Precision-Driven Hybrid Control for 3D Microassembly Dan O Popa - PowerPoint PPT Presentation

Precision-Driven Hybrid Control for 3D Microassembly Dan O Popa Aditya N Das Dan O. Popa, Aditya N. Das Texas Microfactory Texas Microfactory Automation & Robotics Research Institute University of Texas at Arlington, Texas, USA www.arri.uta.edu/popa , popa@uta.edu /p p , p p @ Presentation at ICRA 2009, Kobe, Japan, May 17, 2009 p y

Texas Microfactory at ARRI DFW TM TM from concept to production from concept to production ARRI ARRI

Texas Microfactory Research Program: Smart Micromachines NEXT GENERATION ROBOTICS Humanoids | Precision Microrobotics | Distributed Robotics SMART MICROMACHINES Smart Materials | Smart Devices | Smart Networks MICROMANUFACTURING Packaging | Assembly | Testing | Reliability

Texas Microfactory Technical Focus: Robotics and Microengineering g g μ m achines μ m achines μ devices μ engineering Robotics μν robotics Precision Design Modularity y Packaging Packaging I nteractivity Materials Sensors, Actuators , Pow er Scavengers Hum anoids Structural Health Managem ent Biom im etic Microrobotic Sw arm s

Outline of Presentation • Introduction/ Motivation: Microassembly with Precision Robotics • Hybrid 3D Microassembly and a few applications • Precision Robotics: precision metrics • Yield guarantees and precision metrics: HYAC Condition and RRA Rules • Hybrid Control for hybrid microassembly • Microassembly Systems (Physical and Virtual) bl h l d l • Proposed Hybrid Controller and Path Planner • Simuation and Experimental Results i i d i l l • Conclusion and Future Work

MicroAssembly As a Manufacturing Method Probes on manual stages Manual Serial May use multiscale, multirobot Teleoperated platforms with microgrippers platforms with microgrippers. Vary in throughout and yield. Automated Microgripper arrays Deterministic Flip Chip Bonding/Stacking Parallel Self assembly Stochastic Distributed Array Deterministic assembly: - Weaknesses: expensive equipment, slow, not scalable to many parts - Strengths: can build complex structures can obtain high yield by “tweaking” performance - Strengths: can build complex structures, can obtain high yield by tweaking performance Stochastic assembly: - Weaknesses: only simple assemblies, high yield takes a long time - Strengths: scalable to many parts, simple equipment This presentation proposes a precision-adjusted path-planning and hybrid control strategy to improve the This presentation proposes a precision adjusted path planning and hybrid control strategy to improve the yield and speed of serial, automated microassembly.

MicroAssembly: Context Deterministic Deterministic Stochastic Stochastic Assembly of small components is difficult : Position/process • Top-down Serial interdependency • Some scaling of physics S li f h i • Stringent tolerance Parallel • High precision requirements for equipment Self assembly Bottom-up • Time sensitive • Limited sensing and dexterity • Dynamical effects causing 10 -2 10 -3 10 -4 10 -5 10 -6 10 -7 10 -8 10 -9 vibrations in meters D.O. Popa, H. Stephanou, “Micro and Effect of gravitational forces meso scale robotic assembly”, in SME Journal of Manufacturing Processes , Vol. Effect of surface forces 6, No.1, 2004, 52 ‐ 71. Ki Kinematic/Dynamic control i /D i l Force Control F C l Assembly time Predictability (yield)

Motivation: Automated 3D Microassembly Automated , Serial or Parallel Hybrid Microassembly is a viable pathway for mass production of microsystems with modular component dimensions above ?0 microns. Automated Deterministic Microassembly is difficult and requires ff careful consideration of several important tradeoffs: i. Serial microassembly is slow but can have high yield and construct complex 3D shapes for part dimensions above ?0 microns. ii. Open-loop control with calibration or models can be used, but: i. If it relies on high repeatability it usually leads to both expensive and bulky hardware. ii. ii If it relies on compliance then it must be engineered into If it relies on compliance then it must be engineered into parts and end-effectors. iii. Closed-loop feedback control can provide higher precision but: i. Usage in quasi-static operation decreases throughput (“look and move”). (“ ) ii. Usage in dynamic operation leads to vibrations which are of higher frequency than the sensor or control system bandwidth. iii. Workspace is limited, this applies to sensors , actuators and end-effectors sharing the workspace. Examples of 3D microassembly from Texas Microfactory

Our Approach: Maximize Assembly Yield, then Speed, then Cost Key factors driving the assembly yield at small scales: � Precision requirements (how accurate the robots are) � Tolerance requirements (how accurate the parts are) � Throughput requirements (how fast we want to assemble) � Interaction forces between parts (how parts interact) � Sensory vs. sensorless ability (what kind of sensors do we have available) � Part design (are the parts “assemblable”?). The goal of our work is to formulate a framework with quantitative metrics and decision rules for The goal of our work is to formulate a framework with quantitative metrics and decision rules for microassembly cells: ‐ Design rules – how many robots, heteroceptive sensors, their specs, etc. ‐ Hybrid Control rules – when to switch between controllers ‐ Path Planning rules – which paths to follow to increase precision Path Planning rules which paths to follow to increase precision Our approach i. High yield assembly guarantees from “new” precision metrics ii. Precision adjusted hybrid control for faster throughput through a “complexity index” j y g p g p y iii. “Precise” path planning algorithms to reduce sensor cost and ensure higher precision iv. Complex 3D simulation of microassembly in a virtual world with high realism for planning and validation before being ported to the assembly system.

Background: making small things with automated machines • Mi Micro/nano manufacturing consists of a set of processes used to fabricate features, components, or systems with / f t i i t f t f d t f b i t f t t t ith dimensions described in micrometers or nanometers. • In any endeavor where small things are made, characterized, assembled, tested, or used, it is necessary to apply the fundamental practices of precision engineering . However, these practices are rooted in pre ‐ 20 ‐ th century technology. • Internationally, metrology standards are set by ISO (International Organization for Standardization). In the USA standards are set by NIST and ANSI. They have set precision standards for automated machines, including robots robots. • CNC machine tool industry have applied these concepts routinely, but traditionally, their precision relies on the mechanical structure of the system making them very bulky. • Intelligent Robotics offers the possibility to reduce the size of micro/nano manufacturing systems, but precision Intelligent Robotics offers the possibility to reduce the size of micro/nano manufacturing systems but precision concepts and standards in robotics are rarely followed. As a 21 th century technology, Microrobotics needs new precision concepts beyond the 19 th century ones. • • We propose a new precision framework for top ‐ down robotic assembly systems with guaranteed high yields, We propose a new precision framework for top down robotic assembly systems with guaranteed high yields, reasonable high speeds, and reasonable low cost. • Presentation argues that this will require a new set of robotics ‐ centric precision metrics, other than the conventional definitions of accuracy, repeatability, resolution.

Conventional Precision Metrics Resolution: The smallest output increment that a machine can perform. good Repeatability: The ability of a machine to return to the same state over accuracy many cycling attempts. Poor repeatability Accuracy: The maximum expected difference between the actual and the ideal (desired) output for a given input In Metrology, Resolution and Accuracy are mean values, while gy, y , Repeatability is a statistical distribution. poor accuracy Outputs vary, and examples could be, motion (position), measurement good (distance, angle, temperature, intensity, etc), parts produced (tolerance of repeatability parts). For Precision Robotics, these metrics should : • All be statistical variables following Gaussian Probability Density good Functions. accuracy • Be metrics related to the end ‐ effector, as often times they are confused good with similar metrics at other points of measurement. repeatability • Be redefined to closely match the type of controller sensor and actuator Be redefined to closely match the type of controller, sensor and actuator system used.