Personal communication between User-Centered Design (UCD) specialists and developers is important for communicating user needs and design solutions in agile development. In distributed projects where opportunities for personal communication are limited, the design documentation is an important surrogate. This study has investigated the perceived effectiveness of boundary objects in a distributed agile team, and their role in communicating target user needs. Six in-depth interviews with UCD specialists showed that the boundary objects rarely communicate underlying needs of the users but rather focus on interaction with the system that is being developed. The used boundary objects also do not work as stand-alone deliverables; they need to be explained and elaborated. Making the boundary objects comprehensive enough to be stand-alone is seen as too time consuming and not worth the effort. For agile projects with distributed teams, this creates hand-over and follow-up problems.

Cable and hose routing is a complex and time-consuming process that often involves several conflicting objectives. Complexity increases further when routes of multiple components are to be considered through the same space. Extensive work has been done in the area of automatic routing where few proposals optimize multiple hoses together. This paper proposes a framework for the routing of multiple pre-formed hoses in an assembly using a unique permutation process where several alternatives for each hose are generated. A combinatorial optimization process is then used to find Pareto-optimal solutions for the multi-route assembly. This is coupled with a scoring model that predicts the overall fitness of a solution based on designs previously scored by the engineer as well as an evaluation system where the engineer can score new designs found through the use of the framework to update the scoring model. The framework is evaluated using a testcase from a car manufacturer showing a severalfold time reduction compared to a strictly manual process. Considering the time savings, the proposed framework has the potential to greatly reduce the overall routing processes of hoses and cables.

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.

This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of a few of them. Hopefully, this information can be helpful for someone who wants to choose a method.

Knowledge about several different topics is required to perform a probabilistic optimization. First, it is necessary to incorporate the probabilistic behavior into the analysis by estimating how the uncertainties and variations in the model and its parameters are affecting the performance of the system. The focus in this thesis is on sampling based methods to estimate these probabilities. Secondly, an optimization algorithm should be chosen so that the computer can search for and present an optimal solution automatically.

The probabilistic optimization process can be computationally demanding since numerous simulations of the model are performed each time the value of the objective function is estimated. It is therefore desirable to speed up the process by incorporating computationally effective surrogate models. This is especially important if the simulated model is computationally demanding on its own, e.g. a finite element model with many nodes.

Each of these topics is presented in its own chapter of this thesis. A few  methods are presented and their performances demonstrated for each topic.

Surrogate models can also be used to improve the performances of optimization algorithms when the desire is to optimize computationally expensive objective functions. With this in mind, efforts have been made to improve the Complex-RF optimization algorithm. A modified algorithm is presented in this thesis and the main difference is that it creates and utilizes surrogate models iteratively during the optimization process. The modified algorithm is compared with Complex-RF and is demonstrated to be superior for computationally expensive models.

This thesis investigates how to use optimization efficiently when complex products are developed. Modelling and simulation are necessary to enable optimization of products, but here it is assumed that verified and validated models of the products and their subsystems are available for the optimization. The focus is instead on how to use the models properly for optimization.

Knowledge about several areas is needed to enable optimization of a wide range of products. A few methods from each area are investigated and compared. Some modifications to existing methods and new methods are also proposed and compared to the previous methods.

These areas include

• Optimization algorithms to ensure that a suitable algorithm is used to solve the problem
• Multi-Objective Optimization for products with conflicting objectives
• Multi-Disciplinary Optimization when analyses from several models and/or disciplines are needed
• Surrogate Models to enable optimization of computationally expensive models

Modern frameworks for optimization of complex products often include more than one of these areas and this is exemplified with the industrial applications that are presented in this thesis, including the design and optimization of industrial robots and aircraft systems.

This article presents an optimization framework that is used to optimize a hydro-pneumatic balancing cylinder for industrial robots. A balancing cylinder is a device that is used to balance the gravitational torque of one of the main axes of a high-loaded serial industrial robot. The design of components for an industrial robot is multi-disciplinary, since disciplines such as multibody dynamics, drive train design and robot control are needed. The design process is also multi-objective since the functionality of the balancing cylinder should be optimal, while its size and cost should be minimal. The article therefore also contains a discussion about multi-disciplinary and multi-objective optimization of complex products.

The paper presents a framework that can be used to design and optimize a balancing mechanism for an industrial robot. The framework has the capability to optimize three different concepts - a mechanical, a pneumatic and a hydro-pneumatic. Several disciplines are included in the framework, such as dynamic and static analyses of the robot performance. Optimization is performed for each concept and the obtained optimal designs are all better then the reference design. This means that the framework can be used both as a tool to optimize the balancing mechanism and also to support concept selection.

This paper presents how modular construction systems are used in product development projects for mechanical engineering students to rapidly generate and evaluate different concepts. The method can also be implemented in the industry for the same purposes.

The method is demonstrated with LEGO for three applications – a lifting mechanism, a foldable bicycle and a remote controlled cup mover. Several concepts were built and evaluated for each of the three applications.

The possibility to evaluate each concept with a physical prototype increased the information that the students had when they chose which concept they would develop further. One drawback was that some students required a few hours of LEGO handling to feel confident when they assembled technical solutions.

This paper demonstrates a method that can be used to combine topology optimization with optimization of the boundary conditions. The method utilizes design of experiments and surrogate models to model how the boundary conditions affect the potential mass of the component. A demonstration of the method is made by applying it to design an airplane component and comparing the result to other approaches. The best design is then manufactured using additive manufacturing to verify that it is feasible.

Today there is a desire to perform optimizations in order to receive optimal system properties. However, for computationally expensive simulation models, an optimization maybe too tedious to be motivated. This paper proposes a modification of the Complexoptimization algorithm to enable the creation and usage of local meta-models during theoptimization. Its performance is demonstrated for a few analytical problems and a reliabilitybased design optimization is conducted for an aircraft example.

This paper compares different approaches for using kriging metamodels for robust design optimization, with the aim of improving the knowledge of the performance of the approaches. A popular approach is to first fit a metamodel to the original model and then perform the robust design optimization on the metamodel. However, it is also possible to create metamodels during the optimization. Additionally, the metamodel need not necessarily reanimate the original model; it may also model the mean value, variance or the actual objective function. The comparisons are made with two analytical functions and a dynamic simulation model of an aircraft system as an engineering application. In the comparisons, it is seen that creating a global metamodel before the optimization begins slightly outperforms the other approaches that involve metamodels.

Concepts for complex products are often developed using computer models, introducinguncertainties both in design and model accuracy. There exist several methods forapproximating these uncertainties and this paper presents and compares some of them. Thefocus is on sampling based methods including or excluding response surfaces, and they arecompared by accuracy and computation time, using a Monte Carlo sampling as reference.The application is a simplified system model of a dynamic pressure regulator that controlsthe air supply in the environmental control system of an aircraft.

This paper compares the performance of five methods for robust design optimization of computationally demanding models including one novel method. The comparison is made using several mathematical functions and two engineering problems. The performance metrics are the mean value and standard deviation of the optimum as well as an index that weights together the required number of simulations of the original model and the chance of finding the optimum. The result of the comparison shows that sequential robust optimization is the most effective method.

This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modifications reduces the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is compared with a number of existing algorithms and demonstrated for a few analytical and engineering problems.

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model.

The proposed frameworkutilizes well-established methods such as metamodeling and multi-level optimization inorder to speed up the design optimization process. The contributionof the paper is to show that by applying amerger of well-established methods, the computational cost can be cutsignificantly, enabling search for truly novel concepts.

This paper presents a multidisciplinary design optimization (MDO) framework for automated design of a modular industrial robot. The developed design framework seamlessly integrates High Level CAD templates (HLCt) and physics based high fidelity models for automated geometry manipulation, dynamic simulation, and structural strengthanalysis. In the developed framework, methods such as surrogate models and multilevel optimization are employed in order to speed up the design optimization process. This work demonstrates how a parametric geometric model, based on the concept of HLCt, enables a multidisciplinary framework for multi-objective optimization of a modular industrial robot, which constitutes an example of a complex heterogeneous system.

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.

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.

This paper presents a method for multi-objective optimization of hose assembly routing. Hose routing is a non-trivial task which demand a lot of iterations, especially with the increased complexity in modern vehicles. The proposed method utilizes design automation through multi-objective optimization of routing assemblies containing multiple hoses. The method is intended as a decision support and automation-tool, that reduces the number of iterations needed. The method has been implemented and tested on a case, concerning a set of hoses in an engine compartment, showing credible results.

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.

In this paper, a method of designing for Additive Manufacturing (AM) is proposed, implemented, and evaluated in a case study. In the proposed method, Topological Optimization is combined with a Multidisciplinary Design Optimization (MDO) framework that handles multi-objective optimization. Both the weight and amount of support material needed during manufacturing are minimized. In the proposed method, the topological optimized structure is remodelled into a parametric CAD model. The CAD model is then combined with an FE-model that calculates the stresses in the material and a model that calculates the amount of support structure needed. Two different optimization formulations are evaluated and compared in the case study.

In the case study an upright of a Formula Student racing car is designed. Several design evaluations are performed resulting in a set of Pareto optimal designs that could be used for decision-making where the trade-off between the two objectives is considered. It is concluded that the proposed method fulfils its purpose by being able to identify designs that would be difficult to come up with manually. Several suggestions for further studies in order to improve the method are also discussed.

Purpose

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

Design/methodology/approach

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Findings

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

Originality/value

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.