Process Control and Quality Assurance Software for Additive Manufacturing

Transcription

3 About Sigma Labs Founded 2010: Located in Santa Fe, New Mexico, USA Pioneer of platform independent in-process quality monitoring technology for advanced manufacturing including 3DP Technology validated by industry leaders such as Honeywell Aerospace Expertise Material Science & Technology Division: Metallurgy Group: Los Alamos National Laboratory Commercial expertise: aero engine/frame & power gen components Material science, metallurgy, welding and joining expertise U.S. Patents No. 8,354,608: Method for Control of a Fusion Welding Process by Maintaining a Controlled Weld Pool Volume U.S. Patents Pending Optical Manufacturing Process Sensing and Status Indication System Method and System for Monitoring Additive Manufacturing Processes Multi-Sensor Lagrangian Quality Inference for Additive Manufacturing Six others focused in the area of statistical process control and quality assurance for additive manufacturing Copyright 2016, Sigma Labs, Inc. 3

4 Corporate Structure SIGMA LABS INC. IPQA Products Division AM Services Division SaaS - PrintRite3D Software - Statistical Process Control - Part Quality QaaS - Contract Printing - Digital Quality Record - Design & Metallurgy Copyright 2016, Sigma Labs, Inc. 4

6 Challenges for Metal AM 1. Statistical Process Control (SPC) a. Laser Power b. Laser Travel Speed c. Hatch Spacing d. Layer Thickness e. Powder Quality (e.g., PSD) f. Gas Flow 2. Part Quality a. Metallurgy b. Defect Detection c. Geometry d. Productivity Copyright 2016, Sigma Labs, Inc. 6

7 Solution In-Process Quality Assurance Measures attributes of a process that are predictive of post process results Provides Independent quality monitoring solution Evidence of compliance to design intent Certify your process is in control Predictive capability to identify quality problems Reduce post process inspection cost and increase production yield Capable of being installed on any manufacturers machine Copyright 2016, Sigma Labs, Inc. 7

9 Temperature PrintRite3D Software Provides for scan-by-scan, layer-by-layer and part-by-part quality measurements and assessments In-process Inspection of Metallurgical Properties Generates Digital Quality Record and Certificate based on thermal baselines Install hardware into printer Time Record temperature and geometry Multivariate statistical analysis Qualify and accept part Copyright 2016, Sigma Labs, Inc. 9

11 How PrintRite3D works Sensors Statistical Analysis Software Quality Reporting Collect Raw Data Feature Data Extraction Actionable Reporting Quality Metrics Calculated Every Layer Quality Metrics Feature Data Plots Copyright 2016, Sigma Labs, Inc. 11

21 IPQM Data - Photodiode Segments 1-6 & 8-11 (candidate) vs. Segment 7 (baseline) IPQM data at both the Process & Material Levels yield insight into the soundness of the deposit or part quality, aka QA With input from the Customer the Control Limit would be adjusted to represent a nominal process, aka SPC Copyright 2016, Sigma Labs, Inc. 21

23 Process Intervention - Failing Build Region in red flagged by PrintRite3D : Corresponds to location (z-height) in build where heavy recoater arm interaction occurred Copyright 2016, Sigma Labs, Inc. 23

28 IPQM Data - Pyrometer Segments 1-7 (candidate) vs. Asterisk Segments 7a & 8 (baseline) Segment a7 7a 8 IPQM data at the Process Level yields insight into the process health, aka SPC Baseline, aka Control Limit Copyright 2016, Sigma Labs, Inc. 28

29 IPQM Data - Pyrometer Segments 1-7 (candidate) vs. Asterisk Segments 7a & 8 (baseline) Segment a7 7a 8 IPQM data at the Material Level yields insight into the process health, aka SPC Baseline, aka Control Limit Copyright 2016, Sigma Labs, Inc. 29

30 IPQM Data - Photodiode Segments 1-7 (candidate) vs. Asterisk Segments 7a & 8 (baseline) IPQM data at both the Process & Material Levels yield insight into the soundness of the deposit or part quality, aka QA Segment a7 7a 8 With input from the Customer the Control Limit would be adjusted to represent a nominal process, aka SPC Copyright 2016, Sigma Labs, Inc. 30

31 IPQM Data - Photodiode 3D point cloud Segments 1-7 (candidate) vs. Asterisk Nominal Segments 7a & 8 (baseline) Isometric & Exploded View Segment 8 7a 7 6a Witness Coupon 1 Copyright 2016, Sigma Labs, Inc. 31

34 PrintRite3D CONTOUR Software for Real Time Monitoring of In Process Geometry 1. Is everything there? 2. Is anything missing? 3. Is anything extra? Engine Mount courtesy of Honeywell Aerospace In-process monitoring Compares as-built to asbuilt Provides real time quality metric to a known good standard Semi-quantitative go/no indicator 100 µm feature resolution In-process metrology Geometry measurement tool layer-by-layer Comparison of as-built to original CAD model Edge detection and correlation to physical measurement 3D roll up of all layers Copyright 2016, Sigma Labs, Inc. 34

37 CONTOUR vs. traditional NDI Feature Extraction Results Measures (Inches) Shoulder Width Top hole ID Top hole shoulder ID Base width Base ID Center nub to top of bracket Top hole centers Engineering Drawing OCMM CT CONTOUR TBD TBD TBD TBD TBD Copyright 2016, Sigma Labs, Inc. 37

39 PrintRite3D ANALYTICS Software for Manufacturing Intelligence Trend, Correlation, Exploration Ability to link and correlate all critical data: Build to build Machine to machine Location to location Management of and seamless access to all data (in-process and post process) over entire part lifecycle Data management Open explorative data mining Copyright 2016, Sigma Labs, Inc. 39

40 Contract AM Utilize the EOS M290 Build platform 250 x 250 x325 mm Engineering Materials Nickel, Steel and Cobalt Chrome alloys Aluminum based alloys Density levels Greater than 99.95% PrintRite3D in-process quality assurance Metallurgical and geometric property monitoring in real time during printing Copyright 2016, Sigma Labs, Inc. 40

41 Acknowledgements Many thanks to the engineering team at Sigma Labs including Scott Betts, Michael Brennan, Pete Campbell, Alberto Castro, Lars Jacquemetton, Glenn Wikle and Dr. R. Bruce Madigan of Montana Tech University. Copyright 2016, Sigma Labs, Inc. 41