulate the inflow in this first inflow line210, an on/off valve310in this inflow line210is provided.

Next to this first inflow line210on the right inFIG.2, provision is made for a second inflow line220for the inflow of steam at a high pressure of between 17 and 30 bar and with a temperature of between 190 and 230° C. This second inflow line220again contains an on/off valve320for switching this flow of steam on and off. After these two inflow lines210,220have combined, provision is made, in the combined inflow line, for a control valve322use of which permit the respective flow of steam to be controlled more accurately. A safety valve321is arranged downstream in the inflow line.

Next to the high-pressure feed line220on the right inFIG.2there is arranged a further feed line230that is provided for the inflow of further process gases or liquids. This third inflow line230also contains an on/off valve335for activating and deactivating a corresponding flow of gas or liquid through this third feed line230.

All three feed lines210,220,230then run together in a single bladder feed270, in which a main inlet valve370is provided.

Provision is made, on the outlet side of the bladder, for a main outlet line260for discharging liquids or gases contained in the bladder114. This outlet line260also contains the abovementioned pressure sensors410,420. A main outlet valve360for controlling the media outlets from the bladder114is arranged downstream of these pressure sensors in the outlet direction. Arranged downstream of the outlet valve360in the outlet line260, in turn, is a third pressure sensor430for detecting media in a pressure range between zero and four bar and a fourth pressure sensor440for measuring the pressure of media in a pressure range between minus one and 32 bar. Arranged downstream of the main outlet line260is a further outlet line240having an on/off valve340.

Provision is made, downstream of the main outlet line260and branching off from the outlet line240, for a negative pressure line250having an on/off valve350, via which negative pressure line, for example gases or liquids contained in the bladder114, can be actively aspirated. The aspiration line450or a pump installed downstream thereof (not illustrated inFIG.2) may in this case be provided, for example, in order to establish a vacuum in the region of −0.5 to −0.1 bar and a maximum media temperature of 60° C.

The valves310,320,322,324,330,335,370,340,350,360and sensors410,420,430,440,450illustrated inFIG.2and/or explained above are examples of components of the tire heating press100that are required or are used for an intended vulcanization process of a vehicle tire116,126in the tire heating press100. The sensors410,420,430,440,450illustrated inFIG.2and valves310,320,322,324,330,335,370,340,350,360illustrated inFIG.2, and any sensors that are present (for example, for acquiring a position or a state of the respective valve310,320,322,324,330,335,370,340,350,360) are examples of sensors410,420,430,440,450of the tire heating press100that are required or are used for an intended vulcanization process of a vehicle tire116,126in the tire heating press100.

FIG.2furthermore illustrates a control device130that is configured as a modular control device130having a central module132and a first input/output module134and a second input/output module136. Here, corresponding signal output lines extend from the input/output modules134,136to the various valves310,320,322,321,330,335,370,360,340,350of the media system102. Control signals can be transmitted via these signal output lines from the central module132of the control device130to the abovementioned valves310,320,322,321,330,335,370,360,340,350, in order thereby to adjust corresponding valve positions.

Corresponding signal input lines also extend from the temperature sensor450and the pressure sensors410,420,430,440to the input/output modules134,136in order to transmit the corresponding sensor values to the central module132of the control device130.

This is symbolized inFIG.2by citing the reference signs of the corresponding valves and sensors underneath the input and output lines, which are illustrated symbolically underneath the controller130.

An execution environment for a corresponding control program for controlling the tire heating press100is provided in the central module132. To vulcanize a green tire116introduced in the right-hand part of the tire heating press100, a gas inflow and gas outflow for the correct supporting of the vulcanization process may then, for example, be controlled, in the course of the running of this control program, via the incoming sensor signals and the outgoing actuation signals for corresponding valves310,320,322,324,330,335,370,360,340,350. The vulcanization process of the green tire116is thereby correspondingly supported by a corresponding inflow and outflow of steam into and out of the bladder114.

Also illustrated inFIG.2is an edge device500, which represents one example of an evaluation device in accordance with the present invention. The edge device500in this case contains a neural network502. The neural network502is in this case one exemplary embodiment of an ML model in accordance with the present invention.

This edge device500is connected to the control device130via a field bus line139. Control commands that are used to control the tire heating press100may, for example, be transmitted to the evaluation device via this field bus line. Position information for the valves310,320,322,324,330,335,370,360,340,350of the media system102may furthermore be transmitted to the edge device500via the field bus line139. Measured values from the temperature sensor450and from the pressure sensors410,420,430,440may additionally also be transmitted from the control device to the edge device via this field bus line.

The neural network502has been trained with a multiplicity of valve position values and temperature and pressure sensor values such that the respective position and sensor values have each been respectively assigned the fact whether the bladder functioned correctly at these sensor values, or respectively acquired sensor value combinations, whether the bladder was defective at the corresponding sensor values or sensor value combinations (and, for example, had a leak) or whether a defect with the bladder114,124occurred in the foreseeable future at these sensor values or sensor value combinations. Such a time period may, for example, be that 10 vehicle tires, 50 vehicle tires or else 100 vehicle tires were produced in this time period, for example.

In the course of controlling the media system102with the control device130, position values of the valves310,320,322,324,330,335,370,360,340,350of the media system102and of the sensors410,420,430,440,450of the media system102are then transmitted regularly to the edge device500during the production of vehicle tires and entered there as input variables into the trained neural network502. If, in the case of corresponding input data, the neural network outputs the information that the bladder114is fine, then the production continues without any further notification.

If the neural network502, following the input of corresponding data, such as that explained above, outputs the information that a defect with the bladder114could occur in the foreseeable future, then a corresponding warning notification is output to a user. This warning notification may, for example, be transmitted to a PC600via a data line602and output to the user via this PC600. This warning notification may, for example, comprise the information that a defect with the bladder114,124could be expected in the foreseeable future, where the foreseeable future may be specified in even more detail in the message.

If the neural network502, following the input of corresponding data, such as that explained above, outputs information that there is already a defect with the bladder114, then a corresponding warning notification is, for example, output to a user via the PC600. Provision may furthermore be made in this case for a corresponding message to also be transmitted to the control device130from the edge device500via the field bus139and for a warning signal likewise to be output to the tire heating press100thereby. This may be configured, for example, as a red warning light and/or a corresponding acoustic signal. Provision may furthermore be made in this case for corresponding parameters for controlling the media feed and discharge to be changed such that high-quality or at least tolerable vehicle tires are still produced or can be produced at least for a particular time period using a defective bladder114,124.

FIG.3illustrates a further optional embodiment for the system illustrated inFIG.2.FIG.3in this case illustrates the media system102already illustrated inFIG.2with the bladder114. Also illustrated is the control device130with the central module132and the input and output modules134,136, which are configured, in the manner already explained in connection withFIG.2, with the valves310,320,322,324,330,335,370,360,340,350and sensors410,420,430,440,450of the media system102to control the feed and discharge of hot steam to and from the bladder114. The control device130illustrated inFIG.3furthermore comprises an ML module138, which comprises the neural network502that was provided in the edge device500in the exemplary embodiment illustrated inFIG.2.

In the system illustrated inFIG.3, the neural network502is incorporated directly via a backplane bus of the control device130(not illustrated inFIG.3), via which corresponding valve position values and sensor values are transmitted from the central module132of the control device130to the neural network502in the ML module138. The corresponding information output by the neural network502is then in turn transmitted, via the backplane bus, to the central module132of the control device130, and potentially from the, for example, when a warning notification has been generated, to the PC600via the data line602. A corresponding user may then, as already explained in connection withFIG.2, receive corresponding warnings about the state of the bladder114,124via the PC600. The training of the neural network502and the input and output signals of the control device300in the course of the operation of the tire heating press100correspond to those explained in connection withFIG.2.

FIG.4shows an exemplary embodiment of the neural network502illustrated inFIGS.2and3. The neural network502illustrated inFIG.4in this case has what is known as an autoencoder structure and is hereinafter also called autoencoder502. To simplify the illustration of the structure of the autoencoder502, comparatively few nodes are depicted, and a two-dimensional neural network502has been chosen as an example for illustrative purposes.

Such a neural network502with an autoencoder structure represents one example of an ML model in accordance with the disclosed embodiments of the invention and may, for example, be trained or have been trained using training data in accordance with the disclosed embodiments of the invention using unsupervised learning methods, for example, known to a person skilled in the art and accordance with the disclosed embodiments.

The autoencoder502has what are known as nodes510, which are structured in five node layers521,522,523,524,525in the illustrated example. These node layers521to525are illustrated as superimposed nodes510inFIG.4. Only some of the nodes510are identified using a reference sign inFIG.4, so as to simplify and clarify the illustration. On the left-hand side inFIG.4is illustrated an input data vector560(a vector is a one-dimensional matrix) having four data fields561,562,563,564, where in each case one of the input fields561,562,563,564is connected to in each case one of the nodes510of the first node layer521of the autoencoder500. Data are thereby input into the autoencoder510. The autoencoder502comprises an encoding region530that comprises the first two node layers521,522of the autoencoder500. Here, each of the nodes510of the first node layer521is connected to each of the nodes510of the second node layer522.

A code region540, which consists of a node layer523, adjoins the encoding region530. Here, each node510of the second layer522of the encoding region530is in turn connected to each node of the code layer523of the code region540.

Adjoining the code region540, the autoencoder structure102has a decoding region550, which in turn consists of two node layers524,525. The last of the node layers525is in turn connected to data fields571,572,573,574of an output data vector570.

The autoencoder502may then, for example, be trained such that an input dataset560, for example, comprising position values of valves310,320,322,324,330,335,370,360,340,350and sensors410,420,430,440,450of the media system102, is input into the first node layer521of the encoding region530and the parameters of the nodes510and node connections of the autoencoder500are then adjusted, using one of the learning methods applicable to or typical for autoencoders, such that the output data vector570that is output by the last node layer525of the decoding region550corresponds to the input data vector560, or at least approximately corresponds to the input data vector560. Such typical learning methods are, for example, what is known as the backwards propagation of errors (backpropagation) method, conjugated gradient methods, what is known as a restricted Boltzmann machine mechanism, or comparable mechanisms or combinations thereof. Parameters of a neural network that are determined during training may be, for example, a weighting of a node connection or of an input value for a node (weight), a bias value for a node (bias), an activation function for a network node or parameters of such an activation function (for example, sigmoid function, logistic function, and/or activation function) and/or an activation threshold for a network node or comparable parameters.

The above-described learning method for the autoencoder500illustrated inFIG.4is one example of what is known as unsupervised learning.

A neural network502in accordance with the disclosed embodiments may, for example, alternatively also comprise a network structure for supervised learning. By way of example, network structures for supervised learning and unsupervised learning may also be combined. By way of example, the ML model502illustrated in these figures may comprise a neural network with an autoencoder structure, as is illustrated for example inFIG.4, and/or a plurality of further network structures. Here, the autoencoder structure may deviate from the example of an autoencoder structure502illustrated inFIG.4, both in terms of the number of nodes respectively involved and the dimensionality of the node layers, and in terms of the number of node layers.

The autoencoder structure illustrated inFIG.4is one example of what is known as a deep autoencoder502, because not all of the nodes510of the autoencoder502are connected to an input or output of the autoencoder structure502, and there are thus what are known as “hidden layers”.

Very generally speaking, autoencoder structures502may have, for example, a structure symmetrical with respect to the code region. Here, for example, the number of nodes510per node layer521,522,523,524,525may furthermore decrease from the input side toward the code region, in each case layer by layer, and then increase toward the output side, again layer by layer. The layer523or layers in the code region thereby then has a minimum number of nodes510in the context of the autoencoder structure502. The autoencoder502illustrated inFIG.4is one example of such a symmetrical autoencoder502as described above.

FIG.5is a flowchart of the method for monitoring a vulcanization process of a vehicle tire116,126in a tire heating press100, where at least one of at least one sensor value and at least one control variable of the tire heating press100are acquired.

The method comprises supplying at least one of the at least one sensor value and the at least one control variable to an evaluation device138,500comprising an ML model502which is configured via a machine learning method, as indicated in step510.

Next, the evaluation device138,500and/or the ML model502output a warning notification when an evaluation of at least one of the at least one sensor value and the at least one control variable by the evaluation device138,500reveals either (i) the existence of a defect or an anomaly with either the tire heating press100or at least part of the tire heating press100, or (ii) a defect or an anomaly with either the tire heating press100or at least part of the tire heating press100will occur in the foreseeable future, as indicated in step520.

Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.