Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies.

Supporting designers is one of the main motivations for design research. However, there is an ongoing debate about the ability of design research to transfer its results, which are often provided in form of design methods, into practice. This article takes the position that the transfer of design methods alone is not an appropriate indicator for assessing the impact of design research by discussing alternative pathways for impacting design practice. Impact is created by different means – first of all through the students that are trained based on the research results including design methods and tools and by the systematic way of thinking they acquired that comes along with being involved with research in this area. Despite having a considerable impact on practice, this article takes the position that the transfer of methods can be improved by moving from cultivating method menageries to facilitating the evolution of method ecosystems. It explains what is understood by a method ecosystem and discusses implications for developing future design methods and for improving existing methods. This paper takes the position that efforts on improving and maturing existing design methods should be raised to satisfy the needs of designers and to truly support them.

This paper introduces a rigorous framework for function modeling of complex multidisciplinary systems based on the system state flow diagram (SSFD). The work addresses the need for a consistent methodology to support solution-neutral function-based system decomposition analysis, facilitating the design, modeling, and analysis of complex systems architectures. A rigorous basis for the SSFD is established by defining conventions for states and function definitions and a representation scheme, underpinned by a critical review of existing literature. A set of heuristics are introduced to support the function decomposition analysis and to facilitate the deployment of the methodology with strong practitioner guidelines. The SSFD heuristics extend the existing framework of Otto and Wood (2001) by introducing a conditional fork node heuristic, to facilitate analysis and aggregation of function models across multiple modes of operation of the system. The empirical validation of the SSFD function modeling framework is discussed in relation to its application to two case studies: a benchmark problem (glue gun) set for the engineering design community; and an industrial case study of an electric vehicle powertrain. Based on the evidence from the two case studies presented in the paper, a critical evaluation of the SSFD function modeling methodology is discussed based on the function benchmarking framework established by Summers et al. (2013), considering the representation, modeling, cognitive, and reasoning characteristics. The significance of this paper is that it establishes a rigorous reference framework for the SSFD function representation and a consistent methodology to guide the practitioner with its deployment, facilitating its impact to industrial practice.

This article describes an implementation of a particular design of experiment (DoE) plan based upon optimal Latin hypercubes that have certain space-filling and uniformity properties with the goal of maximizing the information gained. The feature emphasized here is the concept of simultaneous model building and model validation plans whose union contains the same properties as the component sets. Two Latin hypercube DoE are constructed simultaneously for use in a meta-modelling context for model building and model validation. The goal is to optimize the uniformity of both sets with respect to space-filling properties of the designs whilst satisfying the key concept that the merged DoE, comprising the union of build and validation sets, has similar space-filling properties. This represents a development of an optimal sampling approach for the first iteration—the initial model building and validation where most information is gained to take the full advantage of parallel computing. A permutation genetic algorithm using several genetic operator strategies is implemented in which fitness evaluation is based upon the Audze-Eglais potential energy function, and an example is presented based upon the well-known six-hump camel back function. The relative efficiency of the strategies and the associated computational aspects are discussed with respect to the quality of the designs obtained. The requirement for such design approaches arises from the need for multiple calls to traditionally expensive system and discipline analyses within iterative multi-disciplinary optimisation frameworks.

The effective deployment of FMEAs within complex automotive applications faces a number of challenges, including the complexity of the system being analysed, the need to develop a series of coherently linked FMEAs at different levels within the systems hierarchy and across intrinsically interlinked engineering disciplines, and the need for coherent linkage between critical design characteristics cascaded through the systems levels with their counterparts in manufacturing.The approach presented in this paper to address these challenges is based on a structured Failure Mode Avoidance (FMA) framework which promotes the development of FMEAs within an integrated Systems Engineering approach. The effectiveness of the framework is illustrated through a case study, centred on the development of a diesel exhaust aftertreatment system. This case study demonstrates that the structured FMA framework for function analysis supports an effective decomposition of complex interdisciplinary systems facilitating the DFMEA deployment through a series of containable, structured DFMEAs developed at successive system levels, with clear vertical integration of functional requirements and critical parameters cascade.The paper also discusses the way in which the approach supports deployment across engineering disciplines and domains, ensuring the integrity of information flow between the design and manufacturing activities.

Automotive Product Development organisations are challenged with ever increasing levels of systems complexity driven by the introduction of new technologies to address environmental concerns and enhance customer satisfaction within a highly competitive and cost conscious market. The technical difficulty associated with the engineering of complex automotive systems is compounded by the increase in sophistication of the control systems needed to manage the integration of technology packages. Most automotive systems have an electro-mechanical structure with control and software features embedded within the system. The conventional methods for design analysis and synthesis are engineering discipline focused (mechanical, electrical, electronic, control, software). Multi-disciplinary systems engineering design requires a more coherent approach to the concurrent development of systems and their integration on a functional basis, supporting the flawless delivery of customer required functions in a robust and reliable manner. This paper presents an integrated framework for multi-disciplinary systems engineering design underpinned by a Failure Mode Avoidance (FMA) process that ensures coherent information flow within the product creation process. The FMA process is based on the analysis and cascade of functional requirements and associated function failure modes, along with robust countermeasure development and effective design verification. A case study, based on the development of a diesel exhaust aftertreatment system, is used to provide an illustration of the application of this framework and the associated engineering tools that underpin the FMA process within a multi-disciplinary system design context. The case study demonstrates the integration of the FMA process with the systems engineering design and product development process flow, supporting the achievement of right first time through design. The paper also includes a reflection on the practical benefits and challenges associated with the implementation of this framework on a large scale within a PD organisation.