Over the last decades, finite element analysis (FEA) has become a standard tool in industrial product development, allowing for virtual analysis of designs, quick turnaround times and prompt implementation of results. Although academic research also provides numerous approaches for evaluating a product’s robustness towards geometrical, material and load variations based on FEA analyses, this, however, stands in striking contrast to the limited use of these FEA-based variation simulations in industry. In order to bridge the existing gap between academic research and industrial application, this paper explores the barriers that limit the adoption of FEA-based variation simulation. The investigation is based on interviews with five lead engineers, followed by a case study that details the underlying technical challenges and allows for some initial suggestions for future solutions. The case study involves a sterile canister with seven geometrical variables. The design objective is to ensure sufficient sealing within the range of expected probabilistic variation. The combined study details the identified main barriers for a wider application, that is, the lack of robust CAD, practical guidelines to select an efficient design of experiments for design purposes, and the complexity of the automated processes. From a technical perspective, the case study results in estimations for main and interaction effects, an accurate metamodel and Monte Carlo simulations of 100,000 samples providing the design engineer with more detailed and actionable insights on the performance of the product compared with the traditional nominal or best/worst case simulations.

‘Openness’ is one of the key concepts brought forward by postindustrial narratives questioning the modern repartition of roles between industries and customers. In these narratives, citizen participation in design and intellectual property management based on open source principles are the promise of more sustainable production models. In this context, openness in product design and development has been the object of growing interest and experimentation from academia, businesses and grassroots communities. As a result, numerous concepts emerged that attempt to grasp the essence of this phenomenon, unfortunately leading to overlapping, conflicting or speculative depictions. In this article, we share the understanding we gained throughout 6 years of research on Open Design and Open Source Hardware and attempt to make the difference between myths and facts. We depict an enthusiastic but realistic picture of Open Design and Open Source Hardware practices as we could observe them and deliver a structured framework to situate concepts and their differences. From this, we share seven observations leading to seven corresponding research questions and establish a research agenda to stimulate further investigations into this socially relevant and potentially ground-breaking phenomenon.

The design of a connected engineered system requires numerous design decisions that influence one another. In a connected system that comprises numerous interacting decisions involving concurrency and hierarchy, accounting for interactions while also managing uncertainties, it is imperative to make robust decisions. In this article, we present a method for robust design using coupled decisions to identify design decisions that are relatively insensitive to uncertainties. To account for the influence among decisions, design decisions are modelled as coupled decisions. They are defined using three criteria: the types of decisions, the strength of interactions and the decision levels. In order to make robust decisions, robust design methods are classified based on sources of uncertainty, namely, Type I (noise factors), Type II (design variables) and Type III (function relationship between design variables and responses). The design of a one-stage reduction gearbox is used as a demonstration example. To illustrate the proposed method for robust design using coupled decisions, we present the simultaneous selection of gear material and gearbox geometry in a coupled decision environment while managing the uncertainties involved in designing gearboxes.

Much of the literature on biologically inspired design makes two, often unstated and largely unexamined, assumptions: (i) The process of biologically inspired design is independent of the biological domain, and (ii) the design process leads to multifunctional designs. In this paper, we perform a meta-analysis of 74 case studies of biologically inspired design in the Design Study Library. We begin by noting that biologically inspired design has two core processes: problem-driven design and solution-based design. We find that the first assumption about the domain independence of these design processes is questionable. Our analysis indicates that the problem-driven process of biologically inspired design is more prevalent in some domains, whereas the solution-based design process is more common in other domains. Our analysis also indicates that the solution-based process leads to multifunctional designs more often than the problem-driven process. These findings may have useful implications not only for building information-processing theories of biologically inspired design, but also for developing pedagogical techniques for teaching about the paradigm and computational tools for supporting its practice.

This paper develops theoretical foundations for extending Gauss–Hermite quadrature to robust design with computer experiments. When the proposed method is applied with m noise variables, the method requires 4m + 1 function evaluations. For situations in which the polynomial response is separable, this paper proves that the method gives exact transmitted variance if the response is a fourth-order separable polynomial response. It is also proven that the relative error mean and variance of the method decrease with the dimensionality m if the response is separable. To further assess the proposed method, a probability model based on the effect hierarchy principle is used to generate sets of polynomial response functions. For typical populations of problems, it is shown that the proposed method has less than 5% error in 90% of cases. Simulations of five engineering systems were developed and, given parametric alternatives within each case study, a total of 12 case studies were conducted. A comparison is made between the cumulative density function for the hierarchical probability models and a corresponding distribution function for case studies. The data from the case-based evaluations are generally consistent with the results from the model-based evaluation.