Patent Publication Number: US-2022214671-A1

Title: Method for analysing quality deficiencies

Description:
RELATED APPLICATIONS 
     This application is a national Phase of international application No. PCT/DE2020/100357, filed on Apr. 29, 2020, and claims the benefit of German application No. 10 2019 112 099.3, filed on May 9, 2019, and German application No. 10 2019 206 833.2, filed on May 10, 2019, all of which are incorporated herein by reference in their entireties and for all purposes. 
    
    
     FIELD OF DISCLOSURE 
     The disclosure relates to a method for analysing quality deficiencies of workpieces, preferably of vehicle bodies and/or of vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants. 
     BACKGROUND 
     When purchasing motor vehicles, buyers expect a high quality with regard to the paintwork of the motor vehicle. The buyers expect, for example, that the vehicle body and/or the vehicle attachment parts are free of any painting faults, i.e., for example, do not have any dirt inclusions, craters, runs, scratches and/or air inclusions (bubbles). The buyers also expect a uniform colour gradient, hue, gloss level and/or a uniform shine over the vehicle body. An at least approximately uniform structure of the various coatings and/or paint layers and/or an at least approximately uniform layer thickness of the coatings and/or paint layers is also desired. In addition, the paintwork of a vehicle body and/or of vehicle attachment parts should also be resistant to external influences, for example to UV radiation, heat, cold, rain, road salt, stone chips, bird droppings, dust and/or scratches from car washes. 
     In order to be able to meet these high expectations, elaborate quality control checks are carried out on the vehicle bodies and, if necessary, reworking is carried out if defects are detected. In a painting process, a new coating and/or paint layer is applied in each process step, so that quality deficiencies in lower layers are not identifiable or are only identifiable with difficulty during the quality control check. In order to be able to ensure the highest possible quality of the paintwork, a quality control check would therefore be necessary after the application of each individual coating and/or paint layer. However, this is not possible due to the very quick production cycle in a painting plant. 
     SUMMARY 
     An object underlying the disclosure is that of providing a method for analysing quality deficiencies in workpieces, preferably in vehicle bodies and/or vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, by means of which method quality deficiencies can be avoided and/or by means of which method quality deficiency causes in the production process can be determined, avoided and/or remedied. 
     According to examples disclosed herein, this object is achieved by a method for analysing quality deficiencies of workpieces, preferably of vehicle bodies and/or of vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, said method having the features of claim  1 . 
     The method preferably comprises the following steps:
         creating a workpiece-specific data set, uniquely assigned to a workpiece, at the start of a production process, in particular at the start of a painting process and/or creating a workpiece-carrier-specific data set, uniquely assigned to a workpiece carrier, at the start of a production process, in particular at the start of a painting process;   supplementing the workpiece-specific data set while a workpiece is passing through the production process, in particular the painting process, with in particular quality-relevant process data and/or supplementing the workpiece-carrier-specific data set while a workpiece carrier is passing through the production process, in particular the painting process, with in particular quality-relevant process data;   storing the workpiece-specific data set in a database and/or storing the workpiece-carrier-specific data set in a database.       

     The term “in particular” is used in the context of this description and the appended claims exclusively to describe possible discretionary and/or optional features. 
     In the context of this description and the appended claims, a workpiece-specific data set is understood to mean, in particular, a data set that is uniquely assigned to a workpiece. 
     The workpiece-specific data set forms in particular a “digital workpiece” and/or a “digital image” of a particular workpiece. 
     In the context of this description and the appended claims, a workpiece- carrier-specific data set is understood to mean in particular a data set which is uniquely assigned to a workpiece carrier and in particular to all workpieces arranged on the workpiece carrier, for example a workpiece carrier and vehicle attachment parts arranged thereon. 
     In particular, it is conceivable that a plurality of workpieces, especially vehicle attachment parts, are arranged on a workpiece carrier. 
     Such a workpiece carrier is in particular a so-called skid. 
     For example, it is conceivable that the workpiece-specific data set and/or the workpiece-carrier-specific data set is created automatically at the start of the production process, in particular at the start of the painting process, for example by reading order data from an RFID chip arranged on a workpiece. 
     The workpiece-specific data set and/or the workpiece-carrier-specific data set are in particular created automatically. 
     In particular, it is conceivable that the workpiece-specific and/or the workpiece-carrier-specific data set is created in each case from one or more data sets from upstream process steps. 
     For example, it is conceivable that during the creation of the workpiece-specific data set, body-in-white quality data from the body-in-white of a motor vehicle manufacturing process are added to the workpiece-specific data set. 
     It can also be favourable if the workpiece-specific data set and/or the workpiece-carrier-specific data set are supplemented subsequently, for example, by means of paint quality data, which are first determined in a laboratory, for example. 
     The workpiece-specific data set and/or the workpiece-carrier-specific data set preferably comprises a workpiece-specific order data set and/or a workpiece-carrier-specific order data set, for example a unique workpiece identification number, a model type of the particular workpiece and/or a colour code for a colour to be applied to the workpiece in a painting process. The workpiece-specific order data set and/or the workpiece-carrier-specific order data set further comprises, for example, information about a production shift during which a workpiece and/or a workpiece carrier passes through the production process. 
     The workpiece-specific data set and/or the workpiece-carrier-specific data set preferably further comprises a production data set which comprises the process data. 
     The in particular quality-relevant process data preferably include physical or chemical influence parameters for each coating and/or paint layer applied to a workpiece in the production process. 
     It can also be favourable if the in particular quality-relevant process data comprise body-in-white quality data, for example body-in-white quality data which include information about a deformation of a workpiece and/or about a surface roughness of a workpiece. 
     The production process, in particular the painting process, preferably comprises a plurality of successive process steps. 
     In one embodiment of the method, it is provided that the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented with quality data containing information about quality deficiencies, in particular paint defects, of the particular workpiece, preferably at the end of the production process, in particular at the end of the painting process. 
     Preferably, the quality data are determined by a quality inspector as part of a quality control check at a checking station at the end of the production process, for example by means of a visual check, by means of an automatic quality measuring station and/or by means of an automatic fault detection system. 
     The quality data with which the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented contain in particular information about the position, type, size and/or number of quality deficiencies. 
     A position of a quality deficiency can be determined, for example, from coordinates of an automatic quality measuring station and/or an automatic fault detection system. 
     A position of a quality deficiency can, for example, also be determined by a quality inspector and, in particular, can be entered manually via grid coordinates. 
     Quality deficiencies, in particular paint defects, include, for example, painting faults, in particular dirt inclusions, craters, runs and/or air inclusions (bubbles), a different layer thickness, an undesired colour gradient and/or deviations from a predefined treatment result parameter. 
     Treatment result parameters include, for example, the following: a thickness of a coating; a flatness of a coating; a uniformity of a layer thickness of a coating; a colour and/or brightness of a coating; a hardness of a coating; a chemical composition of a coating, in particular a degree of cross-linking and/or a solvent content, and/or a degree of contamination of a coating. 
     The quality data with which the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented also preferably contain information about a target status of a workpiece, i.e. whether a workpiece must be reworked, which reworking must be performed, whether no reworking is necessary and/or whether reworking is impossible. For example, one or more of the following reworking steps are conceivable as reworking: polishing, spot repair and/or repeat of the production process, in particular the painting process. 
     The quality data with which the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented preferably contain information about whether reworking has taken place. 
     It is also conceivable that the quality data with which the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented contain information about whether reworking is not advisable. Workpieces for which reworking is not advisable are, in particular, rejects. 
     In one embodiment of the method, it is provided that quality data from a plurality of workpiece-specific data sets and/or from a plurality of workpiece-carrier-specific data sets are automatically classified into different quality deficiency clusters by means of a cluster method for the detection of systematic quality deficiencies. 
     In the cluster method, the quality data of the plurality of workpiece-specific data sets and/or the plurality of workpiece-carrier-specific data sets are preferably classified by means of one or more classification rules. 
     Preferably, quality data from workpiece-specific data sets and/or from workpiece-carrier-specific data sets are classified into systematic and non-systematic quality deficiency clusters. 
     In particular, for example, quality deficiencies with comparable position, type, size and/or number are classified into a quality deficiency cluster. 
     Furthermore, it is conceivable, for example, that quality deficiencies are classified into a quality deficiency cluster according to the time of occurrence. 
     Preferably, classification rules defined by an expert and/or learned classification rules are used to classify the quality data. 
     A classification rule defined by an expert comprises, for example, a defined number of workpieces having identical and/or similar quality deficiencies within a sliding window of a defined number of workpieces and/or workpiece carriers passing through the production process. 
     For example, it is conceivable that quality data are classified into a systematic quality deficiency cluster if identical and/or comparable quality deficiencies are detected on three or more than three workpieces within five consecutive workpieces in the production process. 
     In the context of this description and the appended claims, identical quality deficiencies are understood to mean, in particular, that the type, size, severity and/or position of the quality deficiencies is identical. 
     In the context of this description and the appended claims, similar quality deficiencies are understood to mean in particular that the size and/or position of the quality deficiencies may differ from one another, but that the type of quality deficiency is identical. 
     Learned classification rules are preferably learned by means of a machine-learning method from quality data of workpiece-specific data sets and/or from workpiece-carrier-specific data sets. 
     For example, it is conceivable that classification rules are learned by means of a supervised and/or unsupervised machine-learning method. 
     In a supervised machine-learning method, quality data of workpiece-specific data sets and/or of workpiece-carrier-specific data sets are divided into systematic and non-systematic quality deficiency clusters. The classification is preferably carried out by means of defined rules and/or user feedback. Supervised machine-learning methods can preferably also be used to identify quality trends or future quality deficiency clusters by means of suitable “labelling”. In an unsupervised machine-learning method, quality data are preferably learned from workpiece-specific data sets of workpieces without a quality deficiency and/or from workpiece-carrier-specific data sets of workpiece carriers of which the workpieces do not have a quality deficiency, i.e. a normal state. If deviations from the normal state occur, these deviations are detected. 
     Defined classification rules and/or learned classification rules are preferably also transferable to other industrial-method plants, in particular to other painting plants. 
     The classification of the quality data by means of the cluster method is preferably carried out continuously. Alternatively or additionally, it is conceivable that the classification of the quality data by means of the cluster method is carried out by batch processing. 
     A systematic quality deficiency cluster is preferably further determined by signal processing methods. 
     For example, it is conceivable that in order to determine a systematic quality deficiency cluster, features from the frequency density function of the quality data are processed by means of one or more signal processing methods, for example by means of Fourier transformation and/or by means of density estimation. 
     It can be favourable if the systemic quality deficiencies detected by means of the cluster method are displayed to a user by means of a visualisation, for example as a real-time message in an alarm system and/or as an analysis message in an analysis system. 
     For example, it is conceivable that workpieces are ejected from the production process based on the quality deficiency clusters detected by means of the cluster method. It can also be favourable if a process control system of the production process is adapted on the basis of quality deficiency clusters detected by the cluster method. 
     In one embodiment of the method, it is provided that, by means of an analysis procedure, quality deficiency causes for the systematic quality deficiencies detected by means of the cluster method and classified into different quality deficiency clusters are automatically detected, preferably by analysis of the in particular quality-relevant process data of the workpiece-specific data sets of the workpieces with systematic quality deficiencies of a particular quality deficiency cluster and/or by analysis of the in particular quality-relevant process data of the workpiece-carrier-specific data sets of the workpiece carriers for which the workpieces have systematic quality deficiencies of a particular quality deficiency cluster. 
     Preferably, systematic quality deficiencies that cannot be detected within the scope of the quality control check can be identified by drawing conclusions from the production process. 
     In the analysis procedure, quality deficiency causes are determined by means of one or more analysis rules. 
     Preferably, analysis rules defined by an expert and/or learned analysis rules are used to determine the quality deficiency causes 
     Learned analysis rules are preferably learned by means of a machine-learning method. 
     For example, it is conceivable that analysis rules are learned by means of a supervised and/or unsupervised machine-learning method. 
     In a supervised machine-learning method, analysis rules are learned by means of user feedback regarding the quality deficiency cause. In an unsupervised machine-learning method, a normal state of process steps of the production process is preferably learned. If deviations from the learned normal state occur, these deviations are detected. 
     Defined analysis rules and/or learned analysis rules are preferably also transferable to other industrial-method plants, in particular to other painting plants. 
     In one embodiment of the method, it is provided that the detected quality deficiency causes include anomalies and/or deviations in the production process, in particular in the painting process. 
     In one embodiment of the method, it is provided that correlations between the detected quality deficiency causes and the systematic quality deficiencies classified into different quality deficiency clusters are determined by means of the analysis procedure. 
     Correlations between the detected quality deficiency causes and the systematic quality deficiencies classified into different quality deficiency clusters are preferably determined by the defined and/or learned analysis rules. 
     Correlations between the detected quality deficiency causes and the systematic quality deficiencies classified into different quality deficiency clusters are preferably also transferable to other industrial-method plants, especially painting plants. 
     In one embodiment of the method, it is provided that imminent systematic quality deficiencies are automatically determined by means of the determined correlations, preferably by automatically inferring future quality deficiencies of a workpiece from one or more anomalies and/or deviations determined in the ongoing production process by means of the correlations determined by means of the analysis procedure, while a workpiece is passing through the production process. 
     Preferably, by means of the analysis procedure, the workpiece is already assigned to a quality deficiency cluster as it passes through the production process. 
     In one embodiment of the method, it is provided that the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented in each individual process step with in particular quality-relevant process data. 
     In one embodiment of the method, it is provided that one or more of the following process parameters are used as in particular quality-relevant process data by means of which a workpiece-specific data set and/or a workpiece-carrier-specific data set is supplemented:
         target-time overruns in process steps of the production process;   events occurring during the run-through of a process step of the production process;   body-in-white quality data of a particular workpiece;   anomalies and/or deviations in the production process;   weather data;   personal data for persons who were involved in a process step of the production process when it was run through.       

     It can be favourable if one or more of the following process parameters are used to supplement a particular workpiece-specific data set and/or a particular workpiece-carrier-specific data set:
         nozzle temperature in a dryer;   paint quantity;   paint colour;   paint batch;   trajectories of robot paths;   booth temperature in paint booths;   booth humidity in paint booths;   bath temperatures and fill level in a cathodic dip painting station and in a pre-treatment station;   conveying movements of a workpiece through a cathodic dip painting station;   a concentration of chemicals in a cathodic dip painting station;   an electrical voltage in a cathodic dip painting station;   temperatures and cycle times in a dryer.       

     Process parameters used to supplement a particular workpiece-specific data set and/or a particular workpiece-carrier-specific data set are preferably pre-processed. 
     In particular, it is conceivable that process parameters with high temporal variability are pre-processed, for example mechanical, hydraulic or electrical process parameters. However, pre-processing of process parameters with low temporal variability, i.e., sluggish process parameters, for example thermodynamic process parameters, can be omitted. 
     In the context of this description and the appended claims, a target-time overrun is understood to mean in particular that the target time specified in a process step is exceeded when the particular process step is carried out for a particular workpiece. 
     Events include, for example, maintenance events, alarms from a control system of the industrial-method plant, information about shift changes, information about production stops, information about access to the treatment stations of the industrial-method plant, and information about cleaning interruptions. 
     Using the maintenance events added to the workpiece-specific data sets and/or using the maintenance events added to the workpiece-carrier-specific data sets, it is possible, for example, to identify systematic quality deficiencies that occur after maintenance. 
     Body-in-white quality data include in particular information about a sheet metal quality of a workpiece or parts of a workpiece, for example bonnets, doors, side parts and/or a roof. 
     Preferably, one or more of the following process parameters are also used to supplement a particular workpiece-specific data set and/or a particular workpiece-carrier-specific data set:
         conveyor information   holding times of a workpiece in the production process;   “overnight storage” of a workpiece within the industrial-method plant.       

     For example, the holding time of a workpiece in a dryer is used to supplement a corresponding workpiece-specific data set. In particular, quality deficiencies can be inferred from a target-time overrun for the presence of the workpiece in a dryer. It can also be favourable if the holding time of a workpiece carrier in a dryer is used to supplement a corresponding workpiece-carrier-specific data set. 
     In the context of this description and the appended claims, deviations in the production process are preferably understood to mean a deviation of states from a target state or a deviation of a measured process value from a predefined process value. 
     Anomalies are preferably determined automatically from deviations between actual process values and target process windows in the process steps of the production process. Alternatively or additionally, it is conceivable that anomalies are automatically determined from deviations between actual process values of a process step of the production process and a normal state for the particular process step learned by means of a machine-learning method. 
     Anomalies in the production process are preferably also determined by means of one or more subordinate microsystem analysis systems, in particular subdivided to a micro level, i.e., to a workpiece level, and/or to a macro level, i.e., to a level of the industrial-method plant. 
     In one embodiment of the method, it is provided that the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented with process fault data containing information about an anomaly and/or deviation in a process step of the production process while a workpiece and/or a workpiece carrier is passing through the process step of the production process. 
     The process fault data are in particular “fault markers”. 
     Preferably, not only workpiece-specific data sets of workpieces that were arranged in a specific treatment station during the production and/or conveying stop are supplemented with process fault data. Rather, it is conceivable that workpiece-specific data sets of other workpieces, which were also arranged in a specific treatment station for a longer period of time due to the production and/or conveying stop, are also supplemented with process fault data. 
     Preferably, furthermore, not only workpiece-carrier-specific data sets of workpiece carriers that were located in a specific treatment station during the production and/or conveying stop are supplemented with process fault data. In particular, it is conceivable that workpiece-carrier-specific data sets of further workpiece carriers, which were likewise arranged in a specific treatment station for a longer period of time due to the production and/or conveying stop, are also supplemented with process fault data. 
     By means of the process fault data, anomalies and/or deviations in a process step of the production process are preferably identifiable as faults. 
     Preferably, the use of process fault data to identify anomalies and/or deviations in the workpiece-specific data set and/or in the workpiece-carrier-specific data set makes it possible to reduce a size of the workpiece-specific data set and/or a size of the workpiece-carrier-specific data set. 
     Preferably, anomalies and/or deviations in a process step of the production process are identifiable as quality deficiency causes by means of the process fault data. 
     Anomalies and/or deviations in a process step of the production process can be associated with a systemic quality deficiency, classified by means of a cluster method, by means of the process fault data, preferably in an automated manner, in particular by means of an analysis procedure. 
     In one embodiment of the method, it is provided that the method is carried out in an industrial-method plant, in particular in a painting plant, which comprises a plurality of treatment stations which are different from one another and in each of which one or more process steps of the production process, in particular of the painting process, can be carried out. 
     Preferably, one or more in particular quality-relevant process data items are recorded in each treatment station for each process step of the production process. 
     In one embodiment of the method, it is provided that the workpiece-specific data set and/or the workpiece-carrier-specific data set is continuously or discontinuously supplemented with the process data, in particular the quality-relevant process data. 
     In one embodiment of the method, it is provided that the workpiece-specific data set and/or the workpiece-carrier-specific data set is supplemented in each case with in particular quality-relevant process data, each of which data items comprises a time stamp by means of which the particular process data can be uniquely associated with a time and/or a process step of the production process. 
     In one embodiment of the method, it is provided that the supplemented workpiece-specific data set of a workpiece and/or the supplemented workpiece-carrier-specific data set of a workpiece carrier is stored in a database during and/or after the passing of a workpiece and/or a workpiece carrier through the production process. 
     The disclosure further relates to a quality analysis system for analysing quality deficiencies of workpieces, preferably of vehicle bodies and/or of vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, in particular after and/or whilst passing through a painting process in painting plants. 
     The disclosure is based on the further object of providing a quality analysis system for analysing quality deficiencies of workpieces, preferably of vehicle bodies and/or vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, by means of which system quality deficiencies can be avoided and/or by means of which system quality deficiency causes in the production process can be determined, avoided and/or remedied. 
     This object is achieved in accordance with examples disclosed herein by a quality analysis system for analysing quality deficiencies of workpieces, preferably of vehicle bodies and/or of vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, having the features of claim  15 . 
     The quality analysis system preferably comprises the following:
         an interface for communication with a control system of an industrial-method plant, in particular a painting plant;   a control device which is set up and configured in such a way that the method in accordance with claims  1  to  14  can be carried out by means of the control device.       

     The disclosure further relates to an industrial-method plant, in particular a painting plant, which comprises the following:
         a control system by means of which a production process, in particular a painting process, can be controlled;   a quality analysis system according to examples disclosed herein.       

     The industrial-method plant preferably comprises a plurality of treatment stations, in particular a plurality of painting stations. 
     For example, it is conceivable that the painting plant comprises a painting line with a plurality of interlinked treatment stations. 
     Alternatively or in addition, it is conceivable that the painting plant comprises a plurality of treatment boxes, wherein each treatment box comprises one or more treatment stations. 
     The painting plant preferably comprises one or more of the following treatment stations:
         pre-treatment station;   cathodic dip painting station;   dryer after the cathodic dip painting station;   primer booth;   primer dryer;   base coat booth;   base coat dryer;   clear coat booth;   clear coat dryer.       

     Further features and/or advantages of examples disclosed herein are the subject of the following description and the illustration in the drawings of embodiment examples. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  a schematic illustration of an industrial-method plant, in particular a painting plant; 
         FIG. 2  a schematic illustration of an embodiment of workpiece-specific data sets of a plurality of workpieces; 
         FIG. 3  a schematic illustration of a cluster method for classifying quality data of the workpiece-specific data sets from  FIG. 2 ; 
         FIG. 4  a schematic illustration of a further embodiment of workpiece-specific data sets of a plurality of workpieces; 
         FIG. 5  a schematic illustration of a cluster method for classifying quality data of the workpiece-specific data sets from  FIG. 4 ; 
         FIG. 6  a schematic illustration of a further embodiment of workpiece-specific data sets of a plurality of workpieces; 
         FIG. 7  a schematic illustration of a cluster method for classifying quality data of the workpiece-specific data sets from  FIG. 6 ; and 
         FIG. 8  a schematic illustration of a visualisation of systematic quality deficiencies detected by means of a cluster method. 
     
    
    
     Like or functionally like elements are denoted by the same reference signs in all figures. 
     DETAILED DESCRIPTION OF THE DRAWINGS 
       FIG. 1  shows a industrial-method plant denoted as a whole as  100 . 
     The industrial-method plant  100  is in particular a painting plant  102 . 
     The industrial-method plant  100  preferably comprises a plurality of treatment stations  104 . 
     The industrial-method plant  100 , in particular the painting plant  102 , comprises seven treatment stations  104 . 
     Preferably, a production process on workpieces  106 , in particular on vehicle bodies  108  and/or on vehicle attachment parts, can be carried out by means of the industrial-method plant  100 . 
     In particular, in each case one or more process steps of the production process can be carried out in the treatment stations  104 . 
     The industrial-method plant  100  shown in  FIG. 1 , in particular the painting plant  102 , is in particular a painting line  110  in the present case. 
     In the painting line  110 , the treatment stations  104  are preferably interlinked. 
     For example, it is conceivable that a workpiece  106  and/or a workpiece carrier passes through the following treatment stations  104  in the specified order. 
     It is conceivable, for example, that only a single workpiece  106 , for example a vehicle body  108 , is arranged on a workpiece carrier (not shown in the drawing) while the workpiece  106  passes through the treatment stations  104 . 
     Alternatively or additionally, it is conceivable that a plurality of workpieces  106 , for example vehicle attachment parts not shown in the drawing, are arranged together on a workpiece carrier while the workpiece carrier with the workpieces  106  arranged thereon passes through the treatment stations  104 . 
     A workpiece  106 , for example a vehicle body  108 , is pre-treated in a pre-treatment station  112  and conveyed from the pre-treatment station  112  to a cathodic dip painting station  114 . 
     From the cathodic dip coating station  114 , the workpiece  106  is conveyed to a dryer  116  downstream of the cathodic dip coating station  114  after a coating has been applied to the workpiece. 
     After drying the coating applied to the workpiece  106  in the cathodic dip coating station  114  in the dryer  116 , the workpiece  106  is preferably conveyed to a base coat booth  118 , in which a coating is again applied to the workpiece  106 . 
     After the coating has been applied in the base coat booth  118 , the workpiece  106  is preferably conveyed into a base coat dryer  120 . 
     After drying the coating applied to the workpiece  106  in the base coat booth  118  in the base coat dryer  120 , the workpiece  106  is preferably conveyed to a clear coat booth  122 , in which a further coating is applied to the workpiece  106 . 
     After application of the coating in the clear coat booth  122 , the workpiece  106  is preferably supplied to a clear coat dryer  124 . 
     After drying the coating applied to the workpiece  106  in the clear coat booth  122  in the clear coat dryer  124 , the workpiece  106  is preferably supplied to a checking station  126  at the end of the production process. 
     In the checking station  126 , a quality control check is preferably carried out by a quality inspector, for example by means of a visual check. 
     The industrial-method plant  100 , in particular the painting plant  102 , preferably further comprises a control system  128  by means of which the production process, in particular the painting process, in the treatment stations  104  can be controlled. 
     It may further be advantageous if the industrial-method plant  100 , in particular the painting plant  102 , comprises a quality analysis system  130 . 
     The quality analysis system  130  preferably comprises an interface  132  for communication with the control system  128  of the industrial-method plant  100 , in particular the painting plant  102 . 
     The quality analysis system  130  is preferably configured to analyse quality deficiencies of the workpieces  106 , in particular of the vehicle body  108  and/or vehicle attachment parts. 
     Preferably, a workpiece-specific data set  134  uniquely assigned to a workpiece  106  is created by means of the control system  128  and/or by means of the quality analysis system  130  at the start of the production process, in particular at the start of the painting process. 
     Workpiece-specific data sets  134  of workpieces  106  preferably each form a “digital workpiece” and/or a “digital image” of a particular workpiece  106 . 
     At the start of the production process, in particular at the start of the painting process, the workpiece-specific data set  134  is preferably created automatically, for example by reading in order data  136  by means of an RFID chip (not shown in the drawing) arranged, for example, on a workpiece  106 . 
     The workpiece-specific data set  134  thus preferably comprises a workpiece-specific order data set  138 , for example a unique workpiece identification number, a model type of the particular workpiece  106  and/or a colour code for a paint to be applied to the workpiece  106  in a painting process. 
     It can also be favourable if a workpiece-specific data set  134  is supplemented with in particular quality-relevant process data  140  while a workpiece  106  is passing through the production process, in particular the painting process. 
     A corresponding workpiece-specific data set  134  is preferably stored in a database  142 . In particular, the supplemented workpiece-specific data set  134  of a workpiece  106  is stored in the database  142  during and/or after the passing of a workpiece  106  through the production process. 
     A workpiece-specific data set  134  preferably further comprises, through the addition of process data  140 , a production data set  144  comprising the process data  140 . 
     It can be favourable if the workpiece-specific data set  134  is supplemented in each individual process step of the production process with, in particular, quality-relevant process data  140 . 
     Each process data item  140 , in particular each quality-relevant process data item  140 , with which a particular workpiece-specific data set  134  of a workpiece  106  is supplemented, preferably comprises a time stamp by means of which the particular process data can be uniquely associated with a time and/or a process step of the production process. 
     The in particular quality-relevant process data  140  preferably comprise physical or chemical influence parameters for each coating and/or paint layer applied to a workpiece  106  in the production process. 
     The workpiece-specific data set  134  of a particular workpiece  106  is preferably supplemented with quality data  146  containing information about quality deficiencies, in particular about paint defects, of the particular workpiece  106 . 
     In particular, it can be provided that the workpiece-specific data set  134  is supplemented with the quality data  146  at the end of the production process, in particular at the end of the painting process. 
     The quality data  146  are preferably determined by a quality inspector as part of a quality control check at the checking station  126 , for example by means of a visual check. 
     For example, it is conceivable that the quality data  146  contains information about the position, type, size and/or number of quality deficiencies of a particular workpiece  106 . 
     Quality deficiencies, in particular paint defects, include, for example, painting faults, in particular dirt inclusions, craters, runs and/or air inclusions (bubbles), a different layer thickness, an undesired colour gradient and/or deviations from a predefined treatment result parameter. 
     Treatment result parameters include, for example, the following: a thickness of a coating; a flatness of a coating; a uniformity of a layer thickness of a coating; a colour and/or brightness of a coating; a hardness of a coating; a chemical composition of a coating, in particular a degree of cross-linking and/or a solvent content, and/or a degree of contamination of a coating. 
     The quality data  146  with which the workpiece-specific data set  134  of a particular workpiece  106  is supplemented preferably further contain information about a target status of a particular workpiece  106 . 
     Preferably, the quality data  146  contain information on whether a workpiece  106  needs to be reworked, what reworking needs to be performed, whether no reworking is necessary and/or whether reworking is impossible. 
     For example, one or more of the following reworking steps are conceivable as reworking: polishing, spot repair and/or repeat of the production process, in particular the painting process. 
     One or more of the following process parameters are preferably used as process data  140 , in particular as quality-relevant process data  140 , by means of which a workpiece-specific data set  134  is supplemented:
         target-time overruns in process steps of the production process;   events occurring during the run-through of a process step of the production process;   body-in-white quality data of a particular workpiece;   anomalies and/or deviations in the production process;   weather data;   personal data for persons who were involved in a process step of the production process when it was run through.       

     Body-in-white quality data include, in particular, information about a sheet metal quality of a workpiece  106  or of parts of a workpiece  106 , for example of bonnets, doors, side parts and/or a roof. 
     It may be favourable if one or more of the following process parameters are used to supplement a particular workpiece-specific data set  134 :
         nozzle temperature in a dryer  116 ,  120 ,  124 ;   paint quantity;   paint colour;   paint batch;   trajectories of robot paths;   booth temperatures in the paint booths  118 ,  122 ;   booth humidity in the paint booths  118 ,  122 ;   bath temperatures and fill level in the cathodic dip painting station  114  and in the pre-treatment station  112 ;   conveying movements of a workpiece through the cathodic dip painting station  114 ;   a concentration of chemicals in the cathodic dip painting station  114 ;   an electrical voltage in the cathodic dip painting station  114 ;   temperatures and cycle times in a dryer  116 ,  120 ,  124 .       

     Process parameters used to supplement a particular workpiece-specific data set  134  are preferably pre-processed. 
     In particular, it is conceivable that process parameters with high temporal variability are pre-processed, for example mechanical, hydraulic or electrical process parameters. However, pre-processing of process parameters with low temporal variability, i.e., sluggish process parameters, for example thermodynamic process parameters, can preferably be omitted. 
     Events include, for example, maintenance events, alarms from the control system  128  of the industrial-method plant  100 , information about shift changes, information about production stops, information about access to the treatment stations  104  of the industrial-method plant  100 , and information about cleaning interruptions. 
     Using the maintenance events added to the workpiece-specific data sets  134 , it is possible, for example, to identify systematic quality deficiencies occurring after maintenance. 
     Preferably, one or more of the following process parameters are further used to supplement a particular workpiece-specific data set  134 :
         conveyor information   holding times of a workpiece  106  in the production process;   “overnight storage” of a workpiece  106  within the industrial-method plant  100 , for example within a dryer  116 ,  120 ,  124 .       

     For example, the holding time of a workpiece  106  in a dryer  116 ,  120 ,  124  is used to supplement a particular workpiece-specific data set  134 . In particular, quality deficiencies can be inferred from a target-time overrun for the presence of the workpiece  106  in a dryer  116 ,  120 ,  124 . 
     Anomalies are preferably determined automatically from deviations between actual process values and target process windows in the process steps of the production process. The actual process values are determined, for example, by means of one or more sensors (not shown in the drawing) in the treatment stations  106 . 
     Alternatively or additionally, it is conceivable that anomalies are automatically determined from deviations between actual process values of a process step of the production process and a normal state for the particular process step learned by means of a machine-learning method. 
     Preferably, the workpiece-specific data set  134  is supplemented with process fault data  148  in the event of anomalies and/or deviations in a process step of the production process, in particular while a workpiece  106  is passing through the particular process step of the production process. 
     The process fault data  148  preferably contain information about an anomaly and/or a deviation in a process step of the production process. 
     In particular, the process fault data  148  are “fault markers”. For example, the process fault data  148  can be used to mark the presence or absence of anomalies and/or deviations in a particular process step as a workpiece  106  passes through the process step. 
     By means of the process fault data  148 , anomalies and/or deviations in a particular process step of the production process are preferably identifiable as faults. 
     Preferably, by using process fault data  148  to identify anomalies and/or deviations in the process data  140  of the workpiece-specific data set  134 , a size of the workpiece-specific data set  134  can be reduced. 
       FIG. 2  shows an embodiment of workpiece-specific data sets  134  of various workpieces  106 . 
     In the first column, for example, the order data  136  of a particular workpiece-specific data set  134  is shown, for example in the form of a colour code and/or in the form of a workpiece identification number. 
     Columns two to seven each show process data  140  of a particular workpiece-specific data set  134  from the process steps of the production process in the treatment stations  104  of the industrial-method plant  100 , in particular the painting plant  102 . 
     By means of an exclamation mark, in particular process fault data  148 , i.e. “fault markers”, are shown, which contain information about anomalies and/or deviations in process steps of the production process. 
     Column nine also shows quality data  146 , which preferably contain information about the position, type, size and/or number of quality deficiencies. 
     For example, an anomaly and/or deviation has been stored in the pre-treatment station  112  as process fault data  148  for the workpiece W 1 . 
     For example, for workpieces W 3  to W 5 , an anomaly and/or deviation was detected in the dryer  116  after the cathodic dip painting station  114  and was marked by process fault data  148 . 
     For example, for workpiece W 8 , an anomaly and/or deviation in the base coat dryer  120  was further marked using process fault data  148 . 
     The quality data  146 , which include information about quality deficiencies detected in the checking station  126 , are stored in the ninth column, for example. For example, the quality data in the present case contain information about the type of quality deficiencies  146 . 
     Preferably, quality data  146  from a plurality of workpiece-specific data sets  134  are automatically classified into different quality deficiency clusters  150  by means of a cluster method for detecting systematic quality deficiencies by means of the quality analysis system  130 . 
     In the cluster method, the quality data  146  of the workpiece-specific data sets  134  are preferably classified by means of one or more classification rules. 
     It can be favourable if the quality data  146  of the workpiece-specific data sets  134  are classified into systematic and non-systematic quality deficiency clusters  150 . 
     Preferably, classification rules defined by an expert and/or learned classification rules are used to classify the quality data  146 . 
     A classification rule defined by an expert comprises, for example, a defined number of workpieces  106  with identical and/or similar quality deficiencies within a sliding window of a defined number of workpieces  106  passing through the production process. 
     In the present case, quality data  146  of workpiece-specific data sets  134  are classified into a systematic quality deficiency cluster  150  if identical and/or comparable quality deficiencies are detected on three or more than three workpieces  106  within five consecutive workpieces  106  in the production process. 
     Learned classification rules are preferably learned by means of a machine-learning method from quality data  146  of workpiece-specific data sets  134 . 
     Classification rules can be learned here by means of a supervised and/or unsupervised machine-learning method. 
     Preferably, the classification of the quality data  146  by means of the cluster method is performed continuously. Alternatively or additionally, it is possible that the classification of the quality data  146  by means of the cluster method is carried out by batch processing. 
     Preferably, systematic quality deficiencies detected by means of the cluster method are displayed to a user by means of a visualisation  152  of the quality analysis system  130  shown in  FIG. 8 , for example as a real-time message in an alarm system and/or as an analysis message in an analysis system. 
     In the visualisation  152  shown in  FIG. 8 , all quality deficiencies of the quality deficiency cluster  150  are located at a side door on the left side of the workpiece  106 , in particular the vehicle body  108 . 
       FIG. 3  shows the implementation of the cluster method for the workpiece-specific data sets  134  shown in  FIG. 2 . 
     At the times T 1  and T 2 , the quality analysis system  130  has not yet detected a quality deficiency cluster  150  with systematic quality deficiencies. 
     At the time T 3 , a quality deficiency cluster  150  is detected by means of the quality analysis system  130 , since the quality data  146  of the workpiece-specific data sets  134  of the workpieces W 3 , W 4  and W 7  each contain the identical quality deficiency F 1 . 
     For example, it is conceivable that workpieces  106  are ejected from the production process on the basis of the quality deficiency clusters  150  detected by the cluster method. Alternatively or additionally, it is possible that a process control of the production process by means of the control system  128  is adjusted on the basis of quality deficiency clusters  150  detected by means of the cluster method. 
     Preferably, an analysis procedure is used to automatically determine quality deficiency causes for the systematic quality deficiencies detected by means of the cluster method and classified into different quality deficiency clusters  150 . Preferably, the in particular quality-relevant process data  140  of the workpiece-specific data sets  134  of the workpieces W 3 , W 4  and W 7  are analysed with systematic quality deficiencies of the quality deficiency cluster  150 . 
     In the analysis procedure, quality deficiency causes are preferably determined by means of one or more analysis rules. 
     Preferably, analysis rules defined by an expert and/or learned analysis rules are used to determine the quality deficiency causes. 
     Learned analysis rules are preferably learned by means of a machine-learning method. For example, it is conceivable here that analysis rules are learned by means of a supervised and/or unsupervised machine-learning method. 
     Anomalies and/or deviations in a process step of the production process are preferably identifiable as a quality deficiency cause by means of the process fault data  148 . 
     Anomalies and/or deviations in a process step of the production process can be associated in an automated manner with a systemic quality deficiency classified by means of the cluster method, preferably by means of the process fault data  148 , in particular by means of the analysis method. 
     Preferably, correlations between the detected quality deficiency causes and the systematic quality deficiencies classified into different quality deficiency clusters  150  are determined by means of the analysis procedure. 
     For example, it is conceivable that an analysis rule defined by an expert is used which establishes a correlation between the anomalies in the dryer  116  after the cathodic dip painting station  114  and the quality deficiencies F 1  of the quality deficiency cluster  150 . 
     Preferably, in the visualisation  152  of the quality analysis system  130 , quality deficiency causes for the quality deficiency cluster  150  shown in  FIG. 8  can be displayed in a window  154  of the visualisation  152 . 
     The detected quality deficiency causes preferably include anomalies and/or deviations in the production process, especially in the painting process. 
     By means of the determined correlations, imminent systematic quality deficiencies are preferably automatically determined, preferably by automatically inferring future quality deficiencies of a workpiece  106  from one or more anomalies and/or deviations detected in the ongoing production process by means of the correlations determined by means of the analysis procedure, while a workpiece  106  is passing through the production process. 
     Preferably, the workpiece  106  is already associated with a quality deficiency cluster  150  by means of the analysis procedure as it passes through the production process. 
     Preferably, systematic quality deficiencies that are not recognisable within the scope of the quality control check in the checking station  126  can thus also be recognised by drawing conclusions from the production process. 
     An embodiment of workpiece-specific data sets  134  of different workpieces  106  shown in  FIG. 4  differs from the embodiment of workpiece-specific data sets  134  of different workpieces  106  shown in  FIG. 2  fundamentally in that the quality data  146  of the workpiece-specific data sets  134  of the workpieces W 6  to W 8  each contain information about the quality deficiency F 2 . 
     At the time T 4 , a quality deficiency cluster  150  is recognised by means of the cluster method using a classification rule, since the quality data  146  of the workpiece-specific data sets  134  of the workpieces W 6  to W 8  each contain the identical quality deficiency F 2  (see  FIG. 5 ). 
     The process data  140  of the workpiece-specific data sets  134  contain information about a booth temperature in the base coat booth  118  in the fifth column. 
     The booth temperature has already risen for workpieces W 4  and W 5 . It is conceivable that the booth temperature in the base coat booth  118  for workpieces W 6  to W 8  is outside a target process window defined for the booth temperature in the base coat booth  118 . 
     By means of the analysis procedure, the booth temperature in the base coat booth  118  is preferably identified as the quality deficiency cause. 
     For example, when performing the analysis procedure, an analysis rule defined by an expert is used which establishes a relationship between the booth temperature in the base coat booth  118  and the quality deficiencies F 2  of the quality deficiency cluster  150 . 
     The performance of the cluster method and the analysis procedure in the embodiment of the workpiece-specific data sets  134  shown in  FIGS. 4 and 5  is substantially the same as the performance of the cluster method and the analysis procedure in the embodiment of the workpiece-specific data sets  134  shown in  FIGS. 2 and 3 , so that in this regard reference is made to the foregoing description thereof. 
     An embodiment of workpiece-specific data sets  134  of different workpieces  106  shown in  FIG. 6  differs from the embodiment of workpiece-specific data sets  134  of different workpieces  106  shown in  FIG. 2  fundamentally in that the quality data  146  of the workpieces W 2  and W 3  in each case contain information about the quality deficiency F 1  and in that the quality data  146  of the workpieces W 4  to W 7  in each case contain information about the quality deficiency F 2 . 
     The quality deficiencies F 1  and F 2  are, for example, identical and/or similar and are already recognised as quality deficiency clusters  150  at the time T 1  by means of the cluster method using a classification rule (see  FIG. 7 ). 
     The process data  140  of the workpiece-specific data sets  134  contain, in the fourth column, information about a target-time overrun in the dryer  116  after the cathodic dip painting station  114 . 
     In particular, the workpieces W 2  and W 3 , which show the quality deficiency F 1 , are affected by this. 
     However, the workpieces W 4  to W 7  following the workpieces W 2  and W 3  are also affected by a target-time overrun in the dryer  116  after the cathodic dip painting station  114 . 
     By means of the analysis procedure, the target-time overrun in the dryer  116  after the cathodic dip painting station  114  is preferably identified as the quality deficiency cause for the quality deficiencies F 1  and F 2 , in particular for the quality deficiency cluster  150 . 
     The performance of the cluster method and the analysis procedure in the embodiment of the workpiece-specific data sets  134  shown in  FIGS. 6 and 7  is substantially the same as the performance of the cluster method and the analysis procedure in the embodiment of the workpiece-specific data sets  134  shown in  FIGS. 2 and 3 , so that in this regard reference is made to the foregoing description thereof. 
     The method steps described with reference to  FIGS. 1 to 8  can be carried out by means of the quality analysis system  130  not only for individual workpieces  106 . 
     In particular, it is conceivable that the method steps are also carried out by means of the quality analysis system  130  for a plurality of workpieces  106 , for example for a plurality of vehicle attachment parts not shown in the drawing, which are each arranged together on a workpiece carrier, while the workpiece carrier with the workpieces  106  arranged thereon passes through the treatment stations  104 . 
     Preferably, a workpiece-carrier-specific data set  134  is used instead of a workpiece-specific data set. 
     In particular, a workpiece-carrier-specific data set is uniquely assigned to a workpiece carrier and all workpieces  106  arranged on the particular workpiece carrier, for example a workpiece carrier and all vehicle attachment parts arranged thereon. 
     In all other respects, the method steps to be carried out by means of the quality analysis system  130  are the same as the method steps described above, so that reference is made to their description in this regard. 
     Overall, a method for analysing quality deficiencies of workpieces  106 , preferably of vehicle bodies  108  and/or of vehicle attachment parts, after or whilst passing through a production process in industrial-method plants  100 , preferably after or whilst passing through a painting process in painting plants  102 , can be provided, by means of which method quality deficiencies can be avoided and/or by means of which method quality deficiency causes in the production process can be determined, avoided and/or remedied. Furthermore, a quality analysis system  130  can be provided for performing such a method for analysing quality deficiencies of workpieces  106 .