Optical manufacturing process sensing and status indication system

Abstract

An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

Claims

1. A manufacturing process sensing and status indication system comprising: one or more optical sensors configured to: measure optical emissions generated by a scanning heat source during an additive manufacturing process as the scanning heat source moves into and out of a field of view of the one or more optical sensors to produce a workpiece, and produce time-domain data from the measured optical emissions; an analysis system configured to provide: a first feature extraction process that extracts, from the time domain data, features that are related to thermal excursions resulting from the scanning heat source moving into and out of the field of view of the one or more optical; a second feature extraction process that extracts, from the time domain data, features that are related to a background temperature of the workpiece detected while the scanning heat source is outside of the field of view of the one or more optical sensors; and a classification process that, from the features extracted by the first and second feature extraction processes, distinguishes features associated with a baseline or nominal operating condition from features associated with a deviant or off-nominal process condition; and a status indicator that is configured to communicate the results of the classification process at a given instant in time to a human-machine interface.

2. The system as recited in claim 1, wherein the status indicator is further configured to record the results of the classification process at a sequential series of times during the manufacturing process.

3. The system as recited in claim 1, wherein machinery used to perform the manufacturing process includes a bed of powdered metals and the scanning heat source is a scanning laser that sinters or melts at least a portion of the bed of powdered metals.

4. The system as recited in claim 3, wherein the manufacturing process is performed using one or more of: a bed of powdered metals that are sintered or melted by a scanning electron beam; a metal wire fed by a mechanical wire feeder and melted or sintered by a laser; a metal wire fed by a mechanical wire feeder and melted or sintered by an electron beam; a metal wire fed by a mechanical wire feeder and melted or sintered by an arc welding process comprising one or more of gas metal arc welding (GMAW), gas tungsten arc welding (GTAW), plasma arc welding (PAW); powder fed by a nozzle and fluidized, carried, or otherwise entrained in an inert gas stream and where the heat addition is by a laser.

5. The system as recited in claim 1, wherein the classification process determines the Mahalanobis Distance between nominal and off-nominal process conditions in a vector space comprised of feature vectors produced by the first and second feature extraction processes.

6. The system as recited in claim 5, where the classification process determines whether the Mahalanobis Distance associated with a specific feature vector is above or below a critical Chi-Squared critical cutoff value to distinguish between nominal and off-nominal conditions.

7. The system as recited in claim 1, wherein the one or more optical sensors comprise a single or multiple wavelength pyrometer.

8. The system as recited in claim 1, wherein the one or more optical sensors comprise a thermocouple.

9. The system as recited in claim 1, wherein the one or more optical sensors comprise a Resistance Thermal Device.

10. The system as recited in claim 1, where the classification system uses features from the first feature extraction process to determine if the heat addition and heat source characteristics associated with the two different process conditions are different or the same.

11. The system as recited in claim 1, where the classification system uses features from the second feature extraction process to determine if a material response associated with the two different process conditions are different or the same.

12. A manufacturing process sensing and status indication system comprising: one or more optical sensors measuring optical emissions generated by a scanning heat source during an additive manufacturing process as the scanning heat source moves into and out of a field of view of the one or more optical sensors to produce a workpiece an analysis system configured to provide: a first feature extraction process that extracts, from sensor data collected by the one or more optical sensors, features that are related to thermal excursions resulting from the scanning heat source moving into and out of the field of view of the one or more optical sensors; a second feature extraction process that extracts, from sensor data collected by the one or more optical sensors, features that are related to a background temperature of the workpiece detected while the scanning heat source is outside of the field of view of the one or more optical sensors; and a classification process that, from the features extracted by the first and second feature extraction processes, distinguishes features associated with a baseline or nominal operating condition from features associated with a deviant or off-nominal process condition; and a status indicator that is configured to communicate the results of the classification process at a given instant in time to a human-machine interface.

13. The system as recited in claim 12, wherein the status indicator is further configured to record the results of the classification process at a sequential series of times during the manufacturing process.

14. The system as recited in claim 12, wherein the additive manufacturing process is performed using a bed of powdered metals that are sintered or melted by a scanning laser.

15. The system as recited in claim 12, wherein the manufacturing process comprises one or more of a bed of powdered metals that are sintered or melted by a scanning electron beam; a metal wire fed by a mechanical wire feeder and melted or sintered by a laser; a metal wire fed by a mechanical wire feeder and melted or sintered by an electron beam; a metal wire fed by a mechanical wire feeder and melted or sintered by an arc welding process comprising one or more of gas metal arc welding (GMAW), gas tungsten arc welding (GTAW), plasma arc welding (PAW); powder fed by a nozzle and fluidized, carried, or otherwise entrained in an inert gas stream and where the heat addition is by a laser.

16. The system as recited in claim 12, wherein the classification processes determines the Mahalanobis Distance between nominal and off-nominal process conditions in a vector space comprised of feature vectors produced by the first and second feature extraction processes.

17. The system as recited in claim 16, where the classification system determines whether the Mahalanobis Distance associated with a specific feature vector is above or below the critical Chi-Squared critical cutoff value to distinguish between nominal and off-nominal conditions.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a schematic illustration of a heat source impinging upon a workpiece.

(2) FIG. 2 is a schematic illustration of a workpiece giving off optical radiation due to heating by a heat source and the optical sensor is a non-contact pyrometer.

(3) FIG. 3 is a schematic illustration of raw pyrometer signals, which are then transformed by extracting features therefrom.

(4) FIG. 4 is a schematic illustration of an optical sensor that is capturing the optical emissions from the radiation process, comprising a spectrometer where the raw spectrum shows integrated spectral intensity as a function of wavelength.

MODES OF CARRYING OUT THE INVENTION, AND INDUSTRIAL APPLICABILITY

(5) In FIG. 1, a heat source 100 is shown impinging upon a workpiece 101. There is a molten region 102 on the workpiece directly below the region of the workpiece 101 affected by the energy source 100. There also could be a region of ionized or vaporized material 103 which is above the molten region 102. Both the molten region 102 and the vaporized or ionized region 103 will emit optical radiation 104. This radiation is detected by an optical sensor 105. The sensor can be stationary with respect to the energy source, also known as an Eulerian reference frame, or it can be in the same reference frame as the moving energy source, also known as a Lagrangian reference frame.

(6) In FIG. 2, the workpiece 200 gives off optical radiation 201 due to heating by the heat source 202 and the optical sensor is a non-contact pyrometer 203. This pyrometer collects a thermal signal 204. It is assume that the size of the heat source 202 is smaller than the size of the field of view of the pyrometer. The thermal signal comprises two type of features: a slower moving signal 205 that is associated with the heat source 202 gradually coming into the field of the view of the pyrometer as well as representing the background temperature of the workpiece 200; and a faster moving signal 206 that represents individual high temperature excursions caused by the heat source 202 as it moves into and out of the field of view of the pyrometer.

(7) In FIG. 3, the raw pyrometer signals 300 are then transformed by extracting features from this data. There are several types features that can be extracted. First, from the slower varying data 301 the heating rate 302 and cooling rate 303 as well as the peak background temperature 304 can be extracted. Secondly, from the faster varying data 305, the heating rate 306, the cooling rate 307 and the peak temperature 308 can be derived. The slower varying data features indicated by 302, 303, and 304 correspond to the material response, as it is largely dictated by the local thermal boundary conditions such as the thermal conductivity, heat sinking properties, etc. Additionally, the slower varying thermal data indicates the background temperature of the workpiece in between scan events from the moving heat source. This again is related to the material response as opposed to the thermal input from the process. The more rapidly varying data features indicated by 306, 307, and 307 are more representative of the process inputs and the energy inputs to the manufacturing process on account of the scanning energy source.

(8) In FIG. 4, the optical sensor that is capturing the optical emissions from the radiation process is a spectrometer 400 which results in the raw spectrum 401 showing integrated spectral intensity as a function of wavelength. In general any spectrum will have both background blackbody radiation features 402 as well as characteristic radiation features 403. It is possible to extract features from raw spectrum by taking the FFT and examining the peak heights in the FFT at some intermediate values of the inverse wavelength. For example the region of more rapid change in the wavelength domain peak 404 can correspond to a local maximum in the FFT 405 in the inverse wavelength domain. The slower the rise of the wavelength domain peak 404 would then correspond to a lower value of the corresponding FFT peak 405. This particular example could happen for example when the wavelength domain peak is caused by evaporation of given chemical species. The lower this evaporation, the lower the spectral intensity and the slower the rise towards the spectral peak because of the nature of the Gaussian fit of the spectral peak. This for example could happen when the energy coupling to the workpiece is optimal in the sense that energy is being absorbed, there is a stable liquid melt pool, and there is sufficient superheat to overcome the latent heat of melting for new powders being sintered but not so much superheat so as to cause excessive vaporization.

(9) Irrespective of how the features are derived, whether they are from the thermal sensor or the spectrometer, the classification scheme can be the same. First, the features associated with a baseline condition are identified as one set of data. Then the features from any given test case can be compared to the baseline condition as follows. First the features from the baseline case are averaged and a vector of the mean of these features M is created. The test vector X has the same dimensionality as the vector of feature means because it has the same number of features, which will be also called the degrees of freedom. Then a classification scheme as taught in this present invention involves the use of the Mahalanobis distance, which is simply given by:
MD.sup.2=[XM].sup.T.Math.COV.sub.X.Math.[XM](5)

(10) Where COV.sub.X is the covariance matrix of X. It can be shown that when the features are normally distributed, then the square of the MD distance will be Chi-Square distributed. The Chi Squared probability density distribution is given by:

(11) $\begin{array}{cc}f\left(x;k\right)=\{\begin{array}{cc}\frac{x^{\left(k\text{/}2\right)-1}{e}^{-x\text{/}2}}{2^{k\text{/}2}\left(\frac{k}{2}\right)},& x0;\\ 0,& \mathrm{otherwise}.\end{array}& \left(6\right)\end{array}$

(12) Where D is the Gamma Function and k is the number of degrees of freedom, which in this case is identical to the number of features. The critical value of the Chi-Squared distribution at a given confidence level and a given number of degrees of freedom can be calculated. This is a threshold value of the distribution above which a point could be considered as an outlier within the context of fitting the MD Distance t a Chi-Squared distribution. For example, at a 95% confidence level, or a critical p-value of 0.05, the corresponding table of critical values of the Chi-Squared distribution and therefore the MD distance squared as well are given by the following table:

(13) TABLE-US-00001 Degrees of Freedom (also Critical Value of the Chi-Squared the number of Features in Distribution - also critical value the Feature Vector) of the square of the MD distance 1 3.84 2 5.99 3 7.82 4 9.49 5 11.07 6 12.59 7 14.07 8 15.51 9 16.92 10 18.31

(14) The present invention provides a method of utilizing optical data through a variety of sensors as well as a variety of feature extraction techniques to enable the classification of nominal vs. off-nominal conditions found in a variety of manufacturing processes that involve the application of heat by a high energy or high temperature transient heat source.

(15) The present invention has been described in the context of various example embodiments. It will be understood that the above description is merely illustrative of the applications of the principles of the present invention, the scope of which is to be determined by the claims viewed in light of the specification. Other variants and modifications of the invention will be apparent to those of skill in the art.