Non-Uniform Rational B-Splines (NURBS) remain central to CAD. Their key strength lies in precision, especially for modeling intricate curves and surfaces. Engineers continue to rely on them. Yet technology advances. Cloud computing, artificial intelligence, and collaborative tools are changing how NURBS are applied. Together, these innovations reshape industrial design workflows. This study investigates those transformations. It focuses on how NURBS-based workflows adapt to the specific needs of sectors such as aerospace, automotive, and additive manufacturing. These industries require high accuracy and complex geometries. We illustrate our analysis with case studies. Through these, we explore the influence of distributed design systems and AI-driven geometry generation. We also examine the use of real-time simulations enabled by the Internet of Things. Cloud collaboration shortens the iteration cycles by 40%, according to recent data [1]. AI-based optimization lowers geometric inaccuracies by about 30% [3]. These improvements are significant and measurable. High-performance computing also plays a role. Especially in lightweight design tasks where speed and detail are critical. HPC accelerates simulation and supports design decisions. Interoperability is equally important. Standards such as STEP enable seamless transitions between CAD and CAE. They help unify tools and ensure continuity across systems. Altogether, the results point to a shift. NURBS workflows are evolving toward more adaptive, sustainable, and computationally resilient structures. Far from obsolete, NURBS continue to serve as vital instruments for the future of CAD.

This study presents a hybrid Non-Uniform Rational B-Spline (NURBS) methodology for the geometric reconstruction of topology-optimized structural components. NURBS are employed exclusively as a post-processing tool; all structural analyses are performed using standard finite elements (SOLID187 elements, ANSYS Mechanical R19.2), and isogeometric analysis (IGA) is not used. The methodology is validated on an Al 6061-T6 electric scooter neck under a 600 N static load. Two SIMP optimization iterations followed by a hybrid NURBS reconstruction reduce the component mass from 1.247 kg to 0.531 kg, achieving a 57.4% mass reduction. Finite element re-validation of the reconstructed geometry yields a maximum von Mises stress of 126.45 MPa (safety factor, SF = 2.18, exceeding the 2.0 requirement), a maximum deflection of 2.31 mm, and a first natural frequency of 127 Hz. Mesh convergence between the 2.5 mm and 1.25 mm refinements is Δ = 0.90%. Relative to the direct SIMP output (201 MPa), NURBS reconstruction reduces the peak stress by 37%, demonstrating that geometric post-processing is not a neutral step but a critical determinant of structural performance. Both fully automated STL reconstruction and edge-based NURBS reconstruction failed for this geometry class due to non-manifold topology and patch discontinuities, respectively. The proposed hybrid region-decomposition approach is the only method that has produced a watertight, FEA-compatible CAD model.