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Obtaining Design Information Flow Pattern at Early Stage of the Product Development Process

Obtaining Design Information Flow Pattern at Early Stage of the Product Development Process. Qi Dong Research Advisor: D. E. Whitney Massachusetts Institute of Technology Oct. 17, 2000. Presentation Outline. Critiques on the Traditional DSM Method Research Objectives Past Research Method

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Obtaining Design Information Flow Pattern at Early Stage of the Product Development Process

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  1. Obtaining Design Information Flow Pattern at Early Stage of the Product Development Process Qi Dong Research Advisor: D. E. Whitney Massachusetts Institute of Technology Oct. 17, 2000

  2. Presentation Outline • Critiques on the Traditional DSM Method • Research Objectives • Past Research • Method • Case Study • Results of Case Study • Uniqueness of the Output Variable Selection • Relationship to Axiomatic Design • Relationship to Robust Design • Conclusions • Next Steps

  3. Critiques on the Traditional DSM Method • DSM’s can not be constructed until the detailed design is carried out, when the most important decisions about a design are already made. • The DSM interactions are obtained through interviews. The resulting DSM describes the as-is design process. • The proposed improvements from the traditional DSM study are based on the as-is process from a management point of view. • The traditional DSM study does not propose a better design concept or a should-be design process.

  4. Research Objectives • Let the product design requirements drive the interactions in the DSM’s. • Construct DSM in the early stage of the product design process before detailed work is assigned and resource commitment is made. • Use the results of the DSM studies to select the design concept with the simplest development process, plan the project and the budget at early stage of the design process.

  5. Past Research • Axiomatic Design (Suh)The Design Matrix relates Design Requirements to Design Parameters. FR = f (DP1, DP2, …) • Robust Design (Taguchi)The objective characteristics are functions of input, noise, and controllable parameters. Y = f(M, x1, x2,…) • Iteratively Solving Systems of Linear Equations (Steward, Strang, etc.)After choosing the output set for a system of linear equations, we can find the sequence of solving the equations through partitioning of the precedence matrix of the variables.

  6. Method • Construct a Design Matrix. • Select Output Variables.DP3 = f (FR1, DP1)DP1 = f (FR2, DP2)DP2 = f (FR3, DP3) • Permute the matrix by row so that the output variables are on the diagonal. We get a precedence matrix (DSM) of the Design Parameters.

  7. Case Study Veeco-CVC Electrostatic Chuck system integration for the PVD cold application. • Read the design documents and consult the design experts to record the relationship between requirements and design parameters in the Design Matrix (DM). • Select the diagonal elements in the Design Matrix as output variables. • Convert DM to DSM. • Interview the design engineers to verify the resulting DSM. • Partition the DSM to aid the integration work.

  8. Results of Case Study • The DSM converted from DM is very close to the actual situation. The revision in the DSM by the expert can be reflected in the DM correctly. This method of obtaining DSM is correct. • The DSM converted from the DM describes the should-be process, while the DSM obtained from interviews represents the as-is process. The ESC heat transfer problem describes this difference. R1 R2 Q”

  9. Uniqueness of the Output Variable Selection FR2.3.3 = b1 * DP2.3.3 + b2 * DP2.3.5 FR2.3.5 = b3 * DP2.3.3 + b4 * DP2.3.5 Where FR2.3.3 = provide on/off and magnitude control for the voltage FR2.3.5 = prevent the chamber RF power from affecting the electric network DP2.3.3 = electric circuit design for the voltage control DP2.3.5 = choke circuit

  10. Uniqueness of the Output Variable Selection (2) DFR2.3.3* = EOC1 * DDP2.3.3* + EOC2 * DDP2.3.5*DFR2.3.5* = EOC3 * DDP2.3.3* + EOC4 * DDP2.3.5* Where DFR2.3.3* = DFR2.3.3 / FR2.3.3,0 DFR2.3.5* = DFR2.3.5 / FR2.3.5,0 DDP2.3.3* = DDP2.3.3 / DP2.3.3,0 DDP2.3.5* = DDP2.3.5 / DP2.3.5,0

  11. Uniqueness of the Output Variable Selection (3) DFR2.3.3* = EOC1 * DDP2.3.3* + EOC2 * DDP2.3.5* DFR2.3.5* = EOC3 * DDP2.3.3* + EOC4 * DDP2.3.5* Where FR2.3.3 = provide on/off and magnitude control for the voltage FR2.3.5 = prevent the chamber RF power from affecting the electric network DP2.3.3 = electric circuit design for the voltage control DP2.3.5 = choke circuit

  12. Uniqueness of the Output Variable Selection (4)

  13. Uniqueness of the Output Variable Selection (5) • Mathematically, only when the values in the DSM are less than one, could the iteration have a chance to converge. Choosing non-diagonal elements as output variables results in DSM’s with values greater than 1, and hence the design iteration will never converge. • In practical sense, the values in the resulting DSM that are greater than 1 says the design parameter has to change more than 100% in order to compensate the change in other design parameter. This is impossible. Therefore, choosing non-diagonal elements is infeasible.

  14. Relationship to Axiomatic Design DM to DSM method • When the ideal Design in the Axiomatic Design theory is not practical, being able to obtain DSM in the early stage of the design allows us to use organization management method to go through the inevitable design iterations smoothly. • Making the diagonal elements in the DM dominant will make the iterations in the resulting DSM converge quickly. This is equivalent to saying the uncoupled design gives the best development process. • Making the above-diagonal elements in the DM less dominant is equivalent to making the design decoupled. Axiomatic Design Uncoupled Design Decoupled Design

  15. Relationship to Robust Design DM to DSM method Output Input Control Parameters Noise FR Diagonal DP Off-diagonal DP Noise Parameter Robust Design DM and DSM can include the noise parameter to present the broader system interface with the outside world. Minimizing the noise factors is to make the off-diagonal elements less dominant. Setting the control parameters is to take off some marks in the DSM by presetting their values to optimal. DSM proposes to make the design robust where iterations occur. The result of robust design is a simpler development process.

  16. Conclusions • At early stage of the product development process, we can correctly obtain a DSM from the relationship between the design requirements and the design parameters. • The DSM driven by the requirements represents a should-be process and hence proposes a better design process than the DSM obtained from interviews which describes the as-is process. • The DSM driven by the requirements is aligned with the spirit of Axiomatic Design and Robust Design and hence promote better product design. • The DSM driven by the requirements allow the organization management to contribute to Time-to-Market when the uncoupled or decoupled design cannot be achieved in practice.

  17. Next Steps • Constraints • How accurate is the prediction at the early stage? Follow up with the ESC case study.

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