Advanced Topics in Automotive Plastics — Computational Methods
The cutting-edge companion to the flagship: ML surrogates, reinforcement-learning optimization, uncertainty quantification, conformal cooling, and multiscale computational materials science.
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Faculty
Faculty details for this seminar will be announced with the full schedule.
Fees
Early: $1,895 (payment 4+ weeks ahead)
Standard: $2,095 (check/ACH) · $2,165 (card)
Group discount: $200 off per attendee for 3+ from the same organization.
Also Available
- Corporate on-site delivery at your facility
- Private cohort sessions
- Digital curriculum licensing
Seminar Overview
The cutting-edge companion to the flagship seminar: machine-learning surrogates (17x faster simulation), deep reinforcement-learning optimization (135x speedups), uncertainty quantification, conformal cooling (90.5% warpage reduction), multiscale computational materials science, ML materials characterization, and polymer degradation and sustainability.
Built from 150+ reviewed research papers with industrial validation, this advanced curriculum is delivered as a hybrid of lecture and hands-on computational workshops. Prerequisite: completion of the Automotive Plastics Part Design seminar or equivalent experience.
Ideal Learner
- Alumni of the Automotive Plastics Part Design seminar advancing to computational methods
- CAE and simulation engineers working with injection-molded automotive parts
- R&D engineers evaluating AI/ML methods for materials and process optimization
Learning Objectives
- Apply ML surrogate models to accelerate plastics simulation workflows
- Formulate reinforcement-learning optimization for design and process parameters
- Quantify uncertainty in simulation-driven design decisions
- Evaluate conformal cooling and advanced tooling strategies with simulation evidence
- Assess polymer degradation and sustainability trade-offs computationally
Consulting Sessions
Seminar attendees can sign up for individual consulting sessions with the instructor. Sessions are free for registered attendees, first-come first-served — sign up when registering by calling 248-539-0473 or during the seminar.
Seminar Outline
- Surrogate model fundamentals
- 17x speedup case studies
- Validation practice
- Deep reinforcement learning for design
- 135x optimization case study
- Process parameter search
- Uncertainty quantification
- Multiscale computational materials science
- ML materials characterization
- Conformal cooling (90.5% warpage reduction case)
- Polymer degradation modeling
- Sustainability computation
More in the Flagship
- FS-01 · Automotive Plastics Part Design — 3-day · Intermediate