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Coverage Path Planning for Thermal Interface Materials

Simon Baeuerle, Andreas Steimer, Ralf Mikut

TL;DR

This work presents a TIM-specific coverage path planning framework that optimizes a parameterized dispense path using CMA-ES integrated with a 2D flow-based process model. The objective combines coverage accuracy, overflow minimization, and void avoidance through a multi-term, tunable loss with area- and cell-wise weighting, enabling feasible patterns under manufacturing constraints. Validated on four existing automotive products and a new product, the approach yields higher TIM coverage, reduced material waste, and continuous dispense paths, and is demonstrated on actual series manufacturing equipment. The framework is transferable to other dispensing tasks and lays groundwork for enhancements such as alternate flow models and gradient-based optimization to reduce compute time.

Abstract

Thermal management of power electronics and Electronic Control Units is crucial in times of increasing power densities and limited assembly space. Electric and autonomous vehicles are a prominent application field. Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined. To plan this application path, design engineers typically perform an iterative trial-and-error procedure of elaborate simulations and manual experiments. We propose a fully automated optimization approach, which clearly outperforms the current manual path planning and respects all relevant manufacturing constraints. An optimum dispense path increases the reliability of the thermal interface and makes the manufacturing more sustainable by reducing material waste. We show results on multiple real products from automotive series production, including an experimental validation on actual series manufacturing equipment.

Coverage Path Planning for Thermal Interface Materials

TL;DR

This work presents a TIM-specific coverage path planning framework that optimizes a parameterized dispense path using CMA-ES integrated with a 2D flow-based process model. The objective combines coverage accuracy, overflow minimization, and void avoidance through a multi-term, tunable loss with area- and cell-wise weighting, enabling feasible patterns under manufacturing constraints. Validated on four existing automotive products and a new product, the approach yields higher TIM coverage, reduced material waste, and continuous dispense paths, and is demonstrated on actual series manufacturing equipment. The framework is transferable to other dispensing tasks and lays groundwork for enhancements such as alternate flow models and gradient-based optimization to reduce compute time.

Abstract

Thermal management of power electronics and Electronic Control Units is crucial in times of increasing power densities and limited assembly space. Electric and autonomous vehicles are a prominent application field. Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined. To plan this application path, design engineers typically perform an iterative trial-and-error procedure of elaborate simulations and manual experiments. We propose a fully automated optimization approach, which clearly outperforms the current manual path planning and respects all relevant manufacturing constraints. An optimum dispense path increases the reliability of the thermal interface and makes the manufacturing more sustainable by reducing material waste. We show results on multiple real products from automotive series production, including an experimental validation on actual series manufacturing equipment.
Paper Structure (21 sections, 3 equations, 12 figures, 2 tables, 3 algorithms)

This paper contains 21 sections, 3 equations, 12 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Methodology
  4. Optimizer and process model
  5. Shaping of objective function
  6. Coverage area evaluation
  7. Initial overflow evaluation
  8. Initial and intermediate void evaluation
  9. Objective function evaluation
  10. Experimental Setup
  11. Evaluation on products A-D (existing products)
  12. Evaluation on product E (new product)
  13. Configuration of objective function
  14. Compliance with mechanical tolerances
  15. RESULTS
  16. ...and 6 more sections

Figures (12)

  • Figure 1: Area coverage types during Coverage Path Planning problem settings. Most approaches consider a constant path width.
  • Figure 2: Overall approach: optimizer interacting with process model
  • Figure 3: Detailed workflow with intermediate in- and outputs
  • Figure 4: Split of overall target area in three individual target area types. $M_{target,cool}$ (green color) defines the cooling area, $M_{target,over}$ (white and red color) the non-cooling area and $M_{target,tab}$ (red color) the taboo zones. In each greyscale image, the white color hue indicates the respective spatial location belonging to the specified target area type.
  • Figure 5: Spatial weighting of target area. The weighting factors are specified for each grid cell. They increase proportionally to the distance between the respective grid cell and the reference area.
  • ...and 7 more figures