This scientific paper introduces a Design Automation (DA) framework that streamlines the Design for Additive Manufacturing (DfAM) process. The framework is designed to simplify the creation and evaluation of different design options by automating geometry creation using high-level CAD templates and setting up and connecting Computer-Aided Engineering (CAE) models to perform functional and manufacturing evaluations. By considering manufacturing constraints early in the design process, the framework aims to investigate various design alternatives and facilitate design changes late in the development process without additional manual work. This framework provides a comprehensive view of the entire DfAM process, integrating everything from functional requirements to manufacturing evaluation and preparation into the same design automation framework. To demonstrate the usefulness of the framework, the authors used it to design a hydraulic pump. Compared to the original design, the design created with the proposed framework reduces pressure drop by more than 50% and reduces the pump's weight by 35%. Furthermore, on an assembly level, the framework consolidates four components into two and eliminates two sealings. In summary, the Design Automation framework introduced in this paper simplifies the DfAM process by enabling automation of geometry creation and the setup and connection of CAE models. The framework facilitates the exploration of different design alternatives early in the process, considering manufacturing constraints, and enables design changes later in the development process without manual work. The benefits of the framework are illustrated through the design of a hydraulic pump, where it achieved significant improvements in performance, weight, and assembly complexity. 

Purpose - The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

Design/methodology/approach - A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

Findings - The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

Originality/value - The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.

This paper presents and analyses the research front on development methodologies that can be applied to flight simulations for development purposes. This field includes any flight simulator used during the development phase of an aircraft, when flight test or other validation data is still scarce. A review of the literature published between 1999-2019 is performed. As flight-specific literature on the topic is limited, a broader view on flight simulators is adopted. Simulators are regarded as risk-averse scientific software; that is, software created to understand a phenomenon and whose primary goal is to be correct. This perspective highlights the lack of suitable established software development methodologies (SwDev) for this software type. Two solutions to this problem have been identified: one treating scientific SwDev as a knowledge acquisition process, and another one treating it as development of enabling systems, following the established product development processes. These solutions need to be completed with methodologies to deal with the multidisciplinarity of the flight simulation problem, such as model exchange standards or workflows for multidisciplinary collaboration.

In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.

The continuous management of a manufacturing company's product portfolio is a key aspect ofsuccessful product development. Managers determine when and which products should be updated orterminated. This process influences inhouse Industrial design teams, thus prompting a range ofdevelopment strategies they might deploy. In product portfolio management there is a tension betweenstandardisation and customisation. From a marketing perspective this is may be addressed through brandDNA, from engineering through modularization. The design perspective (merging those two) has beenill-explored, particularly from professional designers. Previously we proposed a theoretical modeldescribing different industrial design projects and how they influence industrial designers strategicthinking. It was developed through literature reviews and examples found in manufacturing industry.Through a multi-case interview study with 16 participants from five manufacturing companies withstrong brands, this article aims to empirically evaluate the proposed model. The results show that therespondents could describe all but one industrial design projects, the cause maybe that they had not beenexposed to saving a company by doing a total makeover.

This paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

To achieve progression in a product portfolio, companies employ industrial designers to aid in the development and creation of new products. Researchers have shown that industrial designers play a consolidating role at both strategic and operational management levels due to the need to incorporate requirements from several company functions, such as brand, research and development, production, economics and senior management. This article investigates how to expand the ways in which a product portfolio may be extended and updated, from an industrial design perspective. Seven product portfolio development strategies were identified. Inspired by studies of competitor's influence on an existing portfolio, a theoretical descriptive model was developed: the Industrial Design Product Portfolio Management model, which positions the suggested product portfolio development strategies in relation to existing and competing products. The findings may advance our current understanding of existing product portfolio management strategies and the connection between management and product design. Thus, it could be a useful framework for academics, teachers and professionals. 

System-of-Systems Engineering (SoSE) has become a constantly growing field within product development for complex systems. Systems are becoming more and more connected with other systems and the operational environment in general. This takes the development process to new levels of complexity where high degrees of uncertainties are expected due to ever occurring changes in the operational environment, and other external factors such as politics, economy, and technology. This creates a need of being able to understand the influence of changes early in the development process and to facilitate the systems? perseverance. The focus of the development shifts from fulfilling specific requirements, to being able to meet needs and deliver capabilities over time. Additionally, modeling and simulation for complex systems and System-of-Systems (SoS) becomes a valued alternative to the economically prohibited and almost impossible prototype testing. In consideration of this problem, the presented work introduces a method for both modeling and simulation of a SoS. The method uses ontology with description logic reasoning to derive and narrow down a SoS design space which is further analyzed using Agent Based Simulation (ABS). A Search and Rescue (SAR) scenario is used as a case study to test the method. Measures of Effectiveness (MoE), based on the time it takes to find a rescue subject and the cost of doing so, are used to evaluate the SoS performances. The presented method is envisioned to be used early in the development of complex systems and SoS to increase the overall understanding of them.

In order to enable easy modification of results from a design optimization process in a CAD tool, a flexible representation of the geometry is needed. This is not always trivial however, since many file formats are not importable as modifiable geometry into the CAD tool, and if they are, they might not represent the geometry in a way that enables easy modification. To mitigate this problem a design automation (DA) and a machine learning (ML) approach are developed and compared using a test case from an optimization process used to optimize hose routing in tight spaces. In the test case used, the geometry from the optimization process consists of center curves represented as a large number of points. To enable easy modification a more flexible representation is needed such as a spline with a few well-placed control points. Both the DA and ML approach can approximate center curves from the optimization process as splines containing a varying number of control points but do show different properties. The DA approach is considerably slower than the ML but adds a lot of flexibility regarding accuracy and the number of control points used.

The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.