OPTICAL MANUFACTURING PROCESS SENSING AND STATUS INDICATION SYSTEM

• US 20170016781A1
• Filed: 09/26/2016
• Published: 01/19/2017
• Est. Priority Date: 03/26/2014
• Status: Active Grant

• Current Assignee: Divergent Technologies, Inc.
• Original Assignee: Sigma Labs, Inc.
• Inventors: Madigan, Bruce, Dave, Vivek R., Cola, Mark J., Piltch, Martin S., Castro, Alberto
• Granted Patent: US 10,317,294 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B22F 10/25 Direct deposition of metal particles, e.g. direct metal deposition [DMD] or laser engineered net shaping [LENS]
• CPC Class Codes: B22F 10/28 Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
• CPC Class Codes: B22F 10/34 of powder characteristics, e.g. density, oxidation or flowability
• CPC Class Codes: B22F 12/90 Means for process control, e.g. cameras or sensors
• CPC Class Codes: B23K 10/027 Welding for purposes other than joining, e.g. build-up welding
• CPC Class Codes: B23K 15/0026 Auxiliary equipment
• CPC Class Codes: B23K 15/0086 welding for purposes other than joining, e.g. built-up welding
• CPC Class Codes: B23K 26/342 Build-up welding
• CPC Class Codes: B23K 26/70 Auxiliary operations or equipment
• CPC Class Codes: B23K 9/04 Welding for other purposes than joining, e.g. built-up welding
• CPC Class Codes: B23K 9/095 Monitoring or automatic control of welding parameters
• CPC Class Codes: B23K 9/0953 using computing means
• CPC Class Codes: B23K 9/0956 using sensing means, e.g. optical
• CPC Class Codes: B33Y 10/00 Processes of additive manufacturing
• CPC Class Codes: B33Y 30/00 Apparatus for additive manufacturing; Details thereof or accessories therefor
• CPC Class Codes: B33Y 50/00 Data acquisition or data processing for additive manufacturing
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01J 3/2889 Rapid scan spectrometers; Time resolved spectrometry
• CPC Class Codes: G01J 3/443 Emission spectrometry
• CPC Class Codes: G01K 13/00 Thermometers specially adapted for specific purposes
• CPC Class Codes: G01N 21/71 : thermally excited
• CPC Class Codes: Y02P 10/25 : Process efficiency

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:

(a) one or more optical sensors configured to measure the optical emissions from the manufacturing process over a range of wavelengths from 200 nanometers to 1000 nanometers, and produce time-domain optical data;

(b) one or more thermal sensors configured to measure thermal phenomena at two different characteristic timescales, a first timescale associated with the application of heat into the manufacturing process by an external heat source and a second timescale associated with the material response to the application of heat within the manufacturing process by the external heat source, and produce time-domain thermal data;

(c) an analysis system configured to provide

(c1) a first feature extraction process that extracts, from the time domain optical and thermal data, features that are related to the heating rate, cooling rate and peak temperature of the thermal cycles associated with the application of heat into the manufacturing process by an external heat source;

(c2) a second feature extraction process that extracts, from the time domain optical and thermal data, features that are related to the heating rate, cooling rate and peak temperature of the thermal cycle associated with the material response to the external heat input to the manufacturing process;

(c3) 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;

(d) 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. A system as 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. A system as in claim 1, wherein the manufacturing process is an additive manufacturing process.

4. A system as in claim 3, wherein the manufacturing process comprises one or more of:

a bed of powdered metals that are sintered or melted by a scanning laser;

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. A system as in claim 1, 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.

6. A system as in claim 5, 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.

7. A system as in claim 1, wherein the thermal sensor comprises a single or multiple wavelength pyrometer.

8. A system as in claim 1, wherein the thermal sensor comprises a thermocouple.

9. A system as in claim 1, wherein the thermal sensor comprises a Resistance Thermal Device.

10. A system as in claim 1, where the classification system uses features from the first time scale to determine if the heat addition and heat source characteristics associated with the two different process conditions are significantly different or largely similar.

11. A system as in claim 1, where the classification system uses features from the second timescale to determine if the material response associated with the two different process conditions are significantly different or largely similar.

12. A manufacturing process sensing and status indication system comprising:

(a) one or more optical sensors measuring the optical emissions from the manufacturing process over a range of wavelengths from 200 nanometers to 1000 nanometers;

(b) a spectrometer configured to determine the relative intensity of light at various wavelengths over a given integration time;

(c) an analysis system configured to provide

(c1) a first feature extraction process that extracts, from the time domain optical and thermal data, features that are related to the heating rate, cooling rate and peak temperature of the thermal cycles associated with the application of heat into the manufacturing process by an external heat source;

(c2) a second feature extraction process that extracts, from the time domain optical and thermal data, features that are related to the heating rate, cooling rate and peak temperature of the thermal cycle associated with the material response to the external heat input to the manufacturing process;

(c3) 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;

(d) 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. A system as 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. A system as in claim 12, wherein the manufacturing process is an additive manufacturing process.

15. A system as in claim 14, wherein the manufacturing process comprises one or more of:

a bed of powdered metals that are sintered or melted by a scanning laser;

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. A system as 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. A system as 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.

18. A system as in claim 12, at least one of the first and second feature extraction processes comprises:

(i) determining intensity vs. wavelength of the spectral data;

(ii) normalizing the spectral data by either the highest peak height or a suitable peak height elsewhere in the spectra should the highest peak height exhibit signal saturation for the given integration time, calculating a Fast Fourier Transform of the spectral data thus normalized, determining an intermediate relative maximum in this FFT, and using the intermediate relative maximum as a discriminator of the coupling of the heat source to the workpiece.

19. The system of claim 18 where the presence of a local minimum in the plot of FFT intensity as a function of incident power fluence as measured in power per unit area is used as an indicator of a more optimal coupling of incident energy to the workpiece.