Engineering Design Problem: When we design we would like to:
 Improve product or process quality
 Reduce life cycle costs
 Reduce development lead times
This can happen during the initial design phase or sometime during the products life.
The design process starts by generating ideas  the functional performance of these ideas have to be verified.
There are two ways to verify an ideas functional performance:
 Physical testing of a prototype or the final product or process.
 Prediction of the performance of the product or process.
When to measure and when to simulate?
What is the best choice? Generally the answer depends on the system, its behaviour, and the required results.
As a general guideline: Simulate what is easy to simulate and measure what is easy to measure!
Generally it is better to simulate when:
 Evaluating concepts  before prototypes  are available.
 Many different or very long load cases have to be evaluated.
 Required outputs are difficult or impossible to obtain.
 Measurement will be very expensive (land mine tests).
Simulation Can Assist During:
Initial product development (no product or prototype is available)
Allows designer to:
 Make well founded decisions early in product development process.
 Test more ideas in shorter time.
 Understand how product behaviour is affected by different factors.
 Discover unexpected behaviour.
 Be sure product fulfills demands.
Continual product Improvement (prototype or product is available)
 Optimise design solutions for next product generation.
 Estimate product sensitivity to changes in e.g. weather, wear, ageing.
 Test dangerous situations.
 Understand product behaviour and factors that affect the behaviour at controlled repeated conditions.
Analysis Paralysis?
Model definition might be according to Neelamkavil: "A model is a simplified representation of a system (or process or theory) intended to enhance our ability to understand, predict, and possibly control the behaviour of the system".
For testing or simulation one of two approaches can be taken:
 Trail and error
 This method is not well defined and can continue forever without providing any indication as to how close the design is to an optimal value.
 Testing program e.g. DOE study
 More structured approach that gives the designer much more insight into how the design behaves.
MBD Simulation Methodology
 Problem formulation
 Definition of idealized model
 Development of computer model
 Formulation of system equations
 Equation solving
 Results and post processing
 Evaluation and conclusion
1. Simulation Methodology: Problem formulation, describe:
 Technical problem to be solved
 Physical effects to include
 Limitations of system
 System components
 Targets to achieve (force, torque, displacement..)
 Load cases
 Bodies
 Inverse Dynamics
 Ideal constraints
 Robust design
 Model flexibility
 Forward Dynamics
 Model improvements
 Door damper/closer
 Automation
Design door
 Size: 0.6x2.0x0.04 (from ergonomic data)
 It must be comfortably operated by a standard human (see later for human force values).
 Door mechanism must be designed for infinite life.
 Design must be robust e.g. not fail under limit manufacturing tolerances Automation.
Door V2
 Optimise door design
 Door addons
 Damper
 Opening mechanism
 Own mass
 Opening Forces
 Extra accessories
 Manufacturing tolerances
 Misuse cases
2. MBD Simulation Methodology: Definition of idealized model
Collect relevant system data:
 Parts
 Component couplings (interactions between parts)
Model parameters
 Mass, centers of gyration, damping, stiffness
3. MBD Simulation Methodology: Development of computer model Driven by:
 Access to information
 Required results and accuracy
 Allowed simplifications
 Available resources (time and money)
 Model complexity
 Static, Quasi static, Dynamic
 1D ,2D or 3D computer model
 Available modeling methods
 Gravity
 Rigid bodies
 Ideal joints
 Stiffness elements
 Damping elements
 General force components
 Contact
 Advanced elements (tyres, flexible bodies, ..)
Creating models in Adams/View
Location and Orientation
 Points (Parametric): Location only
 Markers: Location and orientation
Specifying Location and Orientation in Adams
 Location: specify in global or local coordinates
 Orientation: specify in global or local coordinates
 Along Axis: Zaxis along line of two points, arbitrary rotation.
 In Plane: Zaxis along line of first two points, third point locates zxplane.
 Good for concept evaluation

 Pendulum with out link

 Next step after acceptable results create CAD

 Pendulum with link

 All Parts (except ground part) has:
 Initial position and orientation
 Part mass & inertia
 Mass and inertia reference
 Initial velocities
 Degrees of freedom
 Governed by laws of mechanics
Moment of Inertia Experiment
Participants:
 Blue > Solid Cylinder
 Green > Hollow cylinder
 Brown > Solid Sphere
 Red > Hollow Sphere
 Rolling radius of all participants equal
 Mass for all participants are equal
 All participants roll without slipping
Constraints
Constraint equations in Adams
 Constraints are represented as algebraic equations in Adams/Solver.
 These equations describe the relationship between two markers.
 Joint parameters, referred to as I and J markers, define the location, orientation, and the connecting parts:
 First marker, I, is fixed to the first part.
 Second marker, J, is fixed to the second part.
Ideal Constraints in Adams
Joint Primitives in Adams
5. MBD Simulation Methodology: Equation solving
 Parameters:
 Solver type
 Stiff methods (Implicit backwards difference formulations)
 Non stiff methods (Explicit forwards difference formulations)
 Step size
 Error
 Simulation duration
6. MBD Simulation Methodology: Results and post processing
 Problem dependent
 Available tools
 Time domain plots
 Frequency domain plots
 Animations
 Data export
3. Design Example: Inverse Dynamics
Solve kinematic equations but calculate forces required to do so.
Use motions
 Joint motion
 Point motion
Measures: Your Virtual instrumentation
 Predefined measures
 Object
 PointtoPoint
 Included angle
 Orientation
 Range
 User defined
 Adams/view Computed
 Adams/Solver function
7. MBD Simulation Methodology: Evaluation and conclusion
Compare system behaviour with initial problem formulation
 Problem solved: continue to next step in development process
 Problem not solved: Find what is wrong, update and iterate MBS procedure.
 Unsatisfactory design
 Model does not exhibit required behaviour
Evaluate certainty of results
 Assumptions
 Numerical approximation
Results Discussion
 Measures in local coordinate systems
 Redundant constraints: BAD! Why?
 Indeterminate structure
 Alternate constraint configuration
4. Design Example: Robust Design
 Manufacturing tolerance simulation
Rotate hinge 0.1deg
Forces
 Forces for engineers:
 Body forces
 Gravity
 Electromagnetic forces
 Aerodynamic forces
 Buoyancy
 Lift
 Drag
 Thrust
 Contact or normal forces
 Friction forces
 Damping forces
 Compliant forces (elastic)
 Applied forces (push, pull, torque)
 Fictions forces
 Coriolis force
 Centrifugal force
 Body forces
Bushing
Adds linear flexibility
Flexibility: Where should the flexibility be added?
Results
Discussion
 Model not over constrained anymore.
 Forces increase with increasing angle of bottom joint.
 Forces increase when distance between joints reduce but effect of tolerance reduces?
5. Design Example: Forward Dynamics
Can a human open the door?
Applied Forces:
 Single Component Force
 Single Component Torque
 Torque Vector
 Force Vector
 General Force
Must select, components, run time direction, and action and reaction bodies.
6. Design Example: Model Improvements
 Improve constraint model
 Add play
 Torque vs axial reaction forces
 Add some way of stopping the door when it is open and closed
 Sensor
 Stop torque
 Point to plane contact
 Load prediction for finite element calculation
 Add door damper to automatically close door