Early consideration of failure modes in the feature development process is essential to identify and trace risks across the physical and embedded AI components of intelligent systems, to enhance the robustness of the feature delivery as well as trust in the AI. This paper introduces an extension of the AIAG/VDA function failure modes taxonomy, to facilitate the integrated analysis of complex intelligent systems with embedded AI. A case study of an autonomous driving feature is discussed as validation of the proposed taxonomy.

As digitalization progresses, the development and integration of sensory functions in technical systems become increasingly important. Managing uncertainty, especially in the early phase of this process, is crucial to ensure the reliability of the data provided. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was presented in prior works. In this contribution, the effectivity of the framework is evaluated by applying it to a sensory function for rotational speed and offset measurement of a disk pack coupling using sensor integrating bolts.

In this study a robust design method is developed for extracting Li from boron (B) clays with the aim of minimizing cost and maximizing productivity. Lithium is commercially extracted from brines and certain minerals. Its extraction from clays has previously been found to be expensive, a major part of the extraction cost being attributed to the raw materials used. In this study, raw materials from lower-cost resources are used without applying any standardization to them and this might increase variation in the results. To minimize the variation, and achieve high extraction levels, robust design, statistical design and analysis of experiments, and response surface methodologies are utilized. As a result, consistently higher extraction levels have been achieved compared to previous studies. The experiments were conducted using the Bigadiç boron clay fields in Turkey. However, the method is generally applicable to other cases also.

Conservation monitoring is paramount for the gathering of information on species and populations in need of conservation actions. However, monitoring of the mountain bongo Tragelaphus eurycerus isaaci, a Critically Endangered antelope endemic to Kenya, has hitherto been limited to surveillance (i.e. focused on persistence of the species in particular areas), thus limiting the information that could be retrieved on the status and trends of these populations. Using a newly developed identification system, we implemented robust design mark–recapture using existing camera-trap records of four wild subpopulations of the bongo. We provide data on sex and age structure and the first estimates of population size in the wild. Males and calves seem to be suffering higher mortality than females, and only two of the four monitored populations include adults of both sexes and calves. The combined size of these two breeding populations is estimated to be 29–50. Our findings confirm the critical situation of the mountain bongo in the wild and highlight the need for conservation actions to reinforce the wild populations for the long-term conservation of this antelope.

When integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error proneness in order to increase the acceptance of the framework in practice. First, the identification of uncertainty is improved by means of an extended effect graph for an automated identification of disturbance factor induced data and model uncertainty. Second, the significance of the subsequent evaluation of uncertainty is enhanced by replacing the initially proposed local sensitivity analysis with a global sensitivity analysis. Finally, a flowchart is proposed that supports the identification of applicable and promising strategies for the development of measures to consider critical disturbance factor induced uncertainty.

Resilience is a widely studied concept that is a key objective in the design and development of sustainable systems. This is especially true for the agricultural systems critical to food production, economic viability, and sustainability of our communities, as farmers seek to meet increasing demand in the face of shocks such as climate change and natural disasters. Although there is a rich body of work examining resilience, there is limited understanding of how the concept of resilience should be tailored for agricultural systems. This study seeks to address this gap by performing a systematic literature review of 50 papers selected from SCOPUS using the PRISMA protocol. A summary of research topics and characteristics by geographical region is presented. The paper also categorizes the types of shocks studied and the corresponding response methods. Results suggest that the focus of resilience research changes by region, which may indicate that design strategies and objectives should also differ by region. Furthermore, the work identifies a need for more simulation-based quantitative research into the impact of resilience.

Robust Design offers a coherent and widely appreciated approach for the parametric exploration of the design space by means of simulation or experimentation, which is well-established in the quality-by design domain. From the perspective of design research, however, this only addresses a relatively narrow part of the design process and is not fully integrated with other design decisions such as concept exploration, the suitable configuration of system elements, or the design of interfaces. Particularly in light of the growing importance of developing technologically advanced and “smart” systems, it seems that a new methodical perspective on Robust Design is needed. Against this background, this paper consolidates knowledge and insights from different research fields and industry sectors. On this basis, new angles to the discussion on product robustness in different domains are explored in order to suggests directions for action and new research areas, both with respect to a methodical RD approach as well as the question of systematic research procedures.

The early use of Robust Design (RD) supports the development of product concepts with low sensitivity to variation, which offers advantages for reducing the risk of costly iterations. Due to the lack of approaches for early evaluation of product robustness, the embodiment-function-relation and tolerance (EFRT-) model was developed, which combines the contact and channel approach and tolerance graphs. The information exchange of both approaches offers a high potential for reliable robustness evaluation results. However, that potential currently relies unused, since the link between applicable robustness criteria and the extended information is missing. To solve this problem, four research steps were determined: (1) understanding of robustness, (2) collection of RD principles, (3) identification of EFRT-model information and (4) mapping of RD principles and information. The results show nine adapted RD principles, the identified model information for the robustness evaluation, the evaluation criteria as well as their mapping. Utilizing the mapping and the proposed criteria in this contribution, a more comprehensive robustness evaluation in early stages is enabled.

The variation management and product quality processes are important tasks to guarantee the assemblability of the systems, the scrap reduction and to avoid delays on production and launching. The compound of activities in Geometric Dimensioning and Tolerancing (GD&T) are necessary, especially in the early design stages, to take into account variations of different nature and from different sources. In this paper, an approach for considering the loading conditions in a polyhedral-based approach in tolerancing design is presented. The load boundary conditions are represented as additional displacement restrictions in the deviation space. The restrictions imposed by the physical limits of a system, the ones coming from the loading conditions and the degrees of freedom (DoF) can be all described and represented with a single polyhedron operand. The approach is illustrated using a simplified 2-D model for both ideal and non-ideal geometry. A 3-D model describing an unilateral contact is presented as a case study using Skin Model Shapes. By taking into account geometrical form defects, external loads, and the kinematics of the system, its sensitivity to variations can be reduced even from early design stages.

In today's VUCA-World it is necessary to consider future requirements to develop change- and future-robust future products, especially regarding the increasing demand for sustainable solutions. In order to address this situation, upgradeability of modular products can be a solution. Considering that elements of modular products are used in several different products and over a long period of time, there is a need to act on this challenge. To uncover areas with a need for action, a systematic literature review on upgradeable and modular products was conducted. After resolving four fields of action and under consideration of the need for sustainable products, another systematic literature review examined the solution space of upgradable modular product architecture. In conclusion, several influencing factors on the upgradeable design of modular products could be identified, which are presented in this work.

The evaluation of impact of collaborative research on robust design methodologies and methods is important to both academic and industry stakeholders. This paper introduces a framework for impact evaluation which combines the broader framework adopted for the academic research impact assessment with the organisation viewpoint centred on business results, process improvement and product development teams capability improvement. A large scale empirical study conducted with evidence from technical reports on workplace projects from an automotive OEM proved the validity of the proposed framework.

This paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.

Predicting the impact of changes in a design can be challenging, especially for complex designs. Margins are often built into the designs which can absorb the knock-on effect of such changes, erroneously allocating which can however, lead to propagation. A method for localising and sizing margins in an incremental design context is the Margin Value Method. This paper adapts MVM in the context of uncertainty in input specifications. It discusses possible ways to allocate them in a design such that undesirable effects of margins are minimised while preventing change propagation.

Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori through a sensitivity analysis, which does not guarantee optimality in terms of robustness. This article introduces an automated design framework based on multiobjective optimisation to evaluate robustness as an additional competing objective. Robustness is computed as a sampled hypervolume of imposed geometrical and operational deviations from the nominal point. In order to address the high number of additional evaluations needed to compute robustness, artificial neutral networks are used to generate fast and accurate surrogates of high-fidelity models. The identification of their hyperparameters is formulated as an optimisation problem. In the frame of a case study, the developed methodology was applied to the design of a small-scale turbocompressor. Robustness was included as an objective to be maximised alongside nominal efficiency and mass-flow range between surge and choke. An experimentally validated 1D radial turbocompressor meanline model was used to generate the training data. The optimisation results suggest a clear competition between efficiency, range and robustness, while the use of neural networks led to a speed-up by four orders of magnitude compared to the 1D code.

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.

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.

Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

The success of complex product development projects strongly depends on the clear definition of target factors that allow a reliable statement about the fulfilment of the product requirements. In the context of tolerancing and robust design, Key Characteristics (KCs) have been established for this purpose and form the basis for all downstream activities. In order to integrate the activities related to the KC definition into product development as early as possible, the often vaguely formulated requirements must be translated into quantifiable KCs. However, this is primarily a manual process, so the results strongly depend on the experience of the design engineer.

In order to overcome this problem, a novel computer-aided approach is presented, which automatically derives associated functions and KCs already during the definition of product requirements. The approach uses natural language processing and formalized design knowledge to extract and provide implicit information from the requirements. This leads to a clear definition of the requirements and KCs and thus creates a founded basis for robustness evaluation at the beginning of the concept design stage. The approach is exemplarily applied to a window lifter.

This paper seeks to address the gap between qualitative Robust Design principles and parameter optimization. The former often fails to consider the challenging amount of details in embodiment and configuration design, while the latter is the widely accepted main thrust in traditional Robust Design. The gap is addressed by exploring the value of five quantitative robustness indicators for Design Space Exploration based on variables, objectives and constraints: The set level indicators, Design Space Size and Pareto Set Dispersion, and the point level indicators, Neighbourhood Performance, Failure Rate and Distance to Failure. As a background for the discussion of the limitations of these indicators an industrial case is presented. The case is an incremental encoder and includes two configurations for comparison, five objectives, eight variables, and a range of constraints. The design spaces are sampled and they show conflicting objectives, dispersed spaces and variables dependencies. Based on this it is suggested that set level indicators are more suitable than point level indicators of early robustness evaluation, but the available indicators are limited in their considerations of design space discontinuity and conflicts.

Combinatorial Design such as configuration design, design optioneering, component selection, and generative design, is common across engineering. Generating solutions for a combinatorial design task often involves the application of classical computing solvers that can either map or navigate design spaces. However, it has been observed that classical computing resource power-law scales with many design space models. This observation suggests classical computing may not be capable of modelling our future design space needs.

To meet future design space modelling needs, this paper examines quantum computing and the characteristics that enables its resources to scale polynomially with design space size. The paper then continues to present a combinatorial design problem that is subsequently represented, constrained and solved by quantum computing. The results of which are the derivation of an initial set of circuits that represent design space constraints. The study shows the game-changing possibilities of quantum computing as an engineering design tool and is the start of an exciting new journey for design research.