A method and a system for predicting crack in a glazing

Abstract

Disclosed herein is a method and system for predicting a crack in a glazing while mounting the glazing on a vehicle is disclosed The method comprises receiving a vehicle body image and a glazing image from a portable device. Further, the method comprises obtaining vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively. Furthermore, the method comprises comparing the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold, respectively. Further, the method comprises determining stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and predefined glazing threshold, respectively. Therefore, the method predicting the crack in the glazing, based on the determined stress in the glazing.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application is a patent of addition claiming priority from the main Indian Patent Application No. 202341065455 filed on September 29, 2023, the entirety of which is incorporate herein by a reference.TECHNICAL FIELD

[0001] The present disclosure relates to the field of glazing, and more particularly relates to a method and a system for predicting crack in a glazing.BACKGROUND OF THE DISCLOSURE

[0002] The following description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0003] Transparent and translucent glazing materials, such as glass, play a vital role in modern architecture, automotive design, and industrial applications. These glazing materials provide aesthetic appeal, natural light transmission, and insulation properties, making them an essential component of vehicles. However, the glazing materials are susceptible to damage, including the development of cracks, which can compromise structural integrity and safety of the vehicles.

[0004] Typically, cracks that are formed on the glazing are either internal crack or surface crack. An internal crack, also known as a subsurface crack or hidden crack, occurs within the bulk or body of the glazing material. Internal cracks can result from various factors, including thermal stress, manufacturing defects or the like.

[0005] Usually, some amount of pre-stress will be present between the vehicle body and the glazing material of the vehicle. The pre-stress will have a negative impact while mounting the glazing material on the vehicle, leading to an internal crack in the glazing material. Thepre-stress is due to the way the glazing material is manufactured and mounted in on the vehicle. The amount of the pre-stress (residual stress) around an outer 50 mm perimeter of the glazing material is significantly greater than inside area of the glazing material, due to differential rate of cooling and will fracture 2.5 times easier than the rest of the glazing material. Further, some amount of additional pre-stress is applied while mounting the glazing material on the vehicle. Thus, both the pre-stress created during the manufacturing of the glazing material and mounting the glazing material will lead to crack of the glazing material.

[0006] Conventionally, predictive models may provide a corrective action for determining the crack. However, the predictive model does not have a capability to predict the occurrence of the internal crack in the glazing material. Thus, there is a requirement of accurately predicting the crack in the glazing material.SUMMARY OF THE DISCLOSURE

[0007] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

[0008] In one non-limiting embodiment of the present disclosure, a method for predicting a crack in a glazing while mounting the glazing on a vehicle is disclosed. The method comprises receiving a vehicle body image and a glazing image from a portable device. Further, the method comprises obtaining vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively. Furthermore, the method comprises comparing the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold, respectively. Further, the method comprises determining a stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and predefined glazing thresholdrespectively. Therefore, the method predicting the crack in the glazing based on the determined stress in the glazing.

[0009] In another non-limiting embodiment of the present disclosure, the vehicle measurement data comprises at least one of: a pre-stress value of a vehicle body, vehicle frame measurement, number of spacers, position of spacers, dimensions of spacer, interspacer distance. The glazing measurement data comprises at least one of a glazing pre-stress value, mounting characteristics, glazing geometry measurement, and adhesive strain value.

[0010] In another non-limiting embodiment of the present disclosure, the method comprises obtaining an assembly stress experienced by the glazing. Further, the method comprises comparing the assembly stress with a pre-defined assembly stress threshold. Thereby the method comprises determining the stress when the assembly stress exceeds a pre-defined stress threshold.

[0011] In another non-limiting embodiment of the present disclosure, the stress in the glazing is determined by one of one or more sensors and a non-contact stress measurement equipment.

[0012] In another non-limiting embodiment of the present disclosure, the method further comprises optimizing the number of spacers, dimensions of spacers and the position of spacers on the vehicle body if the crack is predicted. The number of spacers dimensions of spacer and the position of spacers are optimized based on the determined stress in the glazing.

[0013] In another non-limiting embodiment of the present disclosure, the method further comprises determining a subsequent stress after optimizing the number of spacers and the position of spacers. Further, the method comprises comparing the subsequent stress with a pre-defined stress threshold. Therefore, the method comprises mounting the glazing on the vehicle body based on the comparison when the subsequent stress is within the pre-defined stress threshold.

[0014] In another non-limiting embodiment of the present disclosure, a system for predicting crack in a glazing while mounting the glazing on a vehicle is disclosed. The system comprises a memory and at least one processor coupled with the memory. The at least one processor is configured to receive a vehicle body image and a glazing image from a portable device. Further, the at least one processor is configured to obtain vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively. Further, the at least one processor is configured to compare the vehicle measurement data and the glazing measurement data with corresponding predefined vehicle threshold and predefined glazing threshold, respectively. Furthermore, the at least one processor is configured to determine a stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and predefined glazing threshold, respectively. Therefore, the at least one processor is configured to predict the crack in the glazing based on the determined stress in the glazing.

[0015] In another non-limiting embodiment of the present disclosure, the vehicle measurement data comprises at least one of: a pre-stress value of a vehicle body, vehicle frame measurement, number of spacers, dimensions of spacers position of spacers, interspacer distance. The glazing measurement data comprises at least one of glazing pre-stress value, mounting characteristics, glazing geometry measurement, and adhesive strain value.

[0016] In another non-limiting embodiment of the present disclosure, the at least one processor is configured to obtain an assembly stress experienced by the glazing. Further, the at least one processor is configured to compare the assembly stress with a pre-defined assembly stress threshold. Furthermore, the at least one processor is configured to determine the stress when the assembly stress exceeds a pre-defined stress threshold.

[0017] In another non-limiting embodiment of the present disclosure, the stress in the glazing is determined by one of one or more sensors and a non-contact stress measurement equipment.

[0018] In another non-limiting embodiment of the present disclosure, the at least one processor is configured to optimize the number of spacers and the position of spacers on the vehicle body if the crack is predicted. The number of spacers and the position of spacers are optimized based on the determined stress in the glazing.

[0019] In another non-limiting embodiment of the present disclosure, the at least one processor is configured to determine a subsequent stress after optimizing the number of spacers, dimensions of spacers and the position of spacers. Further, the at least one processor is configured to compare the subsequent stress with a pre-defined stress threshold. Therefore, the at least one processor is configured to mount the glazing on the vehicle body based on the comparison when the subsequent stress is within the pre-defined stress threshold.

[0020] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF DRAWINGS

[0021] The embodiments of the disclosure itself, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings in which:

[0022] Fig. 1 illustrates an exemplary environment of a system for predicting crack in a glazing, in accordance with an embodiment of the present disclosure;

[0023] Fig. 2 shows a detailed block diagram of the system for predicting crack in a glazing, in accordance with an embodiment of the present disclosure;

[0024] Fig. 3 illustrates a flow chart for predicting crack in a glazing, in accordance with an embodiment of the present disclosure;

[0025] Fig. 4a shows an exemplary glazing placed with spacers, in accordance with an embodiment of the present disclosure;

[0026] Fig. 4b shows an exemplary glazing placed with sensor, in accordance with an embodiment of the present disclosure;

[0027] Fig. 5a-5e shows a different configuration for placing the sensor for determining the stress, in accordance with an embodiment of the present disclosure; and

[0028] Fig. 6 represents the flowchart of an exemplary method for predicting crack in a glazing, in accordance with embodiments of the present disclosure;

[0029] The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.DETAILED DESCRIPTION

[0030] The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.

[0031] The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying Figures. It is to be expressly understood, however, that each of the Figures isprovided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

[0032] As described earlier, due to the pre-stress between the vehicle body and the glazing material of the vehicle, the crack may occur in the glazing material. Further, conventionally known predictive model may provide only the corrective action for determining the crack. However, the predictive model does not have the capability to predict the occurrence of the crack in the glazing material. Thus, there is a requirement of accurately predicting the crack in the glazing material.

[0033] The present disclosure provides a method for predicting a crack in a glazing while mounting the glazing on a vehicle. In the present invention the crack may be predicted from a process of manufacturing the glazing till mounting the glazing on the vehicle. In the present invention, a portable device may capture a vehicle body image and a glazing image during any stage between the manufacturing the glazing till mounting the glazing. Then, by processing the vehicle body image, data related to vehicle measurement may be obtained. Then, by processing the glazing image, data related to glazing measurement may be obtained. Further, the vehicle measurement data may be compared with a predefined vehicle threshold and the glazing measurement may be compared with a predefined glazing threshold. Further, a stress in the glazing may be determined by comparing an assembly stress with an assembly stress threshold when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and predefined glazing threshold, respectively. Then, the crack in the glazing may be predicted based on the determined stress. Thus, the present invention predicts the internal crack in the glazing more accurately by considering the glazing and vehicle measurement data. Further, the present invention optimizes the number of spacers and spacers position based on the determined stress in the glazing to avoid cracking of the glazing. A detailed explanation of the proposed technique(s) is disclosed in the forthcoming paragraphs.

[0034] Fig. 1 illustrates an exemplary environment 100 for predicting crack in a glazing, in accordance with an embodiment of the present disclosure. The environment 100 is exemplified in a scenario when the glazing is manufactured and ready to be mounted on a vehicle. However, the same method disclosed in the present disclosure may be used in a scenario when the glazing is mounted on the vehicle. The present invention may predict the crack in the glazing using the vehicle measurement data and glazing measurement data and by predicting the stress in the glazing. In the present disclosure predicts internal crack based on the stress determined in the glazing. The exemplary architecture depicts the environment 100 that may incorporate a glazing 101, a vehicle body 102, a portable device 103, and a server 104. In an exemplary embodiment, the portable device 103 may include, but not limited to mobile, laptop, tab. In an exemplary embodiment, the vehicle measurement data and glazing measurement data may be stored in the server 104. The server 104 may include but not limited to a cloud and a database.

[0035] In some implementations, the portable device 103 may comprise a system 105 for predicting the crack in the glazing. The system 105 may be a computing system that may comprise a processor 106, a memory 107, and a crack prediction model 108. In some implementation, the system 105 may include other components (not shown in this fig.) to implement desired functions of the system 105. In an exemplary embodiment, the processor 106 may receive the vehicle body 102 image and a glazing 101 image from the portable device 103. Then, the processor 106 may obtain vehicle measurement data and glazing measurement data from the received vehicle body 102 image and the glazing 101 image, respectively. Further, crack prediction model 108 may compare the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold, respectively. In an exemplary embodiment, the crack prediction model 108 may be an Artificial Intelligence (AI) based model. Then, the crack prediction model 108 may determine a stress in the glazing 101 based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and the predefined glazing threshold, respectively. Further, the crack prediction model 108 may predict the internal crack in the glazing based on thedetermined stress in the glazing. A detailed explanation of the system 105 is provided in the forthcoming paragraphs in conjunction with Figs. 2 - 6.

[0036] Fig. 2 shows a detailed block diagram of the system 201 for predicting internal crack in a glazing (of Fig. 1), in accordance with an embodiment of the present disclosure. Consider a scenario where the glazing 101 is manufactured, then the system 201 (i.e., same as system 105) of the portable device predicts the occurrence of crack on the glazing 101 while mounting the glazing on the vehicle. The system 201 may comprise the one or more sensors 202, a processor 203, a memory 204 and an Artificial Intelligence (AI) based crack prediction model 205 but not limited thereto.

[0037] In one implementation, the system 201 may comprise the one or more sensors 202, which may capture one or more vehicle body 102 images and one or more glazing 101 images. In Non limiting examples of the one or more sensors 202 may include a camera of the portable device 103. The above-mentioned one or more sensors 202 may provide the vehicle body 102 image and the glazing 101 image to the processor 203 for further processing, respectively (described with respect to Fig. 3).

[0038] In one implementation, the processor 203 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. In one embodiment, the processor 203 may be configured as standalone control unit or combination of one or more control units for implementing the subject matter of the present disclosure. In some embodiments, the processor 203 may be implemented through software or hardware or a suitable combination of software and hardware as per the implementation requirements of the present disclosure. Among other capabilities, the processor 203 may be configured to fetch and execute computer-readable instructions and other information stored in the memory 204.

[0039] In one implementation, the AI based crack prediction model 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digitalsignal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. In the present disclosure, the AI based crack prediction model 205 may also be referred to as a crack prediction model 205. The crack prediction model 205 may be implemented for predicting the crack in the glazing 101. In a non-limiting exemplary embodiment, the crack prediction model 205 may be implemented using Machine Learning (ML) techniques. A person skilled in art would know that the crack prediction model 205 may be implemented with any known Artificial Intelligence (AI) techniques. The crack prediction model 205 may use the vehicle measurement data and the glazing measurement data for predicting the crack in the glazing. In one implementation, the crack prediction model 205 may be internally configured within the processor 203. Alternatively, the crack prediction model 205 may be externally configured or separately built outside the processor 203. In an exemplary embodiment, the crack prediction model 205 may be stored in the memory 204 and the processor 203 may fetch the instructions from the memory 204 to perform the desired functions. In some embodiments, the crack prediction model 205 may comprise a stress prediction unit 205a and a spacer optimization unit 205b. The stress prediction unit 205a may be used for determining the stress experience by the glazing due to the vehicle measurement data and the glazing measurement data. The spacer optimization unit 205b may be used for optimizing the number of spacers, dimensions of spacers and position of spacers on the vehicle body 102.

[0040] In some implementations, the memory 204 may be an external memory chip or an inbuilt Electrically Erasable Programmable Read-Only Memory (EEPROM) memory of the vehicle. In an embodiment, the memory 204 may be a computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or synchronous dynamic random-access memory (SDRAM) and / or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0041] In some implementations, the memory 204 may be configured to store the vehicle body 102 image and the glazing 101, the vehicle measurement data and the glazing measurement data or any other data 204b in the form of various data structures. In one exemplary embodiment, the vehicle body 102 image may refer to a frame of the vehicle where the glazing 101 needs to be mounted. In one exemplary embodiment, the glazing may refer to a windshield or glass that needs to be mounted on the frame of the vehicle. In nonlimiting exemplary embodiment, the vehicle measurement data may include, but not limited to a pre-stress value of a vehicle body, vehicle frame measurement, number of spacers, dimensions of spacers, position of spacers, inter-spacer distance. In non-limiting exemplary embodiment, the glazing measurement data may include, but not limited to glazing pre-stress value, mounting characteristics, glazing geometry measurement, and adhesive strain value.

[0042] In a non-limiting embodiment, the pre-stress value of the vehicle body 102 may refer to a stress generated in the vehicle body 102 while manufacturing the vehicle body 102. The pre-stress value may be obtained from vehicle manufacturing data. The vehicle frame measurement may include but is not limited to frame perimeter of the vehicle body 102, angle of one or more pillars in the frame. In a non-limiting exemplary embodiment, spacer’s in the vehicle body 102 may be used to maintain a support bracket at a predetermined distance and perpendicular to the surface of the glazing 101. The spacers may be installed along an upper or a lower horizontal edge of the glazing in the vehicle (as shown in Fig.4a).The number of spacers refers to the total number spacers used in the frame of the vehicle body 102. The dimension of spacers may include but not limited to length of the spacers, breadth of the spacers, depth of the spacers, and height of the spacers. The position of spacers refers to where exactly the spacers are placed in the frame. For instance, a first spacer may be placed at the lower side of the frame with thirty-degree inclination. The inter-spacer distance refers to a distance between two spacers placed one after the other (as shown in Fig.4a). Further, in a non-limiting embodiment, the vehicle measurement data may also include type of material in the spacers and thickness of spacer.

[0043] In a non-limiting embodiment, the glazing pre-stress may refer to stress generated in the glazing 101 while manufacturing the glazing 101. The glazing pre-stress value may be obtained from glazing manufacturing data. In an exemplary embodiment, the mounting characterizes may include but not limited to tapping force applied on the glazing 101, tapping pressure, tapping location, and mounting pin. The glazing geometry measurement may include but not limited to thickness of the glazing 101, type of manufacturing process used for manufacturing the glazing, total area of the glazing, and curves in the glazing 101. In an exemplary embodiment, the adhesive strain value refers to a strain present in the adhesive (i.e., glue) that may be used for mounting the glazing 101 on the vehicle body 102. In a nonlimiting example, the adhesive strain value may be affected by type of adhesive material, dimension where the adhesive is applied, curing dynamics and other adhesive applications. The other data 204b may include various temporary data and files generated by the processor 203 while performing various functions of the present disclosure. In some embodiments, the memory 204 may be communicatively coupled to the processor 203 and may be configured to store various data processed by the processor 203.

[0044] In an exemplary embodiment, in a training phase, the crack prediction model 205 may be trained using data such as vehicle related measurement data and glazing related measurement data that may include parameters such as pre-stresses in manufacturing, mounting process aspects such as adhesive, gaps on the frame, tapping force, and the like, and vehicle system aspects such as body residual stress and the like. Further, the parameters may be correlated with each other and an analysis may be carried out to derive an impact of each of the data with respect to other data leading to crack in the glazing 101 while mounting the glazing 101 on the vehicle body 102. In an exemplary embodiment, some of the parameters may have higher level of impact compared to other parameters. The parameters may be obtained from manufacturing data that may be recorded while manufacturing the glazing 101 and the vehicle body 102. In an exemplary embodiment, the parameters may be correlated as shown in exemplary Table 1 without limitation, below.•e-Stress e-Stress lass lass apping aps on the glass ickness(mm dius rce while lue strain ructure(mm etal(MPa) dPa) rm) fting(N) ipm / m) reakage •e-Stress in 99558688 97926404 98649771 895222 etal (MPa) 959541662 994162 995536756•e-Stress in 99558688 9955561 96988873 98512031 880427 ass (MPa) 967376262 996498429 lassickness(mm 95954166 96737626 9757613 90271232 92949422 800086959770098 lass 99555616 97330821 98709031 853014 dius(mm) 994162 975761317 997463889 ipping forcehile 97926404 96988873 9733082 99321516 850296?ting(N) 902712322 983510018 lue strain 98649771 98512031 9870903 99321516 859431 Ipm / m) 929494229 994565834 aps on theructure(mm 99553675 99649842 9974638 98351001 99456583 86602595977009889522230 88042715 8530143 85029611 85943133reakage 800086518 866025404Table 1

[0045] In real time, the processor 203 may receive the vehicle body 102 image and the glazing 101 image from the portable device 103. In an exemplary embodiment, the portable device 103 may capture the vehicle body 102 image and the glazing 101 image from the camera sensor of the portable device 103. Further, the processor 203 may process the vehicle body 102 image and the glazing 101 image to obtain the vehicle measurement data and the glazing measurement data from the received vehicle body 102 image and the glazing 101 image, respectively. The vehicle measurement data may include but not limited to the prestress value of the vehicle body 102, the vehicle frame measurement, the number of spacers, the dimensions of spacers, the position of spacers, the inter-spacer distance. The glazingmeasurement data may include but not limited to the glazing pre-stress value, the mounting characteristics, the glazing geometry measurement, and the adhesive strain value.

[0046] In an exemplary embodiment, the processor 203 may obtain the vehicle measurement data and glazing measurement data from the vehicle body 102 image and the glazing 101 image using a unique reference ID. The unique reference ID may be provided in the glazing 101, and the vehicle body 102. The unique reference ID on the vehicle body 102 may provide vehicle measurement data and the unique reference ID on the glazing 101 may provide glazing measurement data. The portable device 103 may receive the unique reference ID by scanning the vehicle body 102 image and the glazing 101 image. In a non-limiting embodiment, the unique reference ID may include but not limited to a QR code, a barcode, a Radio-Frequency Identification (RFID) and an alphanumeric string.

[0047] Upon receiving the vehicle measurement data and the glazing measurement data, the crack prediction model 205 may compare the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold, respectively. In an exemplary embodiment, the predefined vehicle threshold and the predefined glazing threshold may be derived based on the correlation between various parameters during the training phase (as shown in Table 1). The crack prediction model 205 may compare the vehicle measurement data and the glazing measurement data with the corresponding predefined vehicle threshold and the predefined glazing threshold respectively, to verify whether the vehicle measurement data and the glazing measurement data are within the respective thresholds.

[0048] Referring to Fig. 3, at step 302, the crack prediction model 205 may receive the pre-stress of vehicle body 102. Then, the crack prediction model 205 may compare the prestress of vehicle body 102 with a vehicle body threshold at step 303 to determine whether the pre-stress of vehicle body 102 within the vehicle body threshold. In case the pre-stress of vehicle body 102 is not within the vehicle body threshold at step 303, then the crack prediction model 205 may not proceed further and loop back to receive another pre-stress ofthe vehicle body 102. In case, the pre-stress of vehicle body 102 is within the vehicle body threshold, then the crack prediction model 205 may obtain the glazing pre-stress at step 304. Then, at step 305, the crack prediction model 205 may compare the glazing pre-stress with the glazing threshold. In case the glazing pre-stress is not within the glazing threshold, then the crack prediction model 205 may not proceed further and loop back to receive another glazing pre-stress of the glazing 101. In case, the glazing pre-stress of glazing 101 is within the glazing threshold, then the crack prediction model 205 may proceed further to obtain the mounting characteristics at step 306 and the glazing geometry measurements at step 307.

[0049] Further, upon receiving the pre-stress of the vehicle body 102, the glazing pre-stress of the glazing 101, the mounting characteristics, the glazing geometry measurements, the crack prediction model 205 may receive adhesive strain value at step 308. Then, at step 309, the crack prediction model may compare whether the adhesive strain value is within an adhesive threshold. In case the adhesive strain value is not within the adhesive threshold, then the crack prediction model 205 may not proceed further and may loop back to receive another adhesive strain value. In case, the adhesive strain value is within the adhesive threshold, then the crack prediction model 205 may proceed further to obtain the number of spacers, required dimension of spacers and the spacers position at step 310 and internal gap on the vehicle body 102 at step 311.

[0050] Referring back to Fig- 2, when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and the predefined glazing threshold respectively, the crack prediction model 205 may determine the stress in the glazing. In an exemplary embodiment, the stress may be determined by obtaining an assembly stress experienced by the glazing 101. The assembly stress may be generated due to the vehicle measurement data and the glazing measurement data such as tapping force, strength of the frame of the vehicle body 102, and vibration produced by the vehicle body 102. In a non-limiting embodiment, the assembly stress may be obtained by one or more sensors or a non-contact stress measurement equipment.

[0051] In one exemplary embodiment, one or more sensors such as a local RFID sensor may be placed across the periphery of the glazing 101 to determine the assembly stress experienced by the glazing 101 (as shown in Figure. 4b). Further, the one or more sensors may be a thin Film Sensors which are made of thin films or a custom printed electronic sensor film that may detect a pertinent parameter such as stress, force, strain etc. Further, the one or more sensors may be Passive RF Sensors that may be embedded into the glazing 101 and may detect parameters such as stress, strain, and force (as shown in Fig. 4b). The one or more sensors are suitable in low form factor needs, for quick integration and for product integration.

[0052] In other exemplary embodiments, one or more sensors 501 may be placed at the adhesive 502 of the vehicle body 102. The one or more sensors 501 may be embedded on the interfaces of the adhesive between glazing 101 and the vehicle frame. This configuration allows for the direct measurement of contact stresses of the glazing 101 to the vehicle frame created during the adhesive curing stage, continuous monitoring of the induced stress on the glazing 101 from vehicle body 102 onto glazing edge (as shown in Fig.5a). It is worth noting that the one or more sensors 202 may be used for capturing the vehicle body image and the glazing image and the one or more sensors 501 may be used for determining stress in the glazing 101.

[0053] In another exemplary embodiments, the one or more sensors 501 may be placed at the adhesive 502 and between the glazing 101. In this case, the one or more sensors 501 may be integrated in the glazing assembly in addition to the interfaces (as shown in Fig. 5b).Thus, this method of determining the stress may provide additional data point to determine the stresses on outer and inner glazing, especially at the edges near to the adhesive / seal region.

[0054] In another exemplary embodiment, the one or more sensors 501 may be placed below the spacer in the adhesive 502. In this case the one or more sensors 501 may be placed on the vehicle body 102 to glazing 101 interface but specifically in the regions where thereare spacers provided on the glazing (as shown in Fig. 5c). The spacer regions are identified as rubbery contact regions (but rigid than the adhesive / seal used) while mounting a glazing on to the frame and prevents glazing from contacting the frame. The initial stress levels are observed at the spacer interface compared to other locations and can provide information of location with highest stress concentration.

[0055] In another exemplary embodiment, the one or more sensors 501 may be placed near to the adhesive 502. In this case the one or more sensors 501 may be placed adjacent to the adhesive / spacer location to measure the stress in frame of the vehicle body 102 which can change over time (as shown in Fig 5d). Thus, providing a correlation between the stress generated by the vehicle frame and how it impacts the stress levels within the glazing.

[0056] Further, the one or more sensors 501 may be placed both in the adhesive 502 and near to the adhesive 502. Thus, showing a possibility for a single glazing like tailgate, quarterlite or sidefix etc. where the stress levels in the adhesive or joint / fixture with respect to the change vehicle frame stress may be determined. The glazing 101 sensor can be integrated into glazing via. printing and similarly extendable to the vehicle side sensor (as shown in Fig. 5e).

[0057] In a non-limiting embodiment, the non-contact stress measurement equipment for measuring the assembly stress may include but not limited to, a scattered light polariscope, a poleidoscope, a moving birefringence sensor.

[0058] Upon receiving the assembly stress, the crack prediction model 205 may compare the assembly stress with a pre-defined assembly stress threshold to determine the stress when the assembly stress exceeds the assembly pre-defined stress threshold. In case the assembly stress is within the assembly pre-defined stress threshold, then the glazing 101 may not be predicted with occurrence of crack. Thus, the glazing 101 may be mounted on the vehicle body 102. In case, the assembly stress exceeds the assembly pre-defined stress threshold, then the stress may be determined by determining a difference between the assembly stress and the assembly pre-defined stress threshold. In case of determining the stress, the crackprediction model 205 may predict the occurrence of crack in the glazing 101 based on the stress.

[0059] Further, upon determining the occurrence of crack, the crack prediction model 205 may optimize the number of spacers and the position of spacers on the vehicle body 102. The number of spacers and the position of spacers are optimized based on the determined stress in the glazing. Further, the crack prediction model 205 may determine a subsequent stress after optimizing the number of spacers, available dimensions of spacer and the position of spacers. Then, the crack prediction model 205 may compare the subsequent stress with the pre-defined stress threshold. The pre-defined stress threshold may be derived from the manufacturing data. Then, the glazing 101 may be mounted on the vehicle body 102 based on the comparison when the subsequent stress is within the pre-defined stress threshold. Further, the crack prediction model 205 may consider a feedback from the predicted crack. Thus, the efficiency and accuracy of the crack prediction model may be improved.

[0060] Referring back to Fig. 3, at step 312, the crack prediction model may obtain the assembly stress and compare the assembly stress with the assembly pre-defined stress threshold at step 313. In case, the assembly stress is within the assembly pre-defined stress threshold, the glazing may not be predicted with occurrence of crack. Thus, the glazing 101 may be mounted on the vehicle body 102 as indicated in step 314. In case, the assembly stress exceeds the assembly pre-defined stress threshold at step 313, the occurrence of the crack may be predicted at step 315. Further, crack prediction model 205 may optimize the number of spacers, dimensions of spacers and the position of spacers based on the determined stress. For instance, in case the stress is high, then the number of spacers may be increased by reducing the inter spacer gap to avoid the crack on the glazing 101. Further, the crack prediction model 205 may determine the subsequent stress after optimizing the number of spacers and the position of spacers. Then, at step 317, the crack prediction model 205 may compare whether the subsequent stress is within the pre-defined stress threshold. Upon determining that the subsequent stress is within the pre-defined stress threshold, then thecrack prediction model may mount the glazing on the vehicle body. Thus, the present invention prevents cracking of the glazing by determining the stress in the glazing.

[0061] FIG. 6 represents the flowchart of an exemplary method for predicting faults in a glazing assembly, in accordance with embodiments of the present disclosure. The order in which the method 600 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 600 may be considered to be implemented by the respective components and / or by the processor 203 and / or the crack prediction model 205 of FIG. 2.

[0062] At step 601, the method may include receiving the vehicle body 102 image and the glazing 101 image from the portable device 103. In one implementation, the processor 203 may receive the vehicle body 102 image and the glazing 101 image. In another implementation, the crack prediction model 205 may receive the vehicle body 102 image and the glazing 101 image.

[0063] At step 602, the method may include obtaining the vehicle measurement data and the glazing measurement data from the received vehicle body image and the glazing image, respectively. In one implementation, the processor 203 may obtain the vehicle measurement data and the glazing measurement data. In another implementation, the crack prediction model 205 may obtain the vehicle measurement data and the glazing measurement data.

[0064] At step 603, the method may include comparing the vehicle measurement data and the glazing measurement data with the corresponding predefined vehicle threshold and the predefined glazing threshold, respectively. In one implementation, the processor 203 may compare the vehicle measurement data and the glazing measurement data with the corresponding predefined vehicle threshold and the predefined glazing threshold,respectively. In another implementation, the crack prediction model 205 may compare the vehicle measurement data and the glazing measurement data with the corresponding predefined vehicle threshold and the predefined glazing threshold, respectively.

[0065] At step 604, the method may include determining the stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and the predefined glazing threshold, respectively. In one implementation, the processor 203 may determine the stress in the glazing. In another implementation, the crack prediction model 205 may determine the stress in the glazing.

[0066] At step 605, the method may include predicting the crack in the glazing based on the determined stress in the glazing. In one implementation, the processor 203 may predict the crack in the glazing. In another implementation, the crack prediction model 205 may predict the crack in the glazing.

[0067] The order in which the method 600 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described.

[0068] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.

[0069] Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.

[0070] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer- readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[0071] Suitable processors include, by way of example, a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a graphic processing unit (GPU), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), and / or a state machine.

[0072] Advantages of the embodiment of the present disclosure are illustrated herein-

[0073] As previously indicated, aspects of the present disclosure predict cracking in the glazing. The present invention determines the stress in the glazing considering the vehicle measurement data and the glazing measurement data. Thus, the present invention accurately predicts the occurrence of crack before mounting the glazing on eth vehicle body. Further, the present invention determines the number of spacers and the position of spacers and then optimizes the number of spacers, and the position of spacers based on the stress to dampen the stress in the glazing. Thus, cracking in the glazing may be prevented. Further, as thepresent invention considers the feedback from the predicted crack, the crack prediction model may be improved efficiently.1

Claims

CLAIMS1. A method for predicting a crack in a glazing while mounting the glazing on a vehicle, comprising:receiving a vehicle body image and a glazing image from a portable device;obtaining vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively;characterized in that: comparing the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold respectively;determining a stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and the predefined glazing threshold respectively; andpredicting the crack in the glazing based on the determined stress in the glazing.

2. The method as claimed in claim 1, wherein the vehicle measurement data comprises at least one of: a pre-stress value of a vehicle body, vehicle frame measurement, number of spacers, position of spacers, dimensions of spacers, inter-spacer distance, andwherein the glazing measurement data comprises at least one of: glazing pre-stress value, mounting characteristics, glazing geometry measurement, and adhesive strain value.

3. The method as claimed in claim 1, wherein determining the stress in the glazing, comprising: obtaining an assembly stress experienced by the glazing;comparing the assembly stress with a pre-defined assembly stress threshold; and determining the stress when the assembly stress exceeds a pre-defined stress threshold.

4. The method as claimed in claim 3, wherein the assembly stress in the glazing is determined by:one of one or more sensors and a non-contact stress measurement equipment.

5. The method as claimed in claim 2, further comprising:optimizing the number of spacers and the position of spacers on the vehicle body if the crack is predicted, wherein the number of spacers and the position of spacers are optimized based on the determined stress in the glazing.

6. The method as claimed in claim 5, further comprising:determining a subsequent stress after optimizing the number of spacers and the position of spacers;comparing the subsequent stress with a pre-defined stress threshold; andmounting the glazing on the vehicle body based on the comparison when the subsequent stress is within the pre-defined stress threshold.

7. A system for predicting crack in a glazing while mounting the glazing on a vehicle, the system comprises:a memory;at least one processor coupled with the memory;characterized in that, wherein the at least one processor is configured toreceive a vehicle body image and a glazing image from a portable device;obtain vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively;compare the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold respectively;determine a stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and the predefined glazing threshold respectively; andpredict the crack in the glazing based on the determined stress in the glazing.

8. The system as claimed in claim 7, wherein the vehicle measurement data comprises at least one of: a pre-stress value of a vehicle body, vehicle frame measurement, number of spacers, dimensions of spacers, position of spacers, inter-spacer distance, andwherein the glazing measurement data comprises at least one of: glazing pre-stress value, mounting characteristics, glazing geometry measurement, and adhesive strain value.

9. The system as claimed in claim 7, wherein to determine the stress in the glazing, the at least one processor is configured to:obtain an assembly stress experienced by the glazing;compare the assembly stress with a pre-defined assembly stress threshold; and determine the stress when the assembly stress exceeds a pre-defined stress threshold.

10. The system as claimed in claim 9, wherein the assembly stress in the glazing is determined by:one of one or more sensors and a non-contact stress measurement equipment.

11. The system as claimed in claim 8, further the at least one processor is configured to:optimize the number of spacers and the position of spacers on the vehicle body if the crack is predicted, wherein the number of spacers and the position of spacers are optimized based on the determined stress in the glazing.

12. The system as claimed in claim 11, further the at least one processor is configured to: determine a subsequent stress after optimizing the number of spacers, dimensions of spacers and the position of spacers;compare the subsequent stress with a pre-defined stress threshold; andmount the glazing on the vehicle body based on the comparison when the subsequent stress is within the pre-defined stress threshold.

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