Research in Engineering Design

The relationship between geometry (form) and physical behavior (function) dominates many engineering activities. The lack of uniform and rigorous computational models for this relationship has resulted in a plethora of inconsistent (and thus usually incompatible) computer-aided design (CAD) tools and systems, causing unreasonable overhead in time, effort, and cost, and limiting the extent to which CAD tools are used in practice. It seems clear that formalization of the relationship between form and function is a prerequisite to taking full advantage of computers in automating design and analysis of engineering systems. We present a unified computational model of physical behavior that explicitly links geometric and physical representations. The proposed approach characterizes physical systems in terms of their algebraic-topological properties:cell complexes, chains, and operations on them.

Frame creation (framing) faces the challenge of envisioning novel viewpoints that point to value. This paper examines framing and uses Dorst’s definition of frame from the perspective of engineering design theories and methods to highlight three of their key characteristics: (P1) problem-domain independence, (P2) generativity, and (P3) formal definition of a valuable design outcome or process in terms of principles. These properties support two statements. Statement 1: an engineering design theory or method satisfying (P1), (P2) and (P3) holds Dorst’s definition of frame. The design principles on which the engineering design theory or method is based on are the pattern of relationships that characterizes valuable outcomes and processes in that frame. Statement 2: when a designer uses an engineering design theory or method satisfying (P3), the interaction between the design problem frame (A) and the engineering design theory or method frame (B), uncovers a new frame (C) with a definition of value. Illustrative examples based on axiomatic design and C-K theory demonstrate the practical application of these statements.

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.

Design decisions often require input from multiple stakeholders or require balancing multiple design requirements. However, leading axiomatic approaches to decision-based design suggest that combining preferences across these elements is virtually guaranteed to result in irrational outcomes. This has led some to conclude that a single “dictator” is required to make design decisions. In contrast, proponents of heuristic approaches observe that aggregate decisions are frequently made in practice, and argue that this widespread usage justifies the value of these heuristics to the engineering design community. This paper demonstrates that these approaches need not be mutually exclusive. Axiomatic approaches can be informed by empirically motivated restrictions on the way that individuals can order their preferences. These restrictions are represented using “anigrafs”—structured relationships between alternatives that are represented using a graph–theoretic formalism. This formalism allows for a computational assessment of the likelihood of irrational outcomes. Simulation results show that even minimal amounts of structure can vastly reduce the likelihood of irrational outcomes at the level of the group, and that slightly stronger restrictions yield probabilities of irrational preferences that never exceed 5%. Next, an empirical case study demonstrates how anigrafs may be extracted from survey data, and a model selection technique is introduced to examine the goodness-of-fit of these anigrafs to preference data. Taken together, these results show how axiomatic consistency can be combined with empirical correspondence to determine the circumstances under which “dictators” are necessary in design decisions.

The novelty of additive manufacturing (AM) involves new requirements, restrictions and rules, that are considerably different to those of conventional manufacturing methods. Therefore, designers often lack experience and knowledge about identifying AM-suited components. However, to ensure profitability, it is essential to choose components, that are well suited for additive manufacturing. State-of-the-art user-support methods for identifying AM potential widely focus on either economic potential or manufacturability but fail to address both aspects. While machine learning solutions are considered highly efficient, the assessment outcome lacks process transparency, inhibiting troubleshooting, plausibility checks and problem-oriented considerations. This paper proposes a holistic, yet detailed and transparent approach to identify conventionally manufactured parts for AM from an existing product portfolio, enabling decision-making based on quantifiable results. It combines and advances state-of-the-art methods, considering manufacturability, economic profitability and socio-ecological aspects. Besides evaluating AM potential, the method additionally assesses the components' potential for re-design-based enhancement for AM suitability. Besides understanding product functions and present production processes, users are expected to have a basic understanding of company goals. The approach involves inquiries regarding company- and product-specific priorities, enabling a weighted assessment. The weights are determined based on individual company philosophies regarding AM value propositions such as differing stakeholder interests and priorities. Additionally, the approach allows users to investigate different development goals by weighting opportunistic and restrictive assessment. The method application is demonstrated via an assembly comprising 11 parts in a scenario focusing on serviceability, eventually determining suitability statements.

Design methods have been studied by researchers for decades. Academia considers their impact on industry to be insufficient. The objective of this research is to understand the use and impact of design methods in the context of a specific company, Volvo Car Corporation (VCC), by describing the behaviour of engineers in relation to methods, to assist in the future development of design methods and tools. We mainly concentrate on concept selection methods because of their relevance in this company. The data presented is the result of qualitative research carried out during 4 years at VCC, where the authors were located as researchers. The research shows that many methods are employed besides those with an academic name, that some in-company methods used contain improvements to methods researched by academia, that some modifications to academic methods lead to unreliable results, and that there is a lack of objectivity in method modification. For these reasons, the authors suggest further research on understanding the principles of successful and unreliable modification of concept selection methods.

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.

Reverse engineering is a common design strategy in industry. It is a term that has come to encompass a large array of engineering and design activities in the literature; however, in its basic form, reverse engineering is simply the process of extracting information about a product from the product itself. Depending on its use, it may or may not be advantageous to utilize a reverse engineering strategy. As with any rational decision, reverse engineering is only favorable when the benefits from its use outweigh the investment. Therefore, a general understanding of the principles that increase the difficulty or investment required to reverse engineer mechanical products would be helpful for everyone affected by reverse engineering activities. In this paper, we articulate and explore these fundamental principles after reviewing examples from the literature and from our own experience. We then use the principles as a basis for the development of a methodology to build barriers to reverse engineering into new products.