OpenLMD, Multimodal Monitoring and Control of LMD processing

OpenLMD, Multimodal Monitoring and Control of LMD processing

Jorge Rodríguez-AraújoAIMEN Technology Center, Porriño, Spain

Photonics WEST 2017, 1-2-2017

openlmd.github.io | [email protected] 2

Index

Index

1. Laser Metal Deposition

2. Motivation and innovative character

3. Open Laser Metal Deposition

4. ROS-based robot cell integration

5. Multimodal monitoring and virtualization

6. Image registration and data acquisition

7. Closed-loop laser power control

8. 3D geometrical monitoring

9. Adaptive LMD path planning

10.Conclusions and future work


Laser Metal Deposition (LMD) Promising additive manufacturing technique

Parts are built up layer by layer directly from a 3D CAD model

The material is directly deposited on the previous surface

For repair and direct fabrication of pieces

Near-net-shape (close to the final shape)

Manufacturing of large metallic parts

LMD issues Complex setup and adjustment of parameters

Time consuming robot programming for repairing tasks

Thermal heating accumulation and dimensional distortion

Repeatability, rising defects, and metalurllical properties

Traditional off-line process (with constant parameters) becomes unsucessful for large metallic parts

LMD, Laser Metal Deposition


Motivation and innovative character

MotionController

Off-line Path Programming

6-AxisRobot Laser Powder

Feeder

PowerController

FlowController

Motivation Lots of industrial robotized laser cells

Mostly manually operated

Lack of integration for monitoring and control

Innovation Retrofit current laser cladding facilities for LMD

Empower robotized laser cells for effective AM

Apply state of the art robotic software solutions

Goals Build a modular architecture for LMD full automation

Reduce heating accumulation for large parts

Adapt robot programming to the real part

Increase geometry accuracy and repeatability

Conventional Robotized Laser Cladding Cell


Open Laser Metal Depositon

Concept and approach Open-source solution for on-line multimodal monitoring and control of LMD

Modular set of software components. Built on ROS (Robot Operating System)

Robotics, machine vision, embedded control, machine learning, big data

Full compatible with current robotized laser cladding cells

Focus on interoperability and standardization

ROS-based architecture Multiprocessing architecture based on message publishing

Multi-node and multi-machine

Modular (e.g. robot, laser, camera)

Synchronized data acquisition (common timestamp)

High bandwidth data management (i.e. images)

Visualization tools and components (e.g. rviz)

Advanced robotics environment


ROS-based robot cell integration

PowderFeeder

Fiber Laser

6-Axis Industrial Robot

PC Controller

Cladding Head

ROS-Driver(ABB Rapid)

Geometrical Cell Description

(URDF)

STATEPUBLISHER

Laser Source(slave)

Powder Feeder(slave)

COMMANDSERVER

PowerSpeed

Powder flow

Motion path

States

Commands

ROBOT

Process parameters

AIMEN’s LMD robotized laser cell

ROS-based integration of modular laser cells The PC commands the robot integrating interfaces and modules with ROS

The robot controls all the cell elements

ROS components for robot integration Geometrical description (URDF)

ROS driver


Multimodal monitoring and virtualization

Multimodal Cladding Head

3D System

TachyonMWIR

NIR

MWIR+NIRMultispectral

Imaging

LMD Cell Virtualization

Multimodal monitoring approach Coaxial SWIR/MWIR images (thermal monitoring): NIT microcore (1000fps) [1-3um]

Coaxial NIR images (surface monitoring): CMOS camera (100fps) [830-880nm]

Off-axis 3D system: on-line 3D point cloud scanning (50fps)


Coaxial sensors registration Image registration: process of transforming different sets of data into one coordinate system

Data acquisition High throughput (28MB/s)

NIR + MWIR + 3D point cloud + robot

Data management and analysis

Bag files and Pandas DataFrames

NIT NIR(0, 0)

velx

y

Image registration and data acquisition

Calibration → Projection


Closed-loop laser power control High speed SWIR/MWIR thermal meltpool monitoring (1000fps)

NIT Europe Tachyon 1024 microcore camera

Meltpool geometrical monitoring (elliptical approximation)

Increased geometry repeatability and reduced dilution and heat accumulation

Closed-loop laser power control

Meltpool, ellipse approach

Wid

th (m

m)

Time (s)


3D triangulation

workingtable

nozzle

3D geometrical monitoring

Industrial Robotic Laser Cell

ROBOTROS-DRIVER

CAMERAIDS-DRIVER

State Publisher Peak Finder

Robot PoseTool-Camera

Laser TriangulationCalibration

3D ProfileCamera Pose

3D Point CloudWorking Cell Coordinate

On-line 3D geometrical reconstruction

On-line 3D point cloud registration Real-time point cloud registration

Actual metric measurement (mm)

Direct acquisition in robot coordinates


Adaptive LMD path planning

Adapts the path to the real geometry 3D vision guided full automated laser cladding repair of complex metallic parts

Automatic generation of robot trajectories

1. Part scanning and filtering

2. Surface selection and path generation

3. Repair job generation and supervision

3D Filtering

Initialization(setup)

Scan layer

Depth mapTargetDepth map

Disparity

Data

Layer pathplanning

Layer Path Planning(geometrical control)

Laser Cellsupervisor

Robotized Cell

0 Finished

Repair Job

3D geometrical control

Coated surface


Automatic coating of surfaces

3D scanning and robot path planningEnabled by the 3D point cloud directly provided by the

3D geometrical monitoring solution

1. Workarea scanning (direct part information in

1. cell coordinates) [mm]

2. 3D point cloud projection (2D Zmap image)

3. Surface selection directly in the 2D image

4. Segmentation of the Zmap image

5. Contours calculation from the segmented surface

6. Contours and Zmap feed the path planner

7. A new path is automatically calculated from that

1. information

A second scanning after the process enables an

adaptive path planning strategy in a full automatic way

Surface selection

3D scanned surfaces


Conclusions Integration and data acquisition

Spatial reference system and temporally synchronized

Monitoring and control

Real-time closed-loop laser power control

Adaptive path planning

Work in-progress Embedded image monitoring and control

Big data and deep learning approaches

Data acquisition High throughput (28MB/s)

NIR + MWIR + 3D point cloud + robot

Data management and analysis

Bag files and Pandas DataFrames

Robot

Pose Process speed

3D geometry

Point cloud (<0.5mm)

SWIR/MWIR-NIR

2D melt pool geometry

Thermal distribution and texture

Conclusions and future work

Reconfigurable

Modular and reconfigurable

Interoperability

Large parts

Low-cost solution

Scalability

AIMEN – Central y Laboratoriosc/ Relva 27 A

36410 – O PORRIÑO (Pontevedra)Telf.+34 986 344 000 – Fax. +34 986 337 302

Thank you for your attention

Jorge Rodríguez-Araújo | Research EngineerPh +34 986 344 000 | [email protected]

www.aimen.es | [email protected]

This work has receive funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 637081. The dissemination of results herein reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains.

http://openlmd.github.io

mailto:[email protected]

http://openlmd.github.io/

OpenLMD, Multimodal Monitoring and Control of LMD processing

Technology

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