A multistudy approach is presented that allows design thinking of complex systems to be studied by triangulating causal controlled lab findings with coded data from more complex products. A case study illustration of this approach is provided. During the conceptual design of engineering systems, designers face many cognitive challenges, including design fixation, errors in their mental models, and the sunk cost effect. These factors need to be mitigated for the generation of effective ideas. Understanding the effects of these challenges in a realistic and complex engineering system is especially difficult due to a variety of factors influencing the results. Studying the design of such systems in a controlled environment is extremely challenging because of the scale and complexity of such systems and the time needed to design the systems. Considering these challenges, a mixed-method approach is presented for studying the design thinking of complex engineering systems. This approach includes a controlled experiment with a simple system and a qualitative cognitive-artifacts study on more complex engineering systems followed by the triangulation of results. The triangulated results provide more generalizable information for complex system design thinking. This method combines the advantages of quantitative and qualitative study methods, making them more powerful while studying complex engineering systems. The proposed method is illustrated further using an illustrative study on the cognitive effects of physical models during the design of engineering systems.

In reality, the behavior of processes is sometimes vague and the observed data is irregular. This research proposes an approach for optimizing fuzzy multiple responses using fuzzy regression. In this approach, each response repetition is transformed into a signal to noise ratio then modeled using statistical multiple regression. A trapezoidal fuzzy regression model is formulated for each response utilizing the statistical regression coefficients. The most desirable response values and the deviation function are determined for each response. Finally, four optimization models are formulated for the trapezoidal membership fuzzy number to obtain the optimal factor level at each number. Two case studies are adopted for illustration, where excluding response fuzziness will result in misleading optimal factor settings if solved by the traditional optimization techniques. In conclusion, the proposed approach based on fuzzy regression approach can successfully optimize fuzzy multiple responses in a wide range of manufacturing applications on the Taguchi's method. Moreover, compared to other approaches, such as data envelopment analysis and grey relational analysis, the proposed approach has the distinct advantage of being able to generate models using only a small number of experimental data sets and minimizing inherent variations.

Architectural spatial design is a wicked problem that can have a multitude of solutions for any given brief. The information needed to resolve architectural design problems is often not readily available during the early conceptual stages, requiring proposals to be evaluated only after an initial solution is reached. This “solution-driven” design approach focuses on the generation of designs as a means to explore the solution space. Generative design can be achieved computationally through parametric and algorithmic processes. However, utilizing a large repertoire of organiational patterns and design precedent knowledge together with the precise criteria of spatial evaluation can present design challenges even to an experienced architect. In the implementation of a parametric design process lies an opportunity to supplement the designer's knowledge with computational decision support that provides real-time spatial feedback during conceptual design. This paper presents an approach based on a generative multiperformance framework, configured for generating and optimizing architectural designs based on a precedent design. The system is constructed using a parametric modeling environment enabling the capture of precedent designs, extraction of spatial analytics, and demonstration of how populations can be used to drive the generation and optimization of alternate spatial solutions. A pilot study implementing the complete workflow of the system is used to illustrate the benefits of coupling parametric modeling with structured precedent analysis and design generation.

Massive open online courses, online tutoring systems, and other computer homework systems are rapidly changing engineering education by providing increased student feedback and capitalizing upon online systems' scalability. While online homework systems provide great benefits, a growing concern among engineering educators is that students are losing both the critical art of sketching and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). For example, some online systems allow the drag and drop of forces onto FBDs, but they do not allow the user to sketch the FBDs, which is a vital part of the learning process. In this paper, we discuss Mechanix, a sketch recognition tool that provides an efficient means for engineering students to learn how to draw truss FBDs and solve truss problems. The system allows students to sketch FBDs into a tablet computer or by using a mouse and a standard computer monitor. Using artificial intelligence, Mechanix can determine not only the component shapes and features of the diagram but also the relationships between those shapes and features. Because Mechanix is domain specific, it can use those relationships to determine not only whether a student's work is correct but also why it is incorrect. Mechanix is then able to provide immediate, constructive feedback to students without providing final answers. Within this manuscript, we document the inner workings of Mechanix, including the artificial intelligence behind the scenes, and present studies of the effects on student learning. The evaluations have shown that Mechanix is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis; focus groups with students who used the program have revealed that they believe Mechanix enhances their learning and that they are highly engaged while using it.

This paper presents the use of data clustering methods applied to the analysis results of a design-stage, functional failure reasoning tool. A system simulation using qualitative descriptions of component behaviors and a functional reasoning tool are used to identify the functional impact of a large set of potential single and multiple fault scenarios. The impact of each scenario is collected as the set of categorical function “health” states for each component-level function in the system. This data represents the space of potential system states. The clustering and statistical tools presented in this paper are used to identify patterns in this system state space. These patterns reflect the underlying emergent failure behavior of the system. Specifically, two data analysis tools are presented and compared. First, a modified k-means clustering algorithm is used with a distance metric of functional effect similarity. Second, a statistical approach known as latent class analysis is used to find an underlying probability model of potential system failure states. These tools are used to reason about how the system responds to complex fault scenarios and assists in identifying potential design changes for fault mitigation. As computational power increases, the ability to reason with large sets of data becomes as critical as the analysis methods used to collect that data. The goal of this work is to provide complex system designers with a means of using early design simulation data to identify and mitigate potential emergent failure behavior.

We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms. We analyze the characteristics of the two methods on the basis of three floor plan scenarios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally, we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.

The aim of this work is to explore the relationship between products that mechanically transform and individual environmentally conscious behavior (ECB). Our qualitative study led to observations on how each of the three transformation principles, expand/collapse, expose/cover, and fuse/divide, specifically supports ECBs. As expected, expand/collapse enables better portability of products. Increased portability of reusable products (e.g., travel mugs and shopping bags) reduces reliance on their disposable counterparts. A less expected observation is that increased portability also increases the spontaneity by which ECBs could be carried out. While there are fewer ECB supporting products that incorporate the expose/cover principle, we believe that it enables one to include, yet hide potentially unaesthetic, features that support ECB in often-used or worn items. Finally, we found fuse/divide to enable portability beyond what is possible with expand/collapse alone. Fuse/divide may also make possible other product transport and reuse strategies. We conclude that mechanically transformable products support and enable ECBs, especially when existing infrastructure presents obstacles. Such products may increase the rate of participation in ECB, which then justifies improvements to the shortfalls in infrastructure for which they compensate.

During the design of complex systems, a design process may be subjected to stochastic disruptions, interruptions, and changes, which can be described broadly as “design impulses.” These impulses can have a significant impact on the transient response and converged equilibrium for the design system. We distinguish this research by focusing on the interactions between local and architectural impulses in the form of designer mistakes and dissolution, division, and combination impulses, respectively, for a distributed design case study. We provide statistical support for the “parallel character hypothesis,” which asserts that parallel arrangements generally best mitigate dissolution and division impulses. We find that local impulses tend to slow convergence, but systems also subjected to dissolution or division impulses still favor parallel arrangements. We statistically uphold the conclusion that the strategy to mitigate combination impulses is unaffected by the presence of local impulses.

This paper presents a reference framework for the configuration process. The reference framework is established through an extensive review of existing literature, and as such consolidates an extensive theoretical base. The review of literature shows a broadening of the understanding of the configuration task. The definition of the configuration task is somewhat ambiguous because different research groups define configuration tasks differently. This paper proposes a reference framework for configuration that permits a more precise understanding of a configuration task, a definition of the basic concepts in product configuration, and a total configuration system view that describes how operators come together to perform the configuration task in the configuration process. We will define the product, the product model, the configuration task, and the configuration system, and put the whole thing into perspective with the theory of technical systems, where we describe the configuration process and the different abstraction level of configurations. We will also use our resulting framework to describe sales configuration, technical configuration, and reconfiguration. We do this to synthesize previous work, to clarify and make coherent definitions of relevant terms, to extent the definition of product configuration to include “softer” products like information and service, and finally, to give a comparative framework to analyze work done in the field of product configuration. The total configuration system, together with the definition of key concepts, comprises a strong reference framework when working with, developing, and analyzing configuration systems.

A novel swarm intelligence approach for combinatorial optimization is proposed, which we call probability increment based swarm optimization (PIBSO). The population evolution mechanism of PIBSO is depicted. Each state in search space has a probability to be chosen. The rule of increasing the probabilities of states is established. Incremental factor is proposed to update probability of a state, and its value is determined by the fitness of the state. It lets the states with better fitness have higher probabilities. Usual roulette wheel selection is employed to select states. Population evolution is impelled by roulette wheel selection and state probability updating. The most distinctive feature of PIBSO is because roulette wheel selection and probability updating produce a trade-off between global and local search; when PIBSO is applied to solve the printed circuit board assembly optimization problem (PCBAOP), it performs superiorly over existing genetic algorithm and adaptive particle swarm optimization on length of tour and CPU running time, respectively. The reason for having such advantages is analyzed in detail. The success of PCBAOP application verifies the effectiveness and efficiency of PIBSO and shows that it is a good method for combinatorial optimization in engineering.