WO2008142386A1 - Appareil de contrôle de processus d'usinage - Google Patents

Appareil de contrôle de processus d'usinage Download PDF

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Publication number
WO2008142386A1
WO2008142386A1 PCT/GB2008/001700 GB2008001700W WO2008142386A1 WO 2008142386 A1 WO2008142386 A1 WO 2008142386A1 GB 2008001700 W GB2008001700 W GB 2008001700W WO 2008142386 A1 WO2008142386 A1 WO 2008142386A1
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WO
WIPO (PCT)
Prior art keywords
data
characteristic
abnormal
signature
cycle
Prior art date
Application number
PCT/GB2008/001700
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English (en)
Inventor
Colin Sage
David Andrew Clifton
Original Assignee
Rolls-Royce Plc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Rolls-Royce Plc filed Critical Rolls-Royce Plc
Publication of WO2008142386A1 publication Critical patent/WO2008142386A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
    • G05B19/4065—Monitoring tool breakage, life or condition
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/34—Director, elements to supervisory
    • G05B2219/34048—Fourier transformation, analysis, fft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/37—Measurements
    • G05B2219/37519—From machining parameters classify different fault cases
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/37—Measurements
    • G05B2219/37545—References to be compared vary with evolution of measured signals, auto-calibrate
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/50—Machine tool, machine tool null till machine tool work handling
    • G05B2219/50197—Signature analysis, store working conditions, compare with actual

Definitions

  • the invention relates to a method of process monitoring a component manufacturing operation and process monitor apparatus.
  • the invention concerns on-line detection and recognition of manufacturing process malfunctions or abnormal operations and events, and their resulting component anomalies, using an advanced signal-processing algorithm.
  • Data collection for the purpose of process monitoring in a number of industries has been performed for many years. It has been applied to monitor the condition of the product, the process, and the machines performing the process and tools that may be used by the machines.
  • a plurality of sensors have been attached or positioned to collect data about a number of variables.
  • the sensor outputs are linked to a data collection and analysis processor in which they are compared with stored values and the process etc. is judged to be operating within or outside expected parameters.
  • Data representing a "good" process are first collected, for example in a cutting process using new tools, so that the system is taught or learns what is a good process, or an operator can judge an appropriate limit level. Using this data as a reference, the system or operator calculates boundary conditions based on the learned data.
  • the system In normal operation the system continually monitors the sensor outputs and, if a boundary limit is breached, will issue an audible warning and/or issue a signal to interrupt or halt the process. In this way gross malfunctions such as extreme tool wear or failure (even impending failure) can be captured preventing breakage and consequential component damage. However, such systems only issue an alarm or halt the process and do not diagnose the cause of the fault.
  • Power monitoring is another widely used monitoring method in which machine tool power is monitored on simple upper and lower gated limits.
  • the method tends to lack sensitivity, however.
  • a machine capable of handling a physically large component almost invariably is supplied with a large, high power spindle.
  • all operations are performed on a single machine.
  • the same machine tool will be utilised for turning, cutting, and drilling.
  • all of the operations required to produce a disc of 1 meter diameter, having up to 100 holes each of 6 millimetre diameter will be performed using a single machine.
  • the power needed to drill a 6 millimetre hole is a small even insignificant fraction of the power required to turn the outer circumference of the disc.
  • a power monitoring method is not adequately sensitive to the small changes caused by a process malfunction during a drilling operation.
  • United States Patent 5,070,655 concerns a machine tool monitor having sensors arranged to provide power and vibration signals to a controller, which processes the signals in order to detect a change in the machine processing parameters.
  • the monitor apparatus may issue audible and visual alarms to alert an operator, and may automatically halt the machining operation.
  • the monitor further indicates whether the condition of the tool and its associated machining operation warrants scrutiny or service.
  • An example is described in which a grinding machine, provided with power and vibration sensors, has a controller adjusted to signal conditions associated with grinding wheel sharpness, loss of coolant, and excessive vibrations. When the level of a measured parameter exceeds a predetermined limit an alarm is triggered.
  • a need for the invention arises in aeroengine manufacture for monitoring of critical rotating component manufacturing processes.
  • detection of impending cutting tool failure will prevent tool breakage and consequential damage.
  • the costs associated with a broken tool on critical components arise from laboratory investigations and testing to determine effects on the component, the cost of tooling replacement and repair, the cost of re-work and possibility of a scrapped component (which can be particularly costly).
  • undetected surface damage can lead to in-service failure. Where the component in question is built into a safety critical assembly the consequences of failure may include possible loss of life.
  • the present invention seeks to provide a solution to the problems inherent in known process monitoring methods by simultaneously monitoring several different parameters and combining the individual results in such a way as to eliminate false alarms and provide a more versatile and sensitive record of the component manufacture.
  • a further objective of the invention is to provide a monitoring process that not only detects gross process malfunctions, but also is able to distinguish between gross process malfunctions and normal tool wear.
  • the method shall be able to detect the production of surface anomalies caused by process malfunctions undetected by known process monitoring systems.
  • it is intended to provide an indication of the likely cause of a surface anomaly and its likely effect on component surface integrity.
  • a method of process monitoring a component manufacturing operation comprises: attaching a plurality of sensors to a component manufacturing machine to measure a plurality of "n" machining parameters, sampling the sensor outputs during a manufacturing operation cycle, processing the sampled sensor outputs to produce a characteristic signature for each process cycle, storing a multiplicity of characteristic signatures, comparing each newly produced characteristic signature with stored characteristic signatures, and providing an output in accordance with the result of said comparison to indicate whether or not the process is a "normal” or "abnormal” process.
  • the plurality of sensors are arranged to measure a plurality of machining parameters including machine power consumption, tool bit accelerations, acoustic emissions, vibration and tool feed force, and the tool bit acceleration sensors are arranged preferably to measure tool bit accelerations in three mutually perpendicular planes.
  • the output from each of the plurality of sensors is repeatedly sampled during a process cycle to provide a stream of time domain data.
  • time domain data are transformed into the frequency domain using a Fast Fourier Transform.
  • time domain data from each of the sensors are captured in at least one data window and the data in each data window are transformed into the frequency domain using a Fast Fourier Transform.
  • time domain data from each of the "n" sensors may be captured in a plurality of at least partially overlapping data windows and the data in each data window transformed into the frequency domain using a Fast Fourier Transform.
  • n data streams each associated with a different sensor are combined in an "n"-dimensional space to produce at least one characteristic signature or a vector for an individual manufacturing operation cycle or part thereof.
  • the characteristic signatures of a multiplicity of preceding manufacturing operation cycles are stored and a characteristic signature corresponding to a new manufacturing operation cycle is compared with the stored signatures to determine if the new operation cycle is "normal” or "abnormal".
  • Figure 1 shows a schematic diagram of a shop floor machine tool and process monitor system incorporating the invention
  • Figures 2a, 2b, 2c, and 2d show graphical representations of time-series data in five sensor channels captured during a "normal" drilling operation
  • Figure 3 shows a graphical representation of spindle power variation over time during a "normal" drilling operation
  • Figure 4 shows a functional diagram of the data transform and signature analysis method steps of the invention
  • Figure 5 shows a typical characteristic signature of a variable "y" (e.g. Acoustic Emission) constructed with respect to a system operating point (e.g. Spindle power); and
  • y e.g. Acoustic Emission
  • Figure 6 shows a 2-dimensional visualization of a multi-dimensional signature illustrating clustering of "normal” and "abnormal” operations.
  • FIG. 1 Illustrated in Figure 1 is a numerically controlled machine tool generally indicated at 2, for the purposes of this description and solely by way of example, the machine is shown with a drill bit ready to perform a drilling operation.
  • the machine tool 2 is fitted with a number of monitoring devices or sensors 14, 16, 18, 20 which respectively monitor coolant flow rate, spindle or workpiece feed force, workpiece vibration, and workpiece acoustic emissions.
  • An accelerometer 22 is mounted on the tool bit holder 8 or machine spindle 10 to measure accelerations of the tool bit in three mutually orthogonal planes designated x, y and z, see Fig 1. These are planes fixed with respect to the bed of the machine tool 2. This list of monitored parameters is not exclusive and further parameters may be measured depending upon requirements, but we have found so far that those listed as sufficient for present purposes.
  • the output signals: coolant flow rate signal 22; acoustic emission signal 24; vibration signal 26; feed force signal 28; and accelerometer signals 30, 32, 34 are connected to data capture and sampling units indicated at 36.
  • signals representing spindle power 38; spindle feed force 40; cycle interrupt 42 and monitor cycle start 44 are supplied directly from the machine tool control system to the data capture unit 36.
  • a feed force signal 40 is provided from the spindle drive mechanism of the machine tool 2
  • Data captured and sampled by unit 36 is passed to a data transform and signature analysis unit 46 for processing and evaluation.
  • Unit 46 comprises a digital computer programmed to perform the complex calculations necessary to transform the input data signals to produce the characteristic signature representing each process cycle, and to compare each new signature with stored signatures for evaluation purposes and to determine if a process is "normal” or "abnormal".
  • the computer unit 46 is connected to a data storage unit 50 for the purpose of storing new data and for retrieving previously stored data.
  • the computer unit 46 is also connected to an operator interface and graphical display unit generally indicated at 52. Typically this is also a computer having a display screen 54, upon which information is presented for viewing by an operator, and a keyboard by means of which the operator may enter commands etc.
  • the outputs from the various sensors are sampled at a predetermined sampling rate.
  • the raw data is therefore acquired in the time domain and is not in the most suitable form for constructing a characteristic signature and determining whether an individual process cycle is "normal” or "abnormal".
  • the tool 6 carried in the toll bit holder 8 is rotated and moved transversely with respect to the workpiece 4.
  • the sensor outputs therefore exhibit peaks in vibration amplitude at the fundamental rotational frequency of the machine spindle when observed in the frequency domain.
  • the vibration amplitudes can be used to characterise the manufacturing process, as the data are acquired in the time domain, the characteristics are not easy to identify in their initial format.
  • the acquired data are transformed into the frequency domain.
  • the outputs from the plurality of sensors are repeatedly sampled during a process cycle to provide a stream of time domain data, see 48 in Figure 1.
  • the data are transformed into the frequency domain using a Fast Fourier transform (FFT).
  • FFT Fast Fourier transform
  • Each time domain signal was divided in each process cycle into a number of windows, each of a relatively large number of points, and the FFT was performed on each window.
  • the data were divided into windows of 4096 points, and a 4096-point FFT was performed on each data window. At a sampling frequency of 20 KHz this corresponded to approximately 5 FFTs per second of data. It was found by experience that this provided sufficient resolution for identifying frequency-based events indicative of system abnormalities.
  • Fig 2a the three time-series spindle accelerometer signals A x , A y and A 2 on channels 30, 32, 34 respectively; and Fig 2d shows the acoustic emission signal on channel 26.
  • Fig 3 shows the spindle power signal 38.
  • the horizontal axis represents time, note that Figs 2-d inclusive have a common scale but Fig 3 has a slightly different time scale so although events in the first four illustrations are vertically aligned, the same event is slightly displaced in Fig 3.
  • stage 1 initial drilling into the disc
  • stage 2 a peak power period where the drill-bit breaches the rear plane of the disc
  • stage 3 final withdrawal of the drill-bit from the disc.
  • Drill power-on and power-off events are clearly seen at the start an end of a process cycle as transient spikes.
  • the drill makes contact with the static disc and begins to drill into the metal.
  • acoustic emission increases to a largely constant, non-zero value.
  • the value of A z (Fig 2b) also remains approximately zero, but A x and A y increase throughout this drilling operation.
  • Fig 4 shows a functional diagram illustrating the data capture, transformation and analysis procedure performed by unit 46 of Fig 1.
  • the raw sensor data at 48 is readied for analysis by a pre-processing or signal conditioning step 60 in order to remove background noise, unrelated artefacts such as transient power spikes, power supply components and spectral components below a minimum power threshold, as is well known in the art.
  • the time-series data 61 is then loaded into data windows 62a, 62b, 62c [three such windows are shown merely by way of example only]. As each window is filled a Fast Fourier transform is performed on the contents to transform the data from time domain signals into the frequency domain signals 64a, 64b, 64c.
  • a number of FFTs will have been performed, thus generating a set of "spectral features" unique to the process cycle. If there are “n” sensor channels the FFT transforms performed on the full suite of data available will yield “n” feature sets for each process cycle.
  • a set of features is derived from each of the "n” input sensors in feature extraction step 66. In general, “m” features are obtained from each of the "n” sensors, however the number of features in each set may be variable. An example of a feature is a significant frequency.
  • the processed data are normalised in step 68 to ensure comparison between features is performed without dependence on absolute values, such that the units of measurement for each feature are not significant.
  • a characteristic signature is constructed in block 70, labelled "analysis" in the diagram.
  • Each new characteristic signature is compared with a set of stored characteristic signatures 72 by a decision block 74, which decides whether or not a signature is "normal” or "abnormal".
  • a "yes” decision at 76 indicates a successful operation and, as indicated by process block 78, either the machining operation is allowed to continue towards completion or it is successfully completed.
  • the output form decision block 74 is a "no” the signature is analysed more precisely, as indicated by process block 80, to determine whether the process is "abnormal” or a "high limit” has been exceeded and the operation must be stopped immediately, or whether it merely warrants a warning but may be allowed to continue towards completion.
  • the position of these upper and lower limits is selected by reference to the distribution of the data population.
  • the limits of normality may be set to capture a chosen percentage, say 99%, of the results.
  • a result, or signature, falling inside of these limits is deemed to be "normal” and a signature outside of the limits is deemed to be “abnormal”.
  • a probabilistic approach such as this has an arbitrary element, at least initially, but by learning through experience the limits can be adjusted in order to exclude or include components that visual inspection and physical analysis show to be wrongly categorised. In this way a practically useful, tool-specific model of "normality" may be constructed.
  • the use of numerous channels makes visual interpretation of the raw data extremely difficult.
  • Single channel inspection as in most of the above-mentioned prior art examples is highly susceptible to erroneous false alarms.
  • multiple data channels make holistic exploration of the data too difficult, as visualisations typically only cope with 2- or 3-dimensional data.
  • Higher dimensional data that is data that requires more than two or three dimensions to represent, can be difficult to interpret.
  • One approach to simplification, especially with a view to visualisation of the data is to use a mapping procedure, such as Sammon's mappijng, to project the signatures into 2 dimensions.
  • the mapping process to produce this result may be carried out using a neural network.
  • the present invention seeks, therefore, to combine the multi-dimensional data features in such a way that the whole can more easily be visualised and interpreted.
  • this step is performed in the block 70, the function performed in the block 70 is to combine the data features in "m"-dimensional space into a single characteristic vector or point unique to the particular process cycle.
  • the data may be manipulated to produce either a single point representing a complete cycle or a series of points representing different time periods of a cycle.
  • five features were used to construct signatures for each process cycle.
  • the data channels represented in Figures 2a-2d and Figure 3 produce time related measurements throughout a process cycle.
  • Figure 5 An example of one such visualisation is shown in Figure 5 which illustrates a visualisation in which acoustic emission is plotted against spindle power and includes two points one deemed as “normal” and the other as “abnormal”.
  • Figure 6 shows a two-dimensional mapping obtained using a large number of results produced from the A x accelerometer shown at Fig 2a, which is an example of a visualisation from original "m"-dimensional space into 2-dimensions.
  • Fig 2a is an example of a visualisation from original "m"-dimensional space into 2-dimensions.
  • Clearly visible from this 2-dimensional map is the clustering of a great many of the results as well as the overall spread in the distribution of all results.
  • Each signature point is represented in the drawing by a cross, and a degree of clustering is evident in this visualisation of a large number of process signatures.
  • a main cluster of signature points deemed to be "normal” is labelled in the drawing and the limit of within which a signature, and the process with which it is associated, is accepted as “normal” is indicated by a dashed line. Points outside the enclosed space are deemed to be “abnormal” results. In these tests the tool was used repeatedly beyond the point where it would normally have been replaced, in order to generate test data from a worn tool. The trend attributable to tool wear is indicated by an arrow in the drawing. A signature change caused by an anomaly or other significant event will produce a data point distinctly separate from the "normal” cluster. These points will usually exhibit their own clustering. By way of example two such "abnormal” clusters are indicated in the drawing. Events such as, for example, “tool chipping” and “loss of coolant” could be expected to produce distinctively separate and identifiable clusters of their own.
  • the method of the invention not only detects gross process malfunctions but also is able to distinguish between gross process malfunctions and normal tool wear, and is able to recognise events, individually or in combination, indicative of the production of component surface anomalies.
  • This new process is able not only to detect events that may have created a surface defect, but also to inform of the likely cause and what likely effect on surface integrity may be expected.
  • Malfunctions and anomalies are recognised at the data evaluation stage in the combined and fused data as departures from normality. In a normal drilling process, for instance, the analysed data for successive drilled holes will be dimensionally similar and clustered closely together. Increasing but acceptable tool wear will cause the data to drift away from the initial, normal position until an acceptable limit is reached.
  • control limits are set about the reference data group to accommodate normal tool wear. Should these limits be exceeded the system will issue a warning to the operator, for example to change tools.
  • Deviation from the reference group is deemed as a departure from a normal process and the system is pre-programmed to issue an appropriate warning, and if necessary to halt the machining operation.
  • Departures from normality are characteristic of a process malfunction or production of a surface anomaly. Based on learned experience the system is also arranged to produce an indication of the most likely cause of the event. This speeds up decision making on component sentencing and/or machine maintenance in the event that a machine fault is suspected. Following remedial action normal processing is resumed.
  • the method of the present invention is applicable to a wide range of machine tools and operations. All conventional machining processes can be monitored by the system described above including milling, broaching, turning, grinding (all types), drilling, reaming, nobbing, shaping, burnishing, honing etc. With the substitution or inclusion of different sensors for example thermal, pressure, current, other and less conventional machining processes may be monitored such as EDM (electro discharge machining), ECM (electro chemical machining), wire cutting, laser cutting, laser welding, abrasive water jet machining, chemical milling etc. Furthermore other processes may be monitored, such as casting, forging, stamping/pressing, rolling, hot- and cold- cropping, injection moulding, die casting, friction and inertia welding both rotary and linear kinds, super plastic forming etc.
  • EDM electro discharge machining
  • ECM electro chemical machining
  • wire cutting laser cutting
  • laser welding laser welding
  • abrasive water jet machining chemical milling etc.
  • other processes may be monitored, such as casting

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

L'invention concerne un appareil de contrôle de processus de fabrication, en particulier d'un processus d'usinage, et un procédé d'acquisition et d'analyse de données multi-paramètres pour le diagnostic du processus. Plusieurs capteurs (14, 16, 18, 20, 22) sont reliés à une machine-outil (2) afin de surveiller une pluralité de paramètres d'usinage comprenant la consommation d'énergie, les émissions acoustiques, les vibrations, la puissance et la force de la machine. Pendant chaque opération, les sorties des capteurs (24, 26, 28, 30, 32, 34, 38, 40) sont échantillonnées de manière répétée (36) et traitées (46) afin de fournir une signature (54) caractéristique de l'opération. Les données sont analysées afin de déterminer les limites d'une opération d'usinage normale, y compris l'état et le statut des outils (6) et du matériel (2). En stockant les signatures (50) pour un grand nombre d'opérations dont les résultats « normaux » et « anormaux » sont connus, une population de données est créée, avec laquelle de nouvelles signatures peuvent être comparées et une indication de diagnostic (54) est produite. Des alertes d'anomalies et d'évènements anormaux, comme un dommage d'outil, peuvent être produites automatiquement et en temps réel.
PCT/GB2008/001700 2007-05-17 2008-05-16 Appareil de contrôle de processus d'usinage WO2008142386A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB0709420A GB0709420D0 (en) 2007-05-17 2007-05-17 Machining process monitor
GB0709420.4 2007-05-17

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WO2008142386A1 true WO2008142386A1 (fr) 2008-11-27

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FR3028785A1 (fr) * 2014-11-25 2016-05-27 Snecma Procede de detection d'anomalie de tournage d'une piece
US20160349737A1 (en) * 2015-05-29 2016-12-01 Chun-Tai Yen Manufacturing efficiency optimization platform and tool condition monitoring and prediction method
CN106271881A (zh) * 2016-08-04 2017-01-04 华中科技大学 一种基于SAEs和K‑means的刀具破损监测方法
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US9691395B1 (en) 2011-12-31 2017-06-27 Reality Analytics, Inc. System and method for taxonomically distinguishing unconstrained signal data segments
US9886945B1 (en) 2011-07-03 2018-02-06 Reality Analytics, Inc. System and method for taxonomically distinguishing sample data captured from biota sources
CN109968104A (zh) * 2019-03-07 2019-07-05 中南大学 一种高速拉削加工的高灵敏度精密监测方法
US10345800B2 (en) 2016-03-30 2019-07-09 3D Signals Ltd. Acoustic monitoring of machinery
EP3458925A4 (fr) * 2016-05-16 2020-01-22 Weir Minerals Australia Ltd Surveillance de machine
US10839076B2 (en) 2016-12-21 2020-11-17 3D Signals Ltd. Detection of cyber machinery attacks
US10916259B2 (en) 2019-01-06 2021-02-09 3D Signals Ltd. Extracting overall equipment effectiveness by analysis of a vibro-acoustic signal
US11267066B2 (en) 2018-03-20 2022-03-08 Lincoln Global, Inc. Weld signature analysis for weld quality determination
US11520307B2 (en) 2019-01-22 2022-12-06 Fanuc Corporation Tool management system of machine tool
US11556901B2 (en) * 2019-01-22 2023-01-17 Fanuc Corporation Preventive maintenance system of machine tool
US20230038415A1 (en) * 2020-02-07 2023-02-09 Fanuc Corporation Diagnosis device
WO2023052063A1 (fr) * 2021-09-30 2023-04-06 TRUMPF Werkzeugmaschinen SE + Co. KG Procédé de surveillance de processus d'usinage dans une machine d'usinage et machines d'usinage
FR3134902A1 (fr) * 2022-04-20 2023-10-27 N2C Sas Procédé de suivi de l’état de fonctionnement d’actionneurs linéaires compris dans une machine-outil à des fins de diagnostic et de maintenance prédictive, produit programme d’ordinateur, médium de stockage et dispositif de suivi correspondant

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