FUNCTIONAL MODELING METHOD FOR MACHINED SURFACES AND ASSOCIATED MANUFACTURING MACHINE

Abstract

The invention relates to a manufacturing machine, computer program and storage implementing a method for identifying a quality defect implemented by means of a manufacturing machine, comprising the following steps: S1: acquire measurement data of selected operating parameters of the manufacturing machine via said sensors during a manufacturing range, said operating parameters comprising on the one hand coordinates in space (CE) of a plurality of points, and on the other hand at least one other operating parameter (Px) of the manufacturing machine, forming a set of acquired data (LDA); S2: classify the set of acquired data (LDA); S3: calculate at least one set of differential data (DDI) for each point of the classified data set (LDC); S4: generate at least one functional graphical model (MGF); and S5: identify the point of the associated functional graphical model (MGF) as having a quality defect.

Description

Title of the invention: MODELING METHOD FUNCTIONAL MACHINED SURFACE AND ASSOCIATED MANUFACTURING AND IMPLEMENTATION MACHINE Technical field of the invention

[0001] The invention relates to the general technical field of machines and processes for machining and material deposition for the manufacture of parts, and more particularly to the field of modeling of machined or deposited surfaces and its application in the detection of surface condition defects.

[0002] TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] Manufacturing machines are defined as any machine capable of removing or depositing material. Programmable machining centers typically have multiple degrees of freedom to allow for numerous types of movements and thus to machine more complex geometries without needing to reposition the workpiece during a manufacturing sequence.

[0004] Programmable material deposition machines can also have a plurality of degrees of freedom to allow many types of movements and thus allow material to be deposited according to more complex geometries without the need to reposition the part during a manufacturing range.

[0005] Such machines are used by entering a series of trajectory instructions in the form of a program into a machine control unit, thus serving to move one or more tools on the surface of a workpiece, or to move one or more deposition tools on the surface of a mold so as to form the surfaces defining the part to be produced.

[0006] Trajectory instructions generally include commands regarding the position, travel speed, cutting / deposition speed, and tool type that must be observed during manufacturing. The program, or manufacturing sequence, is established to perform a series of operations using the machine in order to generate a specific geometry from a substantially uniform blank or a mold installed in the machine.

[0007] In practice, defects sometimes appear during the machining of certain blanks within the same production range. The origin of these defects may stem from a non-uniformity in the geometry of the blanks, but may also be caused by other phenomena, such as a combination of machining parameters within the range resulting in degraded operation that can damage the surface of the part.

[0008] It also happens that defects appear during the deposition of material onto the mold for the same production run. The origin of these defects may, for example, stem from bonding defects, but may also be caused by other phenomena resulting from a combination of manufacturing parameters within the run, leading to degraded operation that can damage the surface of the part, such as delamination, the presence of a foreign body, or cracking of the plies.

[0009] However, detecting these defects does not always allow for the identification of their causes, and the degradation of these parts represents a direct loss for manufacturers. Similarly, modifying machining / deposition parameters to incorporate large safety margins to prevent part degradation leads to underutilization of the machine's production capacity, resulting in a loss of time and energy in part production.

[0010] In addition, the development of machining / deposition programs is currently poorly instrumented and results from iterative processes inducing a significant and therefore costly development time, and often associated with large safety margins at the level of implementation parameters to ensure that the program will never cause an error once implemented.

[0011] There is therefore a need to improve the methods of responding to a quality defect on a manufactured part, and to improve the processes of developing a manufacturing program.

[0012] The present invention remedies these drawbacks. Summary of the invention

[0013] The invention relates to a method for identifying a quality defect implemented by means of a manufacturing machine capable of determining operating parameters of said manufacturing machine during a manufacturing range of a part by means of a tool having a point.

[0014] According to a general definition of the invention, the process according to the invention further comprises the following steps: - acquire selected operating parameter data of the manufacturing machine during the manufacturing range, said operating parameters comprising on the one hand coordinates in space of the tool tip during its movement forming a plurality of points, and on the other hand at least one other operating parameter of the manufacturing machine associated with each of these points, forming a set of acquired data; - classify the batch of data acquired by association with at least one marker representative of the manufacturing range to form at least one batch of classified data; - matching each point of the classified data set with at least one point of a reference data set, having a marker representative of the manufacturing range identical to that of the classified data set, based on the coordinates in space of each point of the classified measurements set and the coordinates in space of at least one point of the reference data set according to a chosen association rule, - Calculate at least one differential data set for each point in the classified data set with the point in the reference data set to which it was associated; - generate at least one point-by-point functional graphical model of the manufactured part from each calculated differential data set, the functional graphical model being configured to model the geometry of the part in space, each point of the generated part graphical model being associated with the differential data set; and - if the differential data set of a point of the functional graphical model has at least one value greater than a chosen quality threshold for at least one operating parameter, identify the point of the functional graphical model as having a quality defect.

[0015] The Applicant observed that the process according to the invention allows simultaneously a precise modeling of the manufactured part while allowing the detection and precise modeling of any manufacturing defect, both at the level of the manufactured part itself, which would not conform to the expected quality requirements, but also at the level of performance in the control program used for the manufacture of the part which could be optimized.

[0016] In addition, the method according to the invention makes it possible to locate instantly and precisely in space the operating parameters of the manufacturing machine with respect to reference data which do not necessarily have the same exact coordinates, and therefore makes it possible to adjust the number of points acquired to reference data of different resolution.

[0017] By way of example, the operating parameters of the manufacturing machine belong to the group formed by vibrations at the tool level, power of a tool spindle, feed rate of the tool tip, current on each motor of the machine, torque at the tool spindle, spindle speed, noise generated, temperature at the tool tip, pressure applied by the tool, force applied by the tool, force relative to an effector, measured thickness of the part, deviation from the contour, material deposition rate, tension applied to the deposited material, or similar parameter.

[0018] In addition, the representative marker of the manufacturing range includes machining context information belonging to the group formed by machined part / part deposited, manufacturing orders, operations performed, at least one tool used, machine used, sensors used.

[0019] According to an embodiment of the invention, the calculation step further includes an additional substep of calculating a quality score for each point in the differential data set, the quality score being representative of a normalized deviation between the classified data and the reference data of associated points for each operating parameter of the manufacturing machine.

[0020] Advantageously, this step makes it possible to obtain a normalised scoring system for all operating parameters, and thus detect whether said operating parameters are in conformity with the targeted quality score, conformity but potentially optimiseable, or insufficient with respect to the targeted quality score, making it possible in addition to identifying ways to optimize the manufacturing process in addition to identifying a quality defect.

[0021] By way of non-limiting example, the association rule chosen for the matching step of the process according to the invention is carried out via the application of a method of extracting points belonging to the group formed by nearest neighbor, nearest neighbors, radius around the point, followed by an interpolation algorithm according to a mean, a median or a normal distribution.

[0022] In practice, the method further includes a step of recording the functional graphic model thus generated in at least one database.

[0023] Such a step allows an acceleration of the process of identifying the causes of the identified quality defect while ensuring better repeatability of the iterations of the manufacturing processes of a part as well as traceability of the manufacturing process.

[0024] In practice, the step of acquiring measurement data of selected operating parameters of the manufacturing machine is implemented at a frequency between 100 Hz and 500 Hz, and preferably at a frequency between 150 Hz and 250 Hz.

[0025] Advantageously, acquisition at such frequencies provides sufficient resolution to allow for adequate correspondence between points in the acquired data set and reference points during the differential data calculation step. Such acquisition frequencies also ensure that the allocated computing resources are reasonable and that the computation time does not exceed a value chosen for industrial-scale implementation.

[0026] The invention further relates to a manufacturing machine capable of acquiring selected operating parameter data during a manufacturing range for the implementation of the process according to the invention.

[0027] According to a second general definition of the invention, the manufacturing machine comprises: -data processing resources, configured for acquire selected operating parameter data of the manufacturing machine during the manufacturing range, said operating parameters comprising on the one hand coordinates in space of the tool tip during its movement forming a plurality of points, and on the other hand at least one other operating parameter of the manufacturing machine associated with each of these points, forming a set of acquired data; classify the batch of data acquired by association with at least one representative marker of the manufacturing range to form at least one batch of classified data; to match each point in the classified data set with at least one point in a reference data set, having a marker representing the manufacturing range identical to that of the classified data set, based on the spatial coordinates of each point in the classified measurements set and the spatial coordinates of at least one point in the reference data set according to a chosen association rule, calculate at least one differential data set for each point in the classified data set with the point in the reference data set to which it was associated; generate at least one functional point-to-point graphical model of the manufactured part from each batch of calculated differential data, the functional graphical model being configured to model the geometry of the part in space, each point of the graphical model of the part thus generated being associated with the batch of differential data; if the differential data set for a point in the functional graphical model has at least one value exceeding a chosen quality threshold for at least one operating parameter, identify the point in the functional graphical model as having a quality defect; and -data storage means, configured to store at least one database integrating data belonging to the group formed by: a plurality of classified data sets, a plurality of differential data sets, of causes of quality defect associated with the differential data indicating a quality defect, a plurality of reference data sets, the acquired data set(s), the classified data sets, the differential data sets, and each functional graphic model thus generated.

[0028] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0029] Other advantages and features of the invention will become apparent upon examination of the description and drawings in which: - [Fig-1] schematically represents the steps of the process according to the invention; - [Fig.2] schematically represents the process acquisition stage in accordance with the invention; - [Fig.3] schematically represents the classification step of the process according to the invention according to the invention; - [Fig.4] schematically represents the matching and... calculation of the process according to the invention; - [Fig.5] schematically represents a particular embodiment of the calculation step of the process according to the invention; - [Fig.6] schematically represents the step of generating a functional graphical model of the process according to the invention; - [Fig.7] schematically represents the step of identifying a fault quality of the process in accordance with the invention; and - [Fig.8] schematically represents an additional step of querying a database and recording the process. The figures are presented for illustrative purposes only and are in no way limiting to the invention. DETAILED DESCRIPTION

[0030] Unless otherwise specified, the same element appearing on different figures has a unique reference.

[0031] With reference to Figures 1 to 8, the invention relates to a method for identifying a quality defect implemented by means of a manufacturing machine capable of determining operating parameters of said manufacturing machine during a manufacturing process of a part using a tool having a tip

[0032] Manufacturing machine means machining machines and material deposition machines, configured to allow the production of a part according to a manufacturing range.

[0033] A quality defect is understood to mean any divergence between reference parameters and the operating parameters of the manufacturing machine, and any divergence beyond a chosen threshold, of a chosen shape, on the manufactured part compared to the expected part.

[0034] The method according to the invention first comprises a step of acquiring SI data on selected operating parameters of the manufacturing machine during a manufacturing range, said operating parameters comprising on the one hand coordinates in CE space of the tip of the tool during its movement forming a plurality of points, and on the other hand at least one other operating parameter Px of the manufacturing machine associated with each of these points, forming a set of acquired data LD A.

[0035] The term "point" or "machined / deposited point" means each action of acquiring operating parameter data having unique coordinates in CE space, for which at least one operating parameter Px of the manufacturing machine is acquired.

[0036] The term "tool tip" means the end or surface of the tool configured to come into contact with a workpiece during machining-type manufacturing and the end or surface configured to apply the material to be deposited during material deposition-type manufacturing.

[0037] By way of non-limiting example, the operating parameters Px of the manufacturing machine other than the coordinates in CE space belong to the group formed by vibrations at the tool, power of a tool spindle, feed rate of the tool tip, current on each motor of the machine, torque at the tool spindle, spindle speed, noise generated, temperature at the tool tip, pressure applied by the tool, force applied by the tool, force relative to an effector, measured thickness of the part, deviation from contour, material deposition rate, tension applied to the deposited material, or similar parameter.

[0038] A tool is understood to mean any material capable of removing material in order to transform the raw material (block of material) into a finished part with the desired shape, dimensions and surface quality, or capable of depositing material in order to obtain a finished part with the desired shape, dimensions and surface quality.

[0039] According to a particular embodiment of the invention, the SI acquisition step is implemented via sensors integrated into the manufacturing machine.

[0040] According to a second embodiment of the invention, the acquisition step SI comprises the acquisition of data obtained via sensors integrated into the manufacturing machine in combination with data acquired via at least one remote sensor, which records at least one operating parameter of the manufacturing machine Px other than the coordinates in CE space. The recording of the manufacturing machine's operating parameter Px by the remote sensor is performed on a common time base with the sensors of the manufacturing machine, said acquired data being synchronized and correlated with the data from the sensors of the manufacturing machine.

[0041] A sensor is understood to mean any means of measuring a parameter, of calculating from a measurement of a parameter, or of acquiring data available or produced by the manufacturing machine.

[0042] In practice, the SI acquisition step of the selected operating parameter data CE, Px of the manufacturing machine is implemented at a frequency between 100 Hz and 500 Hz.

[0043] This particular frequency range allows for the acquisition of real-time measurement data. Furthermore, such a frequency range makes it possible to obtain a batch of LDA data with a resolution high enough to avoid the need to integrate a time measurement requiring additional calculation and conversion steps, thus limiting the physical computing resources allocated to this task.

[0044] Advantageously, this frequency is adapted to the speed of movement of the tip, and to the targeted accuracy of acquisition.

[0045] According to a preferred mode, the SI acquisition step of the selected operating parameter data CE, Px of the manufacturing machine is implemented at a frequency between 150 Hz and 250 Hz.

[0046] The SI acquisition step further comprises sub-steps including: -S 12 Acquire data on the operating parameter(s) of the Px manufacturing machine; and -S 13 record the acquired data on storage means in the form of an LDA acquired data batch.

[0047] According to a particular embodiment, the acquisition substep S12 is preceded by a substep consisting of calibrating SU the sensors involved in said acquisition substep S12.

[0048] The LDA data set acquired for a given manufacturing range therefore includes, for each acquisition point: -the coordinates in CE space of the tool at the point of acquisition; and -acquisition data of at least one Px operating parameter of the manufacturing machine measured associated with the coordinates in CE space of the tool at the acquisition point.

[0049] By way of non-limiting example, the LDA data set acquired for a given manufacturing range therefore includes for each acquisition point: -the coordinates in CE space of the tool of the acquisition point; -data from a plurality of Px operating parameters of the manufacturing machine, each associated with the coordinates in CE space of the tool of the acquisition point.

[0050] The applicant observed that acquiring Px operating parameter data of the manufacturing machine at such frequencies, in combination with coordinates in CE space, made it possible to obtain a batch of LDA acquired data with sufficient resolution to allow accurate part modeling machined. This particular choice also avoids the generation of an excessive volume of data which could significantly slow down any processing means configured to handle this / these batches of acquired LDA data, and thus allows the implementation of the process on an industrial scale when many parts need to be manufactured.

[0051] The method according to the invention further includes a step of classifying the acquired LDA data by association with at least one MARQ marker representative of the manufacturing range to form at least one batch of classified LDC data.

[0052] In practice, the MARQ marker representing the manufacturing range includes machining context information M1, M2, M3, Mx belonging to the group formed by machined part / deposited part, manufacturing orders, operations performed, at least one tool used, machine used, sensors used, or any other information allowing the implementation context of the manufacturing range to be defined.

[0053] By way of example, the LDC classified data set for a given manufacturing range therefore includes, for each acquisition point: -the coordinates in CE space of the tool at the point of acquisition; - the data of the operating parameter(s) Px of the manufacturing machine associated with the coordinates in CE space of the tool at the acquisition point; and - at least one MARQ marker including the machining context information M1,M2, M3, Mx.

[0054] Advantageously, this S2 classification step makes it possible to associate the LDA acquired data set with a specific implementation context of the manufacturing range, and thus make comparable any data set having a MARQ marker representative of the identical manufacturing range.

[0055] The method according to the invention then includes a step of matching S2B each point of the LDC classified data lot with at least one point of a DREF reference data lot, having a MARQ marker representative of the manufacturing range identical to that of the LDC classified data lot.

[0056] In practice, the S2B matching step includes a first substep of identification S2B1 of at least one set of DREF reference data from a BDD database, said set of DREF reference data having an identical MARQ marker.

[0057] The S2B matching step includes a second association substep S2B2 implemented on the basis of the coordinates in CE space of each point of the LDC classified measurement set and the coordinates in CE space of at least one point of the DREF reference data set by application of at least one chosen association rule.

[0058] By way of example, the association rule includes a method for extracting points belonging to the group formed by nearest neighbor, nearest neighbors, radius around the point, followed by an interpolation algorithm according to a mean, a median or a normal distribution.

[0059] According to a first embodiment, the DREF reference data set comprises at least: -the coordinates in CE space of each theoretical point of the path of the tool tip; -the theoretical data of the operating parameter(s) Px of the manufacturing machine in absolute values ​​associated with said coordinates in CE space of the tool tip; and - the MARQ marker including machining context information Ml, M2, M3, Mx identical to the machining context information Ml, M2, M3, Mx of the classified data set LDC.

[0060] The DREF reference data set therefore includes theoretical nominal values ​​of the operating Px parameters of the manufacturing machine associated with a set of coordinates in CE space for each theoretical point of the part to be manufactured.

[0061] According to a second embodiment, the DREF reference data set comprises at least: -the coordinates in CE space of the tool of each acquisition point of a standard part; -the measurement data of the operating parameter(s) Px of the manufacturing machine, associated with the coordinates in CE space of the tool's measurement point on the master part; and - the MARQ marker including machining context information Ml, M2, M3, Mx identical to the machining context information Ml, M2, M3, Mx of the classified data set LDC.

[0062] According to a third embodiment, the DREF reference data set comprises at least: -the coordinates in CE space of the tool tip for each acquisition point of a previously manufactured part; -the acquisition data of the operating parameter(s) Px of the manufacturing machine associated with said coordinates in CE space for each acquisition point of a previously manufactured part; and - the MARQ marker including machining context information Ml, M2, M3, Mx identical to the machining context information Ml, M2, M3, Mx of the classified data set LDC.

[0063] In practice, each measurement point in the LDC classified data set will have at least one corresponding point in the DREF reference data set, thus forming a set of associated points.

[0064] Advantageously, this matching makes it possible to calculate a differential data set DDI even if the classified data set LDC and the reference data set DREF do not have identical resolution.

[0065] The method according to the invention then includes a calculation step S3 of at least one differential data set DDI for each point of the classified data set LDC with the at least one point of the reference data set DREF to which the point of the classified data set LDC has been associated.

[0066] The calculation step S3 includes calculating the absolute value of the difference between the value of each operating parameter Px of the manufacturing machine at the acquisition point of the classified data set LDC and the value for that same parameter Px at the level of the associated point of the reference data set DREF, this operation being repeated for each set of associated points.

[0067] The Applicant observed that the calculation step made it possible to verify that the compared data are comparable, of the same context, and corresponding so that the result of said calculation is representative of a deviation or not with a chosen quality reference and thus precise.

[0068] According to one embodiment of the invention, the calculation step S3 further comprises an additional substep for calculating a quality score S33 for each parameter Px of each point in the differential data set DDI, the quality score being representative of a normalized deviation between the result of the calculation of the differential data DDI for each parameter Px and a threshold fixed for each parameter Px. The differential data DDI therefore further comprises a quality score associated with each parameter Px for each of the points.

[0069] Advantageously, calculating a quality score makes it possible to normalize the quality scores for all the operating parameters Px of the manufacturing machine according to a single scale, and thus obtain a more uniform batch of differential DDI data.

[0070] The method according to the invention then includes a generation step S4 of at least one point-to-point functional graphical model MGF of the machined part from each batch of calculated differential data DDI.

[0071] Point-to-point means a functional graphical model MGF whose graphical representation is a point cloud or a point cloud ordered in the sequential order of manufacture.

[0072] The point-to-point functional graphical model MGF is configured to model the geometry of the manufactured part in space, each point thus generated being associated with at least one set of differential DDL data

[0073] According to a particular embodiment of the invention, the generation step S4 of the functional graphic model MGF includes a filter substep S41 of the differential data DDI, configured to compare the value of the calculation result for each parameter Px to a noise threshold SB, and to exclude the points whose calculation result of each parameter Px of the differential data DDI is greater than or equal to said noise threshold SB.

[0074] Advantageously, if the operating parameters Px of the manufacturing machine for the batch of differential data DDI associated with at least one point of the functional graphical model MGF are greater than the noise threshold SB, this differential data DDI is considered aberrant / noise and the point(s) are not taken into account for the generation of the functional graphical model MGF. This allows the exclusion of aberrant values ​​that could interfere with the identification of a quality defect.

[0075] By way of non-limiting example, the differential data (DDI) can be represented by means of a color code defined according to the magnitude of the deviation, or as a vector extending from the point in question, allowing for a four-dimensional representation. This step therefore makes it possible to create a graphical representation of the manufactured part highlighting the areas of the part where a parametric deviation for at least one parameter Px has been observed relative to the reference data (DREF).

[0076] Advantageously, a user can therefore immediately observe, via the variations of the differential data DDI, the state of the part and note the presence or absence of a quality defect, and can thus establish a potential correlation between the presence and type of defect observed, and the deviation of parameter Px observed.

[0077] The method according to the invention further includes a step of identifying a quality defect S5 consisting of applying a quality threshold SQ chosen for each of the parameters Px, and comparing said quality threshold SQ to the differential data set DDI calculated for each of the points of the functional graphical model MGF.

[0078] If the differential data set DDI of a point of the MGF functional graphical model has at least one value greater than the quality threshold SQ chosen for at least one parameter Px, identify the associated point of the MGF functional graphical model as having a quality defect and identify the associated point of the MGF functional graphical model for this parameter Px as non-conforming.

[0079] If the differential data set DDI of a point of the functional graphical model MGF has a value less than or equal to the quality threshold SQ chosen for a parameter Px, identify the point of the MGF functional graphical model associated for this parameter Px as conforming.

[0080] According to a first embodiment of the invention, each quality threshold SQ is compared to the calculation results of the differential data set DDI for each corresponding parameter Px of the differential data set DDL

[0081] According to an alternative embodiment of the invention, the quality threshold SQ applied to the quality score is chosen and compared to the quality score for each point of the differential data set DDL

[0082] Advantageously, the process according to the invention makes it possible to guarantee the stability and robustness of manufacturing processes such as machining and material deposition while generating a digital twin of the manufactured part.

[0083] The MGF functional graphic model forms a digital twin of the manufactured part, allowing the manufacturing process parameters to be located instantly and precisely in space, while also ensuring traceability of manufacturing operations to certify that the manufactured part follows the manufacturing instructions.

[0084] The process according to the invention also makes it possible to reduce the industrialization time of parts by decreasing the number of iterations required until the manufacturing process is validated for industrialization.

[0085] Advantageously, the process according to the invention also makes it possible to reduce the machining cycle time by allowing the optimization of the trajectories and the conditions of use of the manufacturing machine.

[0086] Finally, the process according to the invention makes it possible to reduce the time required to inspect the part by concentrating the monitoring and control plan of the manufactured part on what is necessary, namely the areas of the manufactured part for which a quality defect has been identified.

[0087] The identification step S5 is followed by a registration step S6 of the functional graphical model MGF in the database BDD.

[0088] In practice, the BDD database integrates a plurality of functional graphical models MGF, a plurality of classified data sets LDC, a plurality of differential data sets DDI, a plurality of quality scores associated with the differential data sets DDI, at least one list of causes of quality defect associated with the differential data DDI indicating a quality defect, the reference data sets DREF.

[0089] In practice, the S6 recording step of the MGF functional graphical model includes an additional step of querying the BDD database on the basis of the generated and recorded MGF functional graphical model.

[0090] In practice, for each quality defect identified on the MGF functional graphic model, compare the LDD differential data set of said MGF functional graphic model recorded with the DDI differential data sets stored in the BDD database.

[0091] If a match is found for a parameter Px, check if at least one potential cause is associated with it in said database BDD.

[0092] If at least one potential cause is associated with the matching data, mark S71 the quality defect identified on the MGF functional graphic model as having an identified potential cause and record the MGF functional graphic model.

[0093] If no potential cause is associated with the matching data, mark S72 the quality defect identified on the MGF functional graphic model as having an unidentified potential cause and save the MGF functional graphic model.

[0094] The invention further relates to a computer program comprising instructions which, when executed on computer-type processing means, cause the computer to implement the process as described.

[0095] The invention further relates to a computer-readable data storage medium on which the computer program according to the invention is recorded.

[0096] The invention further relates to a manufacturing machine capable of acquiring selected operating parameter data during a manufacturing range for the implementation of the process according to the invention.

[0097] In practice, the manufacturing machine according to the invention includes at least one tool having a point.

[0098] The term "tool tip" refers to the end or surface of the tool configured to come into contact with a workpiece during machining and the end or surface configured to apply the material to be deposited.

[0099] The manufacturing machine according to the invention further comprises data processing means, configured to: - acquire SI selected operating parameter data of the manufacturing machine during the manufacturing range, said operating parameters comprising on the one hand coordinates in CE space of the tool tip during its movement forming a plurality of points, and on the other hand at least one other operating parameter Px of the manufacturing machine associated with each of these points, forming a set of acquired data LD A;

[0100] -classify S2 the batch of acquired LDA data by association with at least one MARQ marker representative of the manufacturing range to form at least one batch of classified LDC data;

[0101] - to match S2B each point of the LDC classified data set with at least one point of a DREF reference data set, having a MARQ marker representative of the manufacturing range identical to that of the LDC classified data set, on the basis of the coordinates in space of each point of the LDC classified measurements set and the coordinates in space of at least one point of the DREF reference data set according to a chosen association rule,

[0102] -calculate S3 at least one differential data set DDI for each point of the classified data set LDC with the point of the reference data set DREF to which it has been associated;

[0103] -generate S4 at least one point-to-point functional graphical model MGF of the manufactured part from each batch of calculated differential data, the functional graphical model being configured to model the geometry of the part in space, each point of the graphical model of the part thus generated being associated with the batch of differential data DDI;

[0104] -if the differential data set DDI of a point of the functional graphical model MGF has at least one value greater than a quality threshold SQ chosen for at least one operating parameter Px, identify S5 the point of the functional graphical model MGF as having a quality defect.

[0105] Finally, the manufacturing machine further includes data storage means, configured to store at least one database BDD integrating data belonging to the group formed by: a plurality of LDC classified data sets, a plurality of DDI differential data sets, of quality defect causes associated with the DDI differential data indicating a quality defect, a plurality of DREF reference data sets, the LDA acquired data set(s), the LDC classified data sets, the DDI differential data sets, and each MGF functional graphic model thus generated.

[0106] According to a particular embodiment of the invention, the manufacturing machine includes integrated sensors configured to measure operating data during the SL acquisition step

[0107] According to a second embodiment of the invention, the manufacturing machine comprises integrated sensors configured to measure operating data during the SI acquisition step in combination with at least one remote sensor, which records at least one operating parameter of the manufacturing machine Px other than the coordinates in CE space. The recording of the manufacturing machine's operating parameter Px by the remote sensor is performed on a common time base with the sensors of the manufacturing machine, said acquired data being synchronized and correlated with the data from the sensors of the manufacturing machine.

[0108] A sensor is defined as any means of measuring a parameter, calculating from a parameter measurement, or acquiring data available or produced by the manufacturing machine.

[0109] Advantageously, the manufacturing machine according to the invention makes it possible to follow the manufacturing process precisely while reducing the industrialization time of a part to be manufactured and reducing the time of quality control operations.

[0110] It also makes it possible to verify compliance with manufacturing instructions and reduce the development cost of a series of parts to be manufactured by ensuring manufacturing homogeneity and cost reduction by reducing the number of manufacturing iterations of the first parts before series production.

Claims

1. Demands A method for identifying a quality defect implemented using a manufacturing machine capable of determining the operating parameters of said manufacturing machine during a manufacturing process of a part using a tool with a point, comprising the following steps: SI: acquire selected operating parameter data of the manufacturing machine during the manufacturing range, said operating parameters comprising on the one hand coordinates in space (CE) of the tool tip during its movement forming a plurality of points, and on the other hand at least one other operating parameter (Px) of the manufacturing machine associated with each of these points, forming a set of acquired data (LDA); S2: classify the batch of acquired data (LDA) by association with at least one marker (MARQ) representative of the manufacturing range to form at least one batch of classified data (LDC); S2B: matching each point of the classified data set (LDC) with at least one point of a reference data set (DREF), having a marker (MARQ) representative of the manufacturing range identical to that of the classified data set (LDC), based on the coordinates in space of each point of the classified measurements set (LDC) and the coordinates in space of at least one point of the reference data set (DREF) according to a chosen association rule, S3: Calculate at least one differential data set (DDI) for each point in the classified data set (LDC) with the point in the reference data set (DREF) to which it has been associated; S4: generate at least one point-to-point functional graphical model (FGM) of the manufactured part from each batch of calculated differential data, the functional graphical model being configured to model the geometry of the part in space, each point of the graphical model of the part thus generated being associated with the batch of differential data (DDI); S5: if the differential data set (DDI) of a point in the functional graphical model (MGF) has at least one value above a quality threshold (QT) chosen for at least one operating parameter (Px), identify the point of the functional graphic model (FGM) as having a quality defect.

2. The method according to claim 1, characterized in that the operating parameters (Px) of the manufacturing machine belong to the group formed by vibrations at the tool level, power of a tool spindle, feed rate of the tool tip, current on each motor of the machine, torque at the tool spindle, spindle speed, noise generated, temperature at the tool tip, pressure applied by the tool, force applied by the tool, force relative to an effector, measured thickness of the part, deviation from the contour, material deposition rate, tension applied to the deposited material, or similar parameter.

3. Method according to claim 1, characterized in that the marker (MARQ) representative of the manufacturing range includes machining context information (M1, M2, M3, Mx) belonging to the group formed by machined part / deposited part, manufacturing orders, operations performed, at least one tool used, machine used, sensors used.

4. A method according to any one of claims 1 or 2, characterized in that the calculation step S3 further comprises an additional calculation substep (S33) of a quality score for each point in the differential data set (DDI), the quality score being representative of a normalized deviation between the classified data (LDC) and the reference data (DREF) of associated points for each operating parameter (Px) of the manufacturing machine.

5. A method according to any one of claims 1 to 3, characterized in that in the S2B matching step, the chosen association rule includes the application of a point extraction method belonging to the group formed by nearest neighbor, nearest neighbors, radius around the point, followed by an interpolation algorithm according to a mean, a median or a normal distribution.

6. A method according to any one of claims 1 to 4, characterized in that it further comprises a step of recording S6 of the functional graphic model (MGF) in at least one database (BDD).

7. A method according to any one of claims 1 to 5, characterized in that the SI acquisition step of selected operating parameter data (Px) of the manufacturing machine is implemented at a frequency between 100 Hz and 500 Hz.

8. A method according to any one of claims 1 to 5, characterized in that the SI acquisition step of selected operating parameter data (Px) of the manufacturing machine is implemented at a frequency between 150 Hz and 250 Hz.

9. Manufacturing machine capable of acquiring selected operating parameter data during a manufacturing range for the implementation of the process according to claims 1 to 7, characterized in that it comprises: - data processing means, configured to acquire (SI) selected operating parameter data of the manufacturing machine during the manufacturing range, said operating parameters comprising on the one hand coordinates in space (CE) of the tool tip during its movement forming a plurality of points, and on the other hand at least one other operating parameter (Px) of the manufacturing machine associated with each of these points, forming a batch of acquired data (LDA); classify (S2) the batch of acquired data (LDA) by association with at least one marker (MARQ) representative of the manufacturing range to form at least one batch of classified data (LDC);to map (S2B) each point of the classified data set (LDC) with at least one point of a reference data set (DREF), having a marker (MARQ) representative of the manufacturing range identical to that of the classified data set (LDC), on the basis of the coordinates in space of each point of the classified measurement set (LDC) and the coordinates in space of at least one point of the reference data set (DREF) according to a chosen association rule, to calculate (S3) at least one differential data set (DDI) for each point of the classified data set (LDC) with the point of the reference data set (DREF) to which it has been associated; to generate (S4) at least one point-by-point functional graphical model (MGF) of the manufactured part from each data set; calculated differentials, the functional graphical model being configured to model the geometry of the part in space, each point of the graphical model of the part thus generated being associated with the differential data set (DDI); if the differential data set (DDI) of a point of the functional graphical model (MGF) has at least one value greater than a quality threshold (SQ) chosen for at least one operating parameter (Px), identify (S5) the point of the functional graphical model (MGF) as having a quality defect; and -data storage means, configured to store at least one database (DB) integrating data belonging to the group formed by: a plurality of classified data sets (LDC), a plurality of differential data sets (DDI), of quality defect causes associated with the differential data (DDI) indicating a quality defect, a plurality of reference data sets (DREF), Ic / lcs acquired data sets (LDA), the classified data sets (LDC), the differential data sets (DDI), and each functional graphical model (MGF) thus generated.

10. A computer program comprising instructions which, when executed on a computer, cause the computer to implement the method according to any one of claims 1 to 7.

11. Computer-readable data storage medium on which the computer program according to claim 9 is recorded.

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