  The latest sheet stamping processes enable efficient manufacturing of complex
shape structural components that have high stiffness to weight ratios, but
these processes can introduce defects. To assist component design for stamping
processes, this paper presents a novel deep-learning-based platform for
optimising 3D component geometries. The platform adopts a non-parametric
modelling approach that is capable of optimising arbitrary geometries from
multiple geometric parameterisation schema. This approach features the
interaction of two neural networks: 1) a geometry generator and 2) a
manufacturing performance evaluator. The generator predicts continuous 3D
signed distance fields (SDFs) for geometries of different classes, and each SDF
is conditioned on a latent vector. The zero-level-set of each SDF implicitly
represents a generated geometry. Novel training strategies for the generator
are introduced and include a new loss function which is tailored for sheet
stamping applications. These strategies enable the differentiable generation of
high quality, large scale component geometries with tight local features for
the first time. The evaluator maps a 2D projection of these generated
geometries to their post-stamping physical (e.g., strain) distributions.
Manufacturing constraints are imposed based on these distributions and are used
to formulate a novel objective function for optimisation. A new gradient-based
optimisation technique is employed to iteratively update the latent vectors,
and therefore geometries, to minimise this objective function and thus meet the
manufacturing constraints. Case studies based on optimising box geometries
subject to a sheet thinning constraint for a hot stamping process are presented
and discussed. The results show that expressive geometric changes are
achievable, and that these changes are driven by stamping performance.
