Method for the quality inspection of ophthalmic lenses

Abstract

The invention relates to a method for the quality inspection of ophthalmic lenses in a production system comprising a plurality of successive stations, wherein the lenses are examined by means of a first inspection station, the method comprising: providing a first training database, a first classification unit, a first decision unit in connection with the first inspection station, classifying a first irregularity on the ophthalmic lens and assigning a label from the first training database by means of a classification function; generating a first result value comprising a positive assessment, a negative assessment or a rework assessment; providing at least one second inspection station downstream of the first station; providing a second training database, a second classification unit, a second decision unit in connection with the second inspection station; generating a second result value comprising a positive assessment, a negative assessment or a rework assessment; comparing the first result value with the second result value and generating feedback information from the comparison and introducing the feedback information into the first training database and into the classification function.

Description

[0001] QUALITY CONTROL PROCEDURES

[0002] OPHTHALMIC LENSES

[0003] Description

[0004] The invention relates to a method for quality control of ophthalmic lenses in a manufacturing system comprising several consecutive stations for processing the lenses, wherein the lenses are examined for quality control by means of at least one first inspection station by generating a basic image of the ophthalmic lens in which at least one possible abnormality on the ophthalmic lens is identified. The invention further relates to a system for quality control of ophthalmic lenses using such a method. PRIOR ART

[0005] WO 2023 / 041659 A1 describes a method for quality control of ophthalmic lenses. The quality control described therein concerns the identification and classification of possible abnormalities on the ophthalmic lens, whereby lenses without abnormalities are also examined, for example, as part of a 100% quality control. Thus, the abnormalities are "possibly" present, as stated, but they do not necessarily occur in every ophthalmic lens.

[0006] Possible abnormalities in the basic images recorded by the ophthalmic lenses can concern defects, but also intentional structures such as markings or the like, for example structures present in the casting mold, as well as contamination, which can be more or less pronounced depending on the abnormality.

[0007] The measurement method for quality control includes a light source for generating a sinusoidal fringe pattern, for example, which is recorded with a camera. The light generated by the light source is passed through the ophthalmic lens and then recorded with a camera. The sinusoidal fringe patterns are then displayed on a screen, for example, and / or evaluated using a corresponding device. The sinusoidal fringe patterns can exhibit phase shifts, have different wavelengths and run horizontally and vertically, for example. Using other known methods, certain abnormalities can be made more visible. The basic images generated are then stored in a database. The different types of abnormalities are classified so that a decision can be made as to which class the abnormality should be assigned.

[0008] A disadvantage of quality control in a manufacturing system with multiple stations for processing and treating a large number of lenses arises when quality control is carried out by the inspection station before or after the first or a number of stations in the manufacturing system, whereby the processing or treatment of the ophthalmic lenses after further stations in the manufacturing system can either weaken or exacerbate the abnormalities. For example, it may happen that certain abnormalities resulting from the result from the first inspection station result in the ophthalmic lens not being passed on to the further stations in the manufacturing system at all, even though this would actually be possible because, for example, subsequent coating of the surface of the ophthalmic lenses could eliminate the abnormality or at least classify it as a good part.

[0009] On the other hand, if certain minor anomalies are assigned to a category according to which the lens is forwarded to the next step, this can lead to the lens being classified as a defective part, particularly after coating or other surface treatment, contrary to the assessment from the first evaluation unit, even though simple rework, for example, by the stations upstream of the first inspection station, could ultimately have produced the lens as a good part. A separate manual station may also be set up, or the manually operated station may not be uniformly structured with the station directly upstream of the station that mostly handles cleaning ophthalmic lenses.However, not every lens rated as poor from the second inspection station can be sent back for rework. This is particularly due to the fact that once a coating has been applied, the lens cannot generally be returned to any previous station in the production system.

[0010] It is therefore desirable to improve the quality control process so that the assessment of a good part of the ophthalmic lens at the first inspection station corresponds as closely as possible to the assessment of a good part after the lens has been fully processed by the manufacturing system. Therefore, the deviation between the assessment from the first inspection station and the final assessment after further processing of the lens by the entire manufacturing system should be as small as possible.

[0011] DISCLOSURE OF THE INVENTION

[0012] The object of the invention is to improve a method for quality control of ophthalmic lenses with at least one inspection station. The method is to be operated in such a way that the result of the evaluation of the ophthalmic lens from the quality control of the first inspection station enables the most accurate prediction possible regarding the quality outcome of the ophthalmic lens after passing through the entire production system. The evaluation, classification, and / or categorization of the abnormalities of the ophthalmic lens from the inspection of the first inspection station should therefore be as accurate as possible.

[0013] This object is achieved on the basis of a method according to claim 1 and on the basis of a system according to claim 12 with the respective characterizing features. Advantageous developments of the invention are specified in the dependent claims. To achieve the object preceding the invention, the following further steps are proposed for the method: Providing a first training database in which labels are stored which have information comprising various first anomalies; Providing a first classification unit in connection with the first training database, wherein the classification unit is trained by training with the training database; Providing a first decision unit in connection with the first control station and in connection with the classification unit;Classifying the first abnormality of the base image on the ophthalmic lens and assigning a label to the ophthalmic lens using a classification function; Generating a first result value from the classification of the first abnormality comprising the information of a positive evaluation, a negative evaluation, or a rework evaluation; Providing at least one second control station after the first, the second, or a further station of the manufacturing system; Providing a second training database in which labels are stored that have information comprising various second abnormalities; Providing a second classification unit in connection with the second training database; Providing a second decision unit in connection with the second control station and in connection with the second classification unit;Generating a second result value from the classification of the second anomaly, comprising the information of a positive rating, a negative rating, or a rework rating; comparing the first result value with the second result value; generating feedback information from the comparison and incorporating the feedback information into the first training database and into the classification function. The base image is a processed base image, and from the sinusoidal images, normally 24 to 100 individual images, several base images are generated, for example 4 base images. These 4 base images are then further processed; various filters can be used, and in this way around 30 images can be generated in which the various anomalies emerge, or the anomalies can be determined from the combination of the 30 images.

[0014] Regarding labels, it should be noted that a label can be assigned to each lens, but it is preferred that a label be assigned to each abnormality, specific characteristics of the abnormality, or even some or every pixel of an abnormality from the evaluation. Thus, each pixel can be assigned a label for in-class or not-in-class for each class of abnormalities, including an assessment of the severity of an abnormality, which then refers to contiguous regions of a class.

[0015] The result can also depend on the interaction of all abnormalities. While a single abnormality can, for example, lead to a negative rating, it is also possible that this only leads to a negative rating in conjunction with another abnormality that would not have led to a negative rating on its own.

[0016] The core idea of ​​the invention is the implementation of the method for quality control of ophthalmic lenses with feedback between the second control station in conjunction with the second decision unit, in order to make the result from the second decision unit available to the first control station with the first decision unit. This feedback system extends the method to allow learning, so that the quality of the information in the training database, in particular the labels and their classification, is continuously improved in conjunction with the classification function in the further application of the method.

[0017] The indication that the decision-making unit, the classification unit, the training database and / or the control station are connected to each other concerns an exchange of data between these units.

[0018] With the at least two control stations according to the invention, a testing infrastructure can be created with which data originating from the first control station can also be available at a later point in time, and the classification of an anomaly can also be retrieved at a later point in time. Measurement data is saved for later review, but not all measurement data is later transferred to the training database. In the event of deviations in the assessment of the ophthalmic lens in the second control station due to defects that were already present during the inspection at the first control station, this deviation can be optimized by adapting the evaluation algorithm. The evaluation algorithm serves, for example, through the classification function to classify the anomaly in the first control station.

[0019] In this way, the classification function can be trained in such a way that it is improved so that there are as few deviations as possible between the assessment of the anomaly from the first control station and that from the second control station. Storing the information in the training database in particular further improves this. The training database can receive additional information, or existing information can be overwritten and thus qualitatively improved so that the classification unit classifies the anomalies in the ophthalmic lenses more effectively. If possible, the result from the second control station is the same as the result from the first control station, so that the two result values ​​​​match. The newly acquired information can be stored using labels that are overwritten or newly stored in the training database.

[0020] Thus, an essential feature of the invention is that the feedback information is used to train the classification function and / or create and / or improve the labels from the training database, such that the decision of the first decision unit as to whether the ophthalmic lens is a good part, a bad part, or a reworkable part is improved. For example, the first decision unit can classify an ophthalmic lens as a good part, but the assessed abnormality is such that, after passing through the further stations of the manufacturing system, the classification of the second decision unit, after the measurement by the second control station, gives the ophthalmic lens a negative rating.The negative assessment from the second decision unit is compared with the assessment of the lens as a good part from the first decision unit, so that the labeling can be determined so that this particular anomaly is more likely to produce a defective part or at least a reworkable part in the future, i.e., for later, subsequent measurements. It should be noted that the reclassification by the first decision unit does not have to occur immediately; for example, the label can be rewritten or recreated, so to speak, only after a corresponding deviation regarding a particular anomaly has accumulated, so that the first decision unit can make a different decision if the corresponding anomaly is repeated.

[0021] The second result value from the second decision unit thus categorizes the lens as a good part, a bad part, or a reworkable part. A good part either results from a good part in the first decision unit, resulting in a true-positive rating, or the good part can also result from a bad part in the first decision unit, resulting in a false-negative rating. A bad part can either result from a good part in the first decision unit that was incorrectly classified, resulting in a false-positive rating, or the bad part can result from a bad part in the first decision unit that is nevertheless passed on to further processing by the production system, resulting in a true-negative rating.The defective part from the first decision unit can also be manually fed to the subsequent stations of the production system, as it cannot be ruled out that the initially negative assessment of the anomaly from the first decision unit may ultimately be largely transferred to the second decision unit due to the subsequent stations, particularly due to a coating or other surface treatment. This is because the subsequent processing and treatment steps of the ophthalmic lenses in the subsequent stations after the first inspection station may reduce the anomaly or even eliminate it completely. The assessment of the anomalies refers to the result of the first station.Finally, it may also be the case that the lens was classified as a good part in the first inspection station and a new defect is discovered for the first time during the subsequent process, resulting in the lens being classified as a bad part in the second inspection station. The classification function of the first decision unit is adjusted according to a particular advantage so that the deviation between the first result value and the second result value is minimal.In the first decision unit, a comparison is made between the abnormality recorded at the first control station and an abnormality stored in the first training database, so that the abnormality is assigned to a specific classification, which is done with the classification unit. If the recorded abnormality corresponds to a specific abnormality stored in the training database, the ophthalmic lens is classified accordingly and provided with a label, which label includes the classification data, wherein the label can also include metadata, in particular in order to be able to identify the lens with the corresponding abnormality even during the later pass through the stations of the production system, in particular by the subsequently set up second classification unit or second decision unit.

[0022] The second anomaly, which remains after passing through the stations of the production system, is labeled with the second classification unit, so that the second decision unit can output a second result value accordingly. If the comparison of the second anomaly, for example, with a label from the second training database, shows a very high match, a check is carried out to determine how the second anomaly should be categorized, ultimately determining whether it is a good part, a bad part, or a reworkable part, since lenses that have been passed through the coating stations can also be reworked if they have certain anomalies.

[0023] The classification function of the first decision unit is changed in such a way that the negative case of the evaluation decreases proportionally, and subsequently more ophthalmic lenses are fed to the second station or further stations of the production system until the proportion of defective parts increases from the second result value. This increase, via the feedback information, counteracts the change in the classification function of the first decision unit. In other words, the change in the classification function can mean an overshoot of the second result value, so that the proportion of defective parts increases again, allowing the classification function to be optimized so that the number of defective parts alternates around an optimum.This means that too many lenses can be rejected in the first decision unit, resulting in false-negative assessments. However, without feedback, this does not allow for an approximation of the actual reject rate. Likewise, too few ophthalmic lenses can be rejected, leading to a false-positive assessment. This also causes the assessed reject rate to deviate further from the actual reject rate. However, with the method according to the invention, the number of lenses rejected in excess, i.e. the false-negative assessment, can be reduced, as can the number of lenses rejected in too few, which corresponds to the false-positive assessment. In this way, the assessed reject rate can be brought closer to the actual reject rate, so that fewer lenses result from the second decision unit as defective parts or reworkable parts.The quality of the control is thus improved and the second result value results in a higher proportion of positively evaluated ophthalmic lenses.

[0024] As already mentioned, it is possible that ophthalmic lenses that receive a negative evaluation from the first decision unit may subsequently be evaluated as positive and manually transferred to the next station. This includes, for example, lenses that are categorized as rejects or non-reworkable by the decision unit, but are nevertheless forwarded by an operator to the next stations of the production system. It may happen that, particularly due to the effects on the lens in the next stations, for example, through a coating or other surface treatment, the anomaly is assigned a new category, according to which a positive evaluation is then made by the second decision unit.The ophthalmic lenses manually fed to the next station by a person are evaluated by the second decision unit, with the resulting second result value for these lenses also being fed back to the classification function of the first decision unit via the feedback information. This allows the evaluated rejection rate to be brought even closer to the actual rejection rate.

[0025] The first decision unit can operate with human assistance or completely autonomously; preferably, it can be operated purely by computer numerical means. In contrast, the second decision unit can also be operated fully automatically, using computer numerical means; preferably, the second decision unit is operated manually under further human supervision in addition to the computer numerical operation. It is also conceivable for the first decision unit and / or the second decision unit to be operated using an AI module, i.e., one comprising artificial intelligence, in particular for training the classification function based on the feedback information.In particular, artificial intelligence can help to optimize the quality of the labels from a large number of measurement data and / or a large number of different anomalies, so that the first classification unit creates a basis for the decision unit to better compare the anomalies with the stored labels of the training database, and to optimize the first decision in such a way that the quality of the first result value of the first decision unit further increases.

[0026] The ophthalmic lenses are provided with the label either directly or at least indirectly, for example in conjunction with an identification number for the passage of the lens through the

[0027] Manufacturing system, wherein the labels contain at least data for

[0028] Recognition of the specific ophthalmic lens and data relating to at least one of the first and second abnormalities or outcome values. In particular, so-called metadata can be part of the label in order to identify the lens at a later point in time and retrieve the outcome values ​​from the decision units.

[0029] The object of the invention is further achieved by a system for quality control of ophthalmic lenses, which is designed for use in a manufacturing system comprising several consecutive stations for processing the lenses. It is provided that a control station and a first decision unit are set up, and independently of this, at least a second control station and a second decision unit are set up, wherein at least the second decision unit is connected to the first decision unit via a communication channel, in particular a cloud system, via which feedback information can be transmitted from the second decision unit to the first decision unit in order to influence a classification function of the first decision unit.The further features and advantages described in connection with the method also apply to the system according to the invention for quality control of ophthalmic lenses. In particular, at least the first decision unit and / or the second decision unit can comprise a KI module to utilize artificial intelligence. It is also conceivable for the first decision unit to be computer-numerically operated and for the second decision unit to be computer-numerically operated and / or manually operated by a person.

[0030] PREFERRED EMBODIMENT OF THE INVENTION

[0031] Further measures improving the invention are described in more detail below, together with the description of a preferred embodiment of the invention, with reference to the figures. It shows:

[0032] Figure 1 is a schematic view of the architecture of the system for carrying out the method according to the invention for quality control of ophthalmic lenses;

[0033] Figure 2 is a diagram in which the decision units are centrally shown, which are passed through by the ophthalmic lenses with schematically shown abnormalities;

[0034] Figure 3 is a diagram showing a rejection rate over time without any feedback between the second decision unit and the first decision unit; and

[0035] Figure 4 is a diagram in which the reject rate is plotted over time with an inventive feedback between the second decision unit and the first

[0036] Decision unit. Figure 1 schematically shows several stations 110, 120, 130, and 140, which are shown merely as examples and are intended to represent the components of the manufacturing system 100 for processing and treating ophthalmic lenses 1. A row of ophthalmic lenses 1 is shown, which are fed to the first station 110, and in stations 110 and 120, the lenses 1 are milled, turned, and polished, for example. Laser engraving or other marking processes can also be performed, which are also later subjected to quality control.

[0037] The second station 120 is followed by the first control station 10, which is designed, for example, according to the disclosure of WO 2023 / 041659 A1.

[0038] The further stations 130 and 140 can, for example, carry out coating processes, hardening processes and other finishing of the surfaces of the ophthalmic lenses 1, so that a final inspection by means of the second inspection station 15 is only carried out after these treatment steps.

[0039] A basic image 11 emerges from the first inspection station 10 and is fed to the first classification unit 13 and the first decision unit 14. The first classification unit 13 in conjunction with the first decision unit 14 determines whether the currently inspected ophthalmic lens 1 is a positive case P or a negative case N. If the case is positive P, the lens 1 is transferred to the third station 130 shown as an example. If the case is negative P, the ophthalmic lens 1 is either completely rejected, shown with a crossed-out lens, and the further negative case can mean that the lens is returned to a previous station 110 and / or 120 or is manually reworked if the ophthalmic lens 1 is reworkable.In the first decision unit 14, a first result value E1 is generated, based on which the decision is made as to whether the lens represents a positive case P or a negative case N. For this purpose, the first decision unit 14 can have a Kl module, exemplified as a separate unit, but which can also be part of the decision unit 14.

[0040] The second inspection station 15 is connected to a second classification unit 17 and a second decision unit 18, which inspects the further processed ophthalmic lens 1 and produces a second result value E2, which is used to determine whether the part is good or bad. The second training database and the second training unit are not shown here.

[0041] The second result value E2 is first transmitted to the first training database 12 and to the first training unit 21 via feedback information R, in particular using a cloud system 19, so that the parameters PA can be trained therein, with which the classification function KF is supplied.

[0042] The classification function KF is coupled to the first decision unit 14, so that the first decision unit 14 can ultimately determine, by evaluating the parameters PA from the training unit 21, whether the first result value E1 and the second result value E2 match. If these match, as shown by a comparison V, the decision from the first decision unit 14 was correct; if they do not match, the decision from the first decision unit 14 was incorrect. The feedback information R thus continues from the second result value E2 via the cloud system 19, the first training database 12, and the first training unit 21, which can be Kl-based from a Kl module, to the classification function KF.

[0043] The feedback consists of comparing the result values ​​E1 and E2, which identifies potential for improvement. This allows new labels E to be generated from the classified measurement data from the control station 10 and the training database 12, which are then entered into the training database 12. New parameters PA are then generated via the training module 21 and passed on to the classification unit 13 or entered into the training database.

[0044] To also generate the case in which an ophthalmic lens 1 classified as a negative case N by the first decision unit 14 is nevertheless fed to the further process, i.e., the third station 130, a manual feed MZ can be performed so that an operator still feeds the negatively assessed ophthalmic lens 1 to the third station 130. If the classification of this ophthalmic lens 1 in the second control station 15 in conjunction with the second decision unit 18 is positive, a false-negative assessment occurs. The result from the second decision unit 18 therefore includes good parts and bad parts, i.e., lenses 1 that can be used and lenses 1 that are either rejects or could be reworked.The good parts can therefore result from a true-positive evaluation TP, a false-negative evaluation FN, and the bad parts can result from a false-positive evaluation FP or a true-negative evaluation TN.

[0045] The illustration further shows a cloud system 19, via which communication between the second decision-making unit 18 and the first decision-making unit 14 can take place even over greater distances. Direct connections via a cable system, a radio system, or the like are of course also possible, particularly if the first and second control stations 10, 15 or the first and second decision-making units 14, 18 are not far apart. In this respect, a local computer can also be used. However, if, for example, the second control with the second control station 15 takes place remotely from the first control with the first control station 10, for example, if the production system 100 is designed in multiple parts and positioned at different locations, the cloud system 19 can be correspondingly advantageous.

[0046] Figure 2 shows, in a schematic view, first the first decision unit 14 and the second decision unit 18, wherein the first decision unit 14 is designed purely computer-numerically, while the second decision unit 18 can be computer-numerically or equipped with an operator.

[0047] The lenses 1 are transferred to the first inspection station 10, each with, for example, a first abnormality A1. The abnormalities A1 can represent defects, intentional structures, markings, structures from the casting mold, contamination, and the like. In particular, the defects can be divided into many different classifications. For example, they can be evaluated based on the quotient of the circumference and the area of ​​the defects detected using the measuring method from the first inspection station 10, which is located upstream of the first classification unit 13 and the first decision unit 14. The same applies to the second inspection station 15, which inspects the finished lenses 1 and ultimately feeds the results to the second classification unit 18 and the second decision unit 18.This large number of different embodiments of the first and second abnormalities are stored in respective training databases 12 and 16 as labels E. The first training database 12 stores a correspondingly large number of variously implemented first and second abnormalities in the form of labels E, which are thus stored as a kind of comparison value in the training database 12 or 16. If an ophthalmic lens 1 with a first abnormality A1 is to be classified, the abnormality A1 is considered via the first classification unit 13. Downstream of the first training database 12 is a first training unit 21 in which parameters PA are generated, based on which the first classification unit 13 classifies the lenses 1 to be tested with the specific abnormalities A1. A training process must be distinguished from the actual process.During training, the similarities between the anomalies A1 and the parameters stored in the labels E are evaluated. During the process itself, it is then checked whether the anomalies correspond to these parameters and are labeled accordingly.

[0048] Finally, these lenses 1, with the respective case evaluations as positive case P or negative case N, are forwarded for further processing, represented by the manufacturing system 100 following the first decision unit 14. Further processing in the manufacturing system 100 results in further processed ophthalmic lenses 1, which are transferred to the second inspection station 15, which is located upstream of the second classification unit 17 and the second decision unit 18. The second decision unit 18 is also connected to the second training database 16 storing the labels E via the second classification unit 17.Parameters PA from the second training unit 22 are transferred to the second classification unit 17 in the same way, so that the comparison process is repeated to finally classify the lenses 1 from the second decision unit 18 accordingly. By way of example, it is shown that the middle lens now also represents a positive case P, but the one from the first decision unit 14 was classified as a negative case N. It is also possible to feed the lenses 1 from the second control station 15 and the second decision unit 18 to the production system 100 again to enable rework.

[0049] The core of the invention is now the comparison between the result of the first decision unit 14 and the second decision unit 18 with the respective result values ​​E1 and E2, so that feedback information R is now generated from this comparison, which is transferred to the first training database 12 or the first classification unit 13. This allows the quality of the database to be improved via the first anomalies A1 and the corresponding classifications, whereby it is additionally possible to store further anomalies A1 in the first training database 12.

[0050] By means of an artificial intelligence K1 not shown in detail here, the first classification unit 13 can be executed as a learning system over the further operating period of the control system in order to further improve the classification of the abnormalities A1 of the ophthalmic lenses 1 by means of the first classification unit 13 with further ongoing operation of the system, in particular to improve the quality of the label E that is attached to the lenses 1.

[0051] Figures 3 and 4 show a so-called normalized reject rate nA over time t. The curve represents the assessed reject rate bA, which alternates with a real reject rate rA. The curve of the assessed reject rate bA shows that the assessed reject rate bA according to Figure 3 without feedback information between the second decision unit and the first decision unit does not approximate the real reject rate rA, whereas according to Figure 4 with feedback information between the second and the first decision unit, the assessed reject rate bA approximates the real reject rate rA. The false-negative evaluations are plotted above the real reject rate rA, and the false-positive evaluations are plotted below.Through the feedback information applied according to Figure 4, the first decision unit in conjunction with the classification unit learns the improved evaluation, so that the result value of the second decision unit more often agrees with the result value of the first decision unit, shown by an approximating evaluated reject rate bA to the real reject rate rA.

[0052] The invention is not limited to the preferred embodiment described above. Rather, a number of variants are conceivable, which utilize the presented solution even in fundamentally different embodiments. All features and / or advantages apparent from the claims, the description, or the drawings, including structural details or spatial arrangements, may be essential to the invention both individually and in a wide variety of combinations.

[0053] List of reference symbols:

[0054] I ophthalmic lens

[0055] 10 first control station

[0056] II Basic image

[0057] 12 first training database

[0058] 13 first classification unit

[0059] 14 first decision-making unit

[0060] 15 second control station

[0061] 16 second training database

[0062] 17 second classification unit

[0063] 18 second decision-making unit

[0064] 19 Cloud system

[0065] 100 manufacturing system

[0066] 110 first station

[0067] 120 second station

[0068] 130 third station

[0069] 140 fourth station

[0070] E label

[0071] E1 first result value

[0072] E2 second result value

[0073] PA parameters

[0074] P positive case

[0075] N negative case

[0076] FN false-negative assessment

[0077] TP true-positive rating

[0078] FP false-positive rating TN true-negative rating

[0079] R Feedback information

[0080] V Comparison

[0081] A1 first abnormality A2 second abnormality

[0082] KF classification function MZ manual feeding nA normalized scrap rate rA real scrap rate bA assessed scrap rate

Claims

Claims:

1. A method for quality control of ophthalmic lenses (1) in a manufacturing system (100) comprising a plurality of successive stations (110, 120, 130, 140) for processing the lenses (1), wherein the lenses (1) are examined for quality control by means of at least one first control station (10) by generating a basic image (11) of the ophthalmic lens (1) in which at least one possible abnormality (A1) on the ophthalmic lens (1) is identified, wherein the method comprises at least the following steps: - providing a first training database (12) in which labels (E) are stored which have information comprising various first anomalies (A1); - Providing a first classification unit (13) in Connection to the first training database (12); - Providing a first decision unit (14) in Connection to the first control station (10) and in connection with the classification unit (13); - Classifying the first abnormality (A1) of the basic image (11) on the ophthalmic lens (1) and assigning a label (E) based on the training parameters from the first training unit to the ophthalmic lens (1) by means of a classification function (KF); - generating a first result value (E1) from the classification of the first abnormality (A1) comprising the information of a positive evaluation, a negative evaluation or a rework evaluation; - providing at least one second control station (15) after the first (110), the second (120) or a further station (140) of the manufacturing system (100); - providing a second training database (16) in which labels (E) are stored which have information comprising various second anomalies (A2); - Providing a second classification unit (17) in Connection to the second training database (16); - Providing a second decision unit (18) in Connection to the second control station (15) and in connection with the second classification unit (17); - generating a second result value (E2) from the classification of the second abnormality (A2) comprising the information of a positive assessment, a negative assessment or a rework assessment; - Comparison of the first result value (E1) with the second result value (E2); - Generating feedback information (R) from the comparison and - Introducing the feedback information (R) into the first training database (12) and into the classification function (KF).

2. Method according to claim 1, characterized in that the training of the classification function (KF) and / or the creation and / or improvement of the labels (E) from the training database (12) is carried out by means of the feedback information (R), such that the decision of the first decision unit (14) is improved as to whether the ophthalmic Lens (1) is present as a good part (G), as a bad part (S) or as a reworkable part (N).

3. Method according to claim 1 or 2, characterized in that the second result value (E2) the ophthalmic lens (1) is present as a good part (G), as a bad part (S) or as a reworkable part N), wherein a good part (G) either results from a good part (G) of the first decision unit (14), so that a true-positive evaluation (TP) is produced, or that the good part (G) results from a bad part (S) of the first decision unit (14), so that a false-negative evaluation (FN) is produced and / or wherein the bad part (S) either results from a good part (G) of the first decision unit (14), so that a false-positive evaluation (FP) is produced or the bad part (S) results from a bad part (S) of the first decision unit (14), so that a true-negative evaluation (TN) is produced.

4. Method according to one of the preceding claims, characterized in that the classification function (KF) of the first decision unit (14) is adapted via the feedback information (R) such that a minimal deviation of the first result value (E1) from the second result value (E2) results.

5. Method according to one of the preceding claims, characterized in that the classification function (KF) of the first decision unit (14) is changed so that the negative case (N) of the evaluation decreases proportionately and subsequently more lenses (1) are fed to the second station (120) or further station of the production system (100) until the proportion of defective parts (S) from the second result value (E2) increases and this increase, via the feedback information (R), adapts the classification function (KF) of the first decision unit (14) to counteract the change.

6. Method according to one of the preceding claims, characterized in that ophthalmic lenses (1) which have the negative case (N) of the evaluation from the first decision unit (14) are subsequently evaluated as positive and are manually transferred to the further station (130).

7. Method according to claim 6, characterized in that the ophthalmic lenses (1) manually fed to the further station (130) by a person are evaluated by means of the second decision unit (18), wherein the resulting second result value (E2) of these lenses (1) is fed back to the classification function (KF) of the first decision unit (14) via the feedback information (R).

8. Method according to one of the preceding claims, characterized in that a local data network or a cloud system (19) is set up for data exchange, wherein the first control station (10), the second control station (15), the first decision unit (14) and / or the second decision unit (18) are connected by means of the cloud Systems (19) communicate with each other in a particularly decentralised manner or exchange data.

9. Method according to one of the preceding claims, characterized in that the first decision unit (14) is operated computer-numerically and that the second decision unit (18) is operated computer-numerically and / or manually by a person.

10. Method according to one of the preceding claims, characterized in that the first decision unit (14) and / or the second decision unit (18) is operated by means of a Kl module comprising artificial intelligence, in particular for training the classification function (KF) based on the feedback information (R).

11. Method according to one of the preceding claims, characterized in that the ophthalmic lenses (1) are provided directly or at least indirectly with a label (E) which has at least data for the recognition of the specific ophthalmic lens (1) and data relating to at least one of the first and second abnormalities (A1, A2).

12. System for quality control of ophthalmic lenses (1), which is designed for use in a manufacturing system (100) comprising several successive stations (110, 120, 130, 140) for processing the lenses (1), characterized in that that a first control station (10) and a first decision unit (14) are set up and, independently thereof, at least a second control station (15) and a second decision unit (18) are set up, wherein at least the second decision unit (18) is connected to the first decision unit (14) via a communication channel, in particular a cloud system (19), via which feedback information (R) can be transmitted from the second decision unit (18) to the first decision unit (14) in order to influence a classification function (KF) of the first decision unit (14).

13. System according to claim 12, characterized in that at least the first decision unit (14) and / or the second decision unit (18) has a Kl module comprising artificial intelligence and / or that the first decision unit (14) is computer-numerically operable and that the second decision unit (18) is computer-numerically operable and / or manually operable by a person.

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