Interdisciplinary Design: Influence of Team Structure on Project Success

This paper focuses on the application of complexity theory and entropy concepts in the design process. Broadly speaking, the design process involves three phases: problem definition, conceptual design and embodiment. In the conceptual design phase, concepts that satisfy the functional requirements of the desired product are identified and compared. It is said that approximately 75% of the total product life-cycle cost is committed in this phase. The conceptual design phase has two essential sub-phases, namely, obtaining a solution set and selecting the most suitable solutions. Our work focuses on the selection sub-phase. The aim within this sub-phase is to minimize the number of selected concept variants and to reduce their chances of rejection in later stages. However, the solution to this problem is quite elusive, mostly because information about concept variants is scarce and rather qualitative at this stage. A common method is to perform a cost-benefit analysis. However, the analysis relies heavily on expert intuition and is thus subjected to high uncertainties. Recently, axiomatic design is gaining popularity. This is a framework that incorporates two axioms, namely, the Independence Axiom and the Minimum Information Axiom, accompanied by several corollaries. However, criticism on the integrity of the Independence Axiom has appeared recently in the literature. Further, the formulation of axiomatic design appears to have logical flaws. Finally, the conceptual design phase, a distinct phase in the design process, cannot be distinguished clearly in axiomatic design. In this paper we try to improve the selection phase of the conceptual design by improving the existing cost-benefit approach. In this vein, performance features against which concepts would be evaluated are established. We propose the use of Kolmogorov complexity theory and entropy concepts from information theory and physics to evaluate the complexity of the performance features. The design concepts are then improved based on the rule to reduce complexity of each performance feature. Weights are finally assigned to each performance feature and an overall complexity index is obtained which is suitable to compare designs. The ideas are further elaborated on by examples.

Additive Manufacturing (AM) brought new design freedom and possibilities that enable design and manufacturing of products with new forms and functionalities. To utilise these possibilities a new design approach emerged, Design for Additive Manufacturing (DfAM), that contains methods and tools for supporting AM oriented design process. Designers working with AM are aware of the need to apply DfAM and AM possibilities in conceptual design phase where they have the most significant influence on product architecture and form but are facing a lack of suitable DfAM approaches for early design phases. Therefore, the presented research is investigating possibilities of storing and representing AM knowledge in the form of design principles to be used in the conceptual design phase. The paper proposes conceiving of Design Principles for Additive Manufacturing repository where formalised AM knowledge is stored in the form of design principles and structured based on function criteria. In the paper, various elements of design principle representation are discussed, as well as their role in the conceptual design process.

BackgroundWeb-based and mobile interventions to influence physical activity behavior have had limited effects on sustained behavior change. One reason may be that the interventions aim to change largely habitual behavior. Following an identity-oriented approach could be a successful strategy to behavior change because people are committed to behave in line with their self-perception of identity.ObjectiveIn this paper, we take a closer look at the role of motivation in long-term adherence to lifestyle interventions. The paper outlines a method for web-based or mobile intervention development that allows exploration of integrating behavior change theory into the design process. We will describe the development of a mobile app that allows people to be self-determined and to value and self-regulate physical activity by adopting an identity-oriented approach.MethodsThis paper describes a Research through Design (RtD) process in which design activities are carried out as part of the knowledge-generating process. Two RtD phases were completed, followed by a conceptual design phase. In the first RtD phase, 8 participants used diary cards to study initial attitudes toward starting with small changes in physical activity. In the second RtD phase, 26 participants used a web-based app to study changes in physical activity. We used an adapted version of the Self-Report Habit Index (SRHI) to evaluate individuals’ perceptions of a particular behavior with respect to the three facets of a habit. The conceptual design phase consolidated the results from first two RtD phases into a design of a mobile app that combines an identity approach with gamification principles. The conceptual design was evaluated in a user-experience study with 4 participants.ResultsIn the first RtD phase, we found that interacting daily with diary cards and reflecting on physical activity patterns is a promising strategy but works better through a digital medium. In the second RtD phase, SHRI ratings from all participants generally increased each week. In the conceptual design phase, we found that the concept of the mobile app was positively evaluated by participants. However, participants mentioned that terms such as “identity” do not resonate with them and that scenarios could be simpler.ConclusionsThis paper provides deeper insights into designing for electronic health (eHealth) interventions and services and suggests a new way that motivation can be shaped by the design of an intervention and adherence to physical activity. To the best of our knowledge, this was the first iterative design study in which the effects of adopting an identity approach to both motivation and physical activity were included and observed. Initial promising results were found for using a web-based intervention where habits and identification with the personal importance of a behavior were repetitively triggered.

1.2 What are the main phases of product design and development Before discuss the collaboration issues for product design and development, we need to briefly review the phases of product design and development. The major phases of product design and development are normally defined as conceptual design, preliminary design, and detail design and development (Blanchard, 2004). During the conceptual design phase, the concepts, which are also called scheme, are built in order to completely and efficiently design the transceiver. The concepts may include such as product operational requirements and maintenance, current product problem (or deficiency), functional analysis for the product, applicable technical performance measures (TPMs), and specific performance measures and design-to criteria. Preliminary design phase begins with a “functional baseline” product configuration described in the product specification prepared during conceptual design phase. The functional baseline is translated into detailed qualitative and quantitative design requirements for allocating applicable elements of the product. An “allocated baseline” configuration in the form of development, product, and process specifications is established during this phase. At the beginning of detail design and

Analytical Hierarchy Process(AHP) is a well proven evaluation-decision making tool which has been used extensively in almost every field of research that involves decision making. This study investigates AHP's applicability to industrial design at the conceptual design phase. The industrial design process is made up of several important phases, one of which is the conceptual design. This is the step in which several concept ideas for a certain product are created as alternatives among which the most suitable one is selected for further development. It is very important to perform this selection correctly as it has a direct effect on the success of the later design phases, final design and eventually the marketed product. A wrong selection may create design issues, causing redesign work, remanufacture or even recalling of the final product back from market, meaning a total failure. Evaluation and selection of the available conceptual designs are usually done without strictly defined selection criteria and in a subjective environment which is open to negative interference from the outside. There are also several inside factors like decision maker's prejudices and biases that might effect the selection negatively. For the design concept to be determined in the best possible way, a tool which is immune to all kinds of external and internal influences is necessary. This tool should be methodic, easy to use and as suitable as possible for use in a computer environment. A short scan among tools of this nature shows that AHP is one of them that can fulfill the needs of the industrial design process. In this article, the history of AHP is introduced briefly first, then the inner workings of it is explained in detail and an application is presented to demonstrate its use at the conceptual design phase of a sample product design process.

A case is made that introducing CAD-based geometry prematurely in the aircraft design process actually hinders multidisciplinary design. Consequently, a design process which allows a designer to quickly build and analyze aerospace configurations without CAD or computational meshing specialists is proposed. This allows higher fidelity analyses to be applied at conceptual design and decreases the turnaround time for trade studies and optimizations. The basic premise of conceptual geometry modeling is presented and discussed. Key features and capabilities which are essential for a successful geometry modeler are identified. Desktop Aeronautics' conceptual geometry modeler, RAGE, is described, with emphasis on recent progress and advances in the tool. A preliminary graphical-user-interface is presented along with an application programming interface. Extensibility of the RAGE modeler is discussed along with the importance of this feature. The applicability of RAGE to simple trade studies is demonstrated in an example problem. The precision of the RAGE modeler is also demonstrated on a complex aircraft design. Future plans for the geometry modeler are also discussed. I. Background In the conceptual phase of aircraft design, there is often a wide range of geometric configurations that the designer wishes to consider, analyze, and assess. A designer’s intuition and creativity are crucial to a successful conceptual design, yet are easily inhibited by the modeling and analysis process itself, particularly if high-fidelity analysis is warranted. If the time between inception and assessment of a candidate design is relatively long, a designer may be forced to limit the number of concepts considered. New data revealed in a design iteration may suggest or require significant changes to the geometry configuration. Since the majority of considered design configurations will eventually be discarded, an ideal conceptual design process should involve a highly efficient and easy to use geometry modeler. Significant changes should be allowed without tedious re-work of the geometry model. Exporting the model for analysis should require little effort and have the capability to be automated. Therefore, any modeling application used in this process should be flexible and not restrict the designer’s choice of user interface, analysis method, and design optimization environment. Most current design processes in the aerospace industry rely heavily on computer-aided-design (CAD) applications. As a general design tool, CAD is a powerful geometry modeler capable of precisely representing practically any shape. Unfortunately, this generality is realized at the cost of requiring specialized training to efficiently and effectively use the software. Also, a CAD model is often not easily modified, especially if significant re-work of the geometry is necessary. This can lead to the premature freeze of an aircraft design due to employing excessively high-fidelity geometry modeling too early in the design process. Parametric CAD modeling could address this issue, but creating a well-designed parametric CAD model is difficult. Not only does it require an extremely skilled operator, but also the designer must have some insight into what parameterization may ultimately be desired. Modifying a parametric CAD model is often more difficult than modifying a conventional CAD model. Significant changes, such as adding or removing cross-sections, may invalidate the parameterization in some cases. In most organizations, CAD modeling (whether it be parametric or conventional) is often assigned to a dedicated CAD specialist. Since the conceptual designer is usually not a CAD specialist, the latter must communicate his design intent to the former, often using sketches, dimensions, and word of mouth. When the CAD specialist interprets the sketches, any part or detail not precisely defined usually requires judgment and artistic license. This interpretation may result in loss of design intent and indeterminate iteration between the CAD specialist and designer to obtain a satisfactory result. Because this process is iterative, CAD operators often must recreate similar geometry

<div class="section abstract"><div class="htmlview paragraph">Current transport aircraft are mature systems, thus require increased fidelity at the beginning of the design process to allow further optimization. Furthermore, a desire exists to explore unconventional aircraft configurations at the conceptual level. This has motivated the development of a tool which effectively manages the trade-off between high-fidelity levels, flexibility and short turn-around times. This paper presents a CATIA V5-based parametric aircraft geometry modeler developed by Bombardier Aerospace. The aim of the tool is to provide consistent high-fidelity geometric data early in the conceptual aircraft design process.</div><div class="htmlview paragraph">The intended near-term use of the modeler is two-fold: during the early design phase, the modeler computes geometric data such as areas, volumes, ESDU aircraft parameters, etc. In the competitive analysis domain, the tool provides a high-quality three-dimensional model with manageable effort. In both cases, the engineer is presented with a fully parametric three-dimensional CATIA V5 aircraft model. In the medium-term, use by expert departments is envisioned. </div><div class="htmlview paragraph">Moreover, the geometrical data extracted from CATALIST can be saved into a standard database and fed to various analysis codes to compute aircraft performance characteristics. As aircraft models are in native CATIA V5 format, this fits seamlessly into the established design process at Bombardier Aerospace. Additionally, this approach allows critical analyses to be performed earlier in the conceptual design phase.</div><div class="htmlview paragraph">Aircraft exterior and interior modules are in use today along with systems modules and a basic structural representation. An overview of the key features of the tool is presented along with sample applications. Tool usability and modeling quality have been extensively validated with selected regional aircraft and business jets from Bombardier.</div></div>

Status of the EU test blanket systems safety studies

The main focus of this paper is how the concept design phase of the product development process can be improved by using an objective data-driven approach in selecting a final concept design to progress further. A quantitative new test-bed ‘Product Optimisation Value Engineering’ (PROVEN) is presented to critically assess new and evolving powertrain technologies at the concept design phase. The new test-bed has the ability to define a technology value map to assess multiple technical options as a function of its attributes, whose precise values can be determined at a given cost. A mathematical model that incorporates a highly adaptable, data-driven and multi-attribute value approach to product specification and conceptual design is developed, novel to the concept design process. This creates a substantially optimised product offering to the market, reducing overall development costs while achieving customer satisfaction.

Architectural design decisions are primarily made through an interaction between an architect and a client during the conceptual design phase. However, in larger-scale public architecture projects, the client is frequently represented by a community that embraces numerous stakeholders. The scale, social diversity, and political layers of such collective clients make their interaction with architects challenging. A solution to address this challenge is using new information technologies that automate design interactions on an urban scale through crowdsourcing and artificial intelligence technologies. However, since such technologies have not yet been applied and tested in field conditions, it remains unknown how communities interact with such systems and whether useful concept designs can be produced in this way. To fill this gap in the literature, this paper reports the results of a case study architecture project where a novel crowdsourcing system was used to automate interactions with a community. The results of both quantitative and qualitative analyses revealed the effectiveness of our approach, which resulted in high-level stakeholder satisfaction and yielded conceptual designs that better reflect stakeholders’ preferences. Along with identifying opportunities for using advanced technologies to automate design interactions in the concept design phase, we also highlight the challenges of such technologies, thus warranting future research.

During the early phases of engineering design, the costs committed are high, costs incurred are low, and the design freedom is high. It is well documented that decisions made in these early design phases drive the entire design's life cycle cost. In a traditional paradigm, key design decisions are made when little is known about the design. As the design matures, design changes become more difficult in both cost and schedule to enact. The current capability-based paradigm, which has emerged because of the constrained economic environment, calls for the infusion of knowledge usually acquired during later design phases into earlier design phases, i.e. bringing knowledge acquired during preliminary and detailed design into pre-conceptual and conceptual design. An area of critical importance to launch vehicle design is the optimization of its ascent trajectory, as the optimal trajectory will be able to take full advantage of the launch vehicle's capability to deliver a maximum amount of payload into orbit. Hence, the optimal ascent trajectory plays an important role in the vehicle's affordability posture yet little of the information required to successfully optimize a trajectory is known early in the design phase. Thus, the current paradigm of optimizing ascent trajectories involves generating point solutions for every change in a vehicle's design parameters. This is often a very tedious, manual, and time-consuming task for the analysts. Moreover, the trajectory design space is highly non-linear and multi-modal due to the interaction of various constraints. When these obstacles are coupled with the Program to Optimize Simulated Trajectories (POST), an industry standard program to optimize ascent trajectories that is difficult to use, expert trajectory analysts are required to effectively optimize a vehicle's ascent trajectory. Over the course of this paper, the authors discuss a methodology developed at NASA Marshall's Advanced Concepts Office to address these issues. The methodology is two-fold: first, capture the heuristics developed by human analysts over their many years of experience; and secondly, leverage the power of modern computing to evaluate multiple trajectories simultaneously and therefore enable the exploration of the trajectory's design space early during the pre- conceptual and conceptual phases of design. This methodology is coupled with design of experiments in order to train surrogate models, which enables trajectory design space visualization and parametric optimal ascent trajectory information to be available when early design decisions are being made.

Systems engineering optimization of Omid-2 in the conceptual design phase

The process used by engine manufacturers for the development of a new engine includes the planning and conceptual design phases, followed by the detailed design phase, in which the design target specifications are met. In the conceptual design phase, a rough specification of the target engine is presented to facilitate a detailed design and the additional cost of modification is reduced exponentially. In the conceptual design phase, however, not only is there no real engine. but there are also no 1D and 3D models present, so it is impossible to test and simulate them. Therefore, at this stage, a model that can predict emission and performance only according to the specifications and operating conditions of the engine would be very useful. Previous studies developed an EGR prediction model that can be used in the 0-D NOx prediction using a deep learning method. In this study, a NOx prediction model with high accuracy using only the operating conditions as input variables, without ECU data, was developed using deep neural networks. The developed model has high accuracy with an R-square of 0.988. The feature of this model is that all the input parameters for the deep neural network come from the operating conditions of the engine. Therefore, this model can be used in the early stages of the development of new engines when testing and simulation cannot be performed because they do not exist. The designer can set the range of the operating conditions such that they do not exceed the NOx limits at the specific operating point (specific rpm and BMEP). This variable operating design methodology is expected to be useful in the development of new engines for automobile manufacturers with various engine data.

The Modular Optical Space Telescope (MOST) project is a design tool that allows rapid generation and analysis of many unique space telescope realizations. The project uses a parametric integrated model to quantitatively analyze many dierent architectures during the conceptual design phase. MOST aims to include advanced concepts and technologies such as lightweight, deformable optics, dynamic wavefront control, and low-cost segmented mirror systems. Traditional optical performance metrics are considered alongside programmatic metrics such as cost, robustness, and mission operations, permitting an examination of these conflicting requirements at the very beginning of the project. The model has been substantially updated to include new technologies, control, and performance metrics, and the trade studies continue to provide insights into determining favorable space telescope architectures. urrent space telescopes are reaching their limits in terms of size, cost, and performance. Future systems will have larger aperatures with increasingly tight performance requirements, with line-of-sight (LOS) jitter on the order of milli-arcseconds and wavefront error (WFE) on the order of nanometers. At the same time, lower mass and lower cost systems are desired. This creates an implicit conflict for currently designed systems, thus new technologies and telescope types must be developed and implemented. Technology possibilities for these new systems include deployable mirrors or sparse apertures, structural control, and active optics. Sparse apertures and deployable optics can be deployed on orbit, eliminating the launch fairing diameter as an aperture size constraint. Also, the smaller, identical mirrors used in sparse apertures are easier and less expensive to manufacture than large monolithic mirrors. Structural control can provide a means to achieve better performance with a less massive system by counteracting the flexible dynamics of the lightweight system. Active optics can help to meet the strict performance requirements. A fast steering mirror can correct for LOS jitter, and wavefront sensing and mirror shape control can rectify both thermal and dynamic distortions of the primary mirror. These and other technologies provide a promising path to meeting both the performance and systematic requirements of future space telescopes. The challenge is in determining which combinations of technologies create favorable architectures. It is important to explore the entire tradespace of possible designs to ensure that superior designs are not overlooked. In order to assist with these important decisions and tradespace exploration, the MOST project has created a parametric modeling technique to consider the implications of design decisions during the conceptual design phase. 1,2 In the past, simple models, along with engineering judgment, have been used in the conceptual design phase where key design decisions were made. During the preliminary design phase, detailed models of the single point design were built and analyzed. If the requirements were not met, then another point design was chosen and modeled. This strategy works well if the system is based upon heritage designs and performance can be adequately predicted. However, with the addition of new technologies, there is no guarantee that the chosen point design would meet the requirements, and if the design does meet requirements, there is no assurance that it is an optimal design. Therefore, the MOST project uses a parameterized model to quickly and automatically generate and analyze many designs during the conceptual

The design process of new air- and rotorcraft is commonly separated into three different consecutive phases. In the conceptual design phase, the viability of different designs is investigated with respect to customer requirements and/or the market situation. It usually ends with the identification of a basic aircraft lay-out. In the subsequent preliminary design stage the various disciplines are introduced, thus redefining the design process as a multidisciplinary optimization (MDO) task. The objective of this design stage is to enhance the initial aircraft configuration by establishing an advanced design comprising a loft provided with primary structure. This updated aircraft configuration represents a global optimum solution for the specified requirements which will then be optimized on a local level in the concluding detailed design phase with particular regard to manufacturing aspects. The investigations in the preliminary design phase comprise the generation of numerous similar but still different analytical and finite element (FE) models. Even though computational power is constantly increasing the model generation process is still a time-consuming task. Moreover, it is also a potential source of errors which — in the worst case — may lead to time- and cost-intensive redesign activities during the detailed design. As the preliminary design stage, therefore, is of particular importance during the overall design process the model generation process benefits from parametric models and automated process chains. The presented paper overviews the tools used for the automated generation of FE models developed and used at the Institute of Structures and Design (BT) of the German Aerospace Center (DLR) for the subsequent use in numerical simulations. Furthermore, basic requirements for the effective use of parametrization and automation like a common data format and infrastructure will be introduced. Exemplary models and applications will be presented to illustrate the positive impact on efficiency in aircraft design. Concluding, future development steps and possible applications will be discussed.