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MULTIOBJECTIVE OPTIMIZATION of a skip lifting system

A 60' webinar that highlights how to optimize skip performance while saving tremendously on time

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Join to learn how you can

easily create an automated engineering workflow, using Optimus to encapsulate MapleSim's advanced modeling and simulation capabilities. In doing so, Optimus automatically recognizes all design parameters and system response variables available in MapleSim.

build and validate meta-models that confidently model system response in all design regions of interest, enabling a truly multiobjective optimization within tight time constraints.

perform a sensitivity analysis on the model, to quickly eliminate the design parameters that have limited impact on system response.

select the best out of the good, applying decision filters to all Pareto-optimal solutions identified by the multiobjective optimization process.

Using a concrete example of a skip lifting system used in a Dual-Truck Mobile Sizer (DTMS) machine, we will show how Optimus, id8 decide & MapleSim enable engineering teams to decide on the best design, saving days of computations.

Looking for more in-depth information?

MapleSim is used to set up a fully parameterized model of the skip, including all its key components (geometry, mechanical operation, hydraulic circuits and controllers, ...).

Optimus integrates the MapleSim model into a single engineering workflow, and orchestrates the simulation process to automatically explore the design space.

Latin-Hypercube sampling methods generate a smart set of data in less than 30 minutes - and do this fully automated.

Meta-models are built and validated using leave-one-out cross-validation methods - giving confidence that optimum design regions are well-captured.

Multiobjective optimization delivers a set of Pareto-optimal solutions, among which the engineering team can choose the best fit.

Introducing engineering preferences into the process, decision makers can filter Pareto points to easily select the best out of the good.

Find out much more

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