This article discusses tools that are used for the systematic optimization of engineering designs. It focuses on the practical application of optimization technology in a computer-aided engineering environment. The article presents numerical optimization algorithms and provides some background on how these algorithms make decisions when searching for the optimal design. It also provides information on structural optimization, topology optimization, materials processing optimization, multidisciplinary optimization, and global optimization.

Additive manufacturing is a collection of manufacturing processes, each of which builds a part additively based on a digital solid model. The solid model-to-additive manufacturing interface and material deposition are entirely computer-controlled. The traditional additive manufacturing applications have been used for low production runs of parts with complex shapes and geometric features. Additive manufacturing is also used for topology optimization and it impacts the process and supply chain. This article discusses processes, including vat photopolymerization, material jetting, powder bed fusion, directed energy deposition, material extrusion, binder jetting, and sheet lamination.

Additive manufacturing (AM) processes fabricate parts in a layer-by-layer manner by which materials are added and processed repeatedly. This article introduces the general concepts and approaches to design for AM (DFAM) and outlines important implications for part characteristics, design opportunities, manufacturing practices, supply chains, and even business models. It presents contrasting perspectives on DFAM, followed by a discussion on more general and overarching opportunistic design methods and on design for constraints, similar to conventional DFM. It concludes with a presentation of a design approach to the AM process chain, acknowledging that AM-fabricated parts typically undergo several postprocessing steps and that it is important to design taking into account these steps.

Additive manufacturing (AM) provides exceptional design flexibility, enabling the manufacture of parts with shapes and functions not viable with traditional manufacturing processes. The two paradigms aiming to leverage computational methods to design AM parts imbuing the design-for-additive-manufacturing (DFAM) principles are design optimization (DO) and simulation-driven design (SDD). In line with the adoption of AM processes by industry and extensive research efforts in the research community, this article focuses on powder-bed fusion for metal AM and material extrusion for polymer AM. It includes detailed sections on SDD and DO as well as three case studies on the adoption of SDD, DO, and artificial-intelligence-based DFAM in real-life engineering applications, highlighting the benefits of these methods for the wider adoption of AM in the manufacturing industry.