Manufacturing AI training
Process optimization
Learn the data-driven method for combining production variables with AI models to optimize quality, yield, and energy consumption at the same time.
What is process optimization?
Process optimization combines production variables such as temperature, pressure, speed, and mix ratio with AI models to optimize quality, yield, and energy consumption together. Instead of running experiment after experiment, metamodels and optimization algorithms find the best conditions quickly.
Far fewer experiments
Design of experiments and metamodels predict the whole design space from a minimum number of runs, so trial and error shrinks.
Multi-objective optimization
MOGA weighs competing goals such as quality, cost, and energy at once and returns the Pareto-optimal set.
Knowledge that accumulates on its own
Optimization history is stored as data, so earlier results stay reusable when staff change or the process is revised.
What you'll learn
The core material this course covers.
- Design of experiments (DOE) fundamentals
- Metamodels (surrogate models) and process prediction models
- Optimization algorithms (MOGA, local and global)
- Multi-objective optimization in practice
- The cycle of collecting data, updating the model, and re-deriving optimal conditions
- Pareto analysis for energy savings and yield improvement
Curriculum
What we cover
- 1
Introduction to process optimization
Look at where the PDCA approach runs out of road, then cover data-driven optimization and the kinds of processes it suits.
- 2
Hands-on design of experiments
Compare full factorial and Latin hypercube sampling, and practice the design principles that extract the most information from the fewest runs.
- 3
Building metamodels
Learn how regression, interpolation, and ML metamodels differ, then judge model reliability with cross-validation.
- 4
Hands-on multi-objective optimization
Understand how MOGA works, read a Pareto front, and generate the decision-ready report automatically.
- 5
Taking results to the floor
Learn how to hand over the optimal parameters, set monitoring metrics, and run the optimization cycle repeatedly.
How teams put this to use
Automating engineering simulation work
Automatic sampling and metamodels cut simulation work that took days down to minutes, widening the design space that could be explored.
Integrated design optimization for a UPS inverter
Multi-objective optimization tuned electrical efficiency and thermal performance together, locating the balance point between them with data.
Thermal design optimization for an automotive battery module
Sensitivity analysis of design variables led to an optimal arrangement that improved thermal performance and space efficiency together.
Expected outcomes
What changes once your team puts this to work on site.
- Fewer repeated experiments cut R&D time and cost.
- Optimal conditions come out of the data, without deep specialist knowledge.
- Multi-objective optimization makes the quality, cost, and energy tradeoff explicit.
- Optimization history accumulates automatically into an organizational asset.
Process optimization — FAQ
Yes. A metamodel can be built from plant experiment data alone. CAE is used for simulation-based optimization, but for processes driven by sensor and measurement data, Python and statistical tools are often enough.
The Pareto front does not choose for you; the answer depends on which objective matters most. The course covers how to compare each solution quantitatively and a decision framework for weighting objectives by business priority to narrow down the final choice.
We'll design the training around your operation
Schedule, group size, and curriculum detail all get set with you.