In this paper, the application of deep learning methods to the task of machining feature
recognition in CAD models is studied. Four contributions are made: 1. An automatic method …

Large-scale, high-quality data sets are central to the development of advanced machine
learning techniques that increase the effectiveness of existing optimization methods or even …

The state-of-the-art non-isothermal Hot Forming and cold die Quenching (HFQ®) process
can enable the cost-effective production of complex shaped, high strength aluminium alloy …

Geometric deep learning models, like Convolutional Neural Networks (CNNs), show
promise as surrogate models for predicting sheet stam** manufacturability but lack design …

Sheet stam** is a widely adopted manufacturing technique for producing complex
structural components with high stiffness-to-weight ratios. However, designing such …

Abstract Machine learning is opening up new ways of optimizing designs, but it requires
large data sets for training and verification. The primary focus of this paper is to explain the …

Despite the recognized importance of datasets in data-driven design approaches, their
extensive study remains limited. We review the current landscape of design datasets and …

Parametric modeling is a computer-aided design (CAD) paradigm where a design can be
created by defining geometric constraints with parameters. In design change as well as …

Many high-level technical products are associated with changing requirements, drastic
design changes, lack of design information, and uncertainties in input variables which …

Abstract The novel Hot Forming and cold die Quenching (HFQ®) process can provide cost-
effective and complex deep drawn solutions through high strength aluminium alloys …