Training of Spatial Ability on Engineering Students Through a Remedial Course Based on Augmented Reality

This paper presents the results of a study designed to evaluate the effect of attending an intensive remedial course based on desktop augmented reality exercises to improve the spatial ability of freshman engineering students. Many of these students have problems in managing visual information or in creating mental models of objects represented by their orthographic projections. The study reports about research on comparison tests about the spatial skills of engineering students from two Spanish universities before and after performing a specific training for improving these abilities. The training was completed by 66 students as participants, considering a control group composed of 25 students from both universities. Results show that students from both universities improve their spatial ability and there is no statistical significance between students from both universities, neither before nor after training, so we may conclude that training’s effect on both universities is analogue.

Confirmation and Cognitive Bias in Design Cognition

The desire to better understand design cognition has led to the application of literature from psychology to design research, e.g., in learning, analogical reasoning, and problem solving. Psychological research on cognitive heuristics and biases offers another relevant body of knowledge for application. Cognitive biases are inherent biases in human information processing, which can lead to suboptimal reasoning. Cognitive heuristics are unconscious rules utilized to enhance the efficiency of information processing and are possible antecedents of cognitive biases. This paper presents two studies that examined the role of confirmation bias, which is a tendency to seek and interpret evidence in order to confirm existing beliefs. The results of the first study, a protocol analysis involving novice designers engaged in a biomimetic design task, indicate that confirmation bias is present during concept generation and offer additional insights into the influence of confirmation bias in design. The results of the second study, a controlled experiment requiring participants to complete a concept evaluation task, suggest that decision matrices are effective tools to reduce confirmation bias during concept evaluation.

Incorporating Heterogeneous Customer Preferences With Dirichlet Process Mixture Model for Product Positioning in Environmentally Conscious Design

Increased environmental protection legislation forces manufacturers to employ environmentally conscious design and manufacturing methods. In addition, customer preferences for energy efficient and environmentally sustainable products influence manufacturers design strategies. In order to influence customer buying behaviors for environmentally friendly products, manufacturers need to understand customer preferences first. Manufacturers can make optimal design decisions based on inference on customers’ decision making models. It is recognized that consumers are heterogeneous in their response to different attributes for any given type of product or service. In this paper, we proposed a framework for incorporating heterogeneous customer preferences with Dirichlet Process mixture model for product positioning in environmentally conscious design. The uncertainty about the functional form of the customer preference distribution can be expressed by using a nonparametric prior, in which the number of clusters grows without bound as the amount of data grows. An automobile design case study is used here to demonstrate the proposed approach.

Technology Evolution Modeling and Decision Making Under Uncertainty

Design is an uncertain human activity involving decisions with uncertain outcomes. Sources of uncertainty in product design include uncertainty in modeling methods, market preferences, and performance levels of subsystem technologies, among many others. The performance of a technology evolves over time, typically exhibiting improving performance. As the performance of a technology in the future is uncertain, quantifying the evolution of these technologies poses a challenge in making long-term design decisions. Here, we focus on how to make decisions using formal models of technology evolution. The scenario of a wind turbine energy company deciding which technology to invest in demonstrates a new technology evolution modeling technique and decision making method. The design of wind turbine arrays is a complex problem involving decisions such as location and turbine model selection. Wind turbines, like many other technologies, are currently evolving as the research and development efforts push the performance limits. In this research, the development of technology performance is modeled as an S-curve; slowly at first, quickly during heavy research and development effort, and slowly again as the performance approaches its limits. The S-curve model typically represents the evolution of just one performance attribute, but designers generally deal with problems involving multiple important attributes. Pareto frontiers representing the set of optimal solutions that the decision maker can select from at any point in time allow for modeling the evolution of technologies with multiple attributes. As the performance of a technology develops, the Pareto frontier shifts to a new location. The assumed S-curve form of technology development allows the designer to apply the uncertainty of technology development directly to the S-curve evolution model rather than applying the uncertainty to the future performance, giving a more focused application of uncertainty in the problem. The multi-attribute technology evolution modeling technique applied in decision-making gives designers greater insight when making long-term decisions involving technologies that evolve.

Bayesian Inference for the Demand of Engineering Products

Within the context of a profit making firm, the job of a design engineer is to choose design parameters and product attributes that maximize the expected utility of profit. To do this effectively, the engineer needs to have an estimate of the demand for the product as a function of its price and its attributes. The firm may conduct a survey to elicit consumer preferences for the product at a given price and would like to update their belief about demand given the survey data. The purpose of this paper is to present a Bayesian methodology for demand estimation that meets this need. The estimation process begins with a prior probability distribution of demand at a given price. Using Bayesian analysis, we show how to update demand for the product given various pieces of information such as market analysis, polls and a variety of other methods. We also discuss situations where consumers can demand multiple units of the product at the given price.

Managing Dilemmas Embodied in 21st Century Engineering

In this session we describe in four parts the pedagogy and out-comes of a course Designing for Open Innovation designed to empower 21st century engineering students to develop competencies associated with innovating in an inter-connected technologically flat world: 1. Competencies for Innovating in the 21st Century, [1]. 2. Developing Competencies In The 21st Century Engineer, [2]. 3. Identifying Dilemmas Embodied in 21st Century Engineering, [3]. 4. Managing Dilemmas Embodied in 21st Century Engineering - this paper. In the first paper we describe the core characteristics of the engineering in an interconnected world and identify the key competencies and meta-competencies that 21st century engineers will need to innovate and negotiate solutions to issues associated with the realization of systems. In the second paper, we describe our approach to fostering learning and the development of competencies by an individual in a group setting. We focus on empowering the students to learn how to learn as individuals in a geographically distanced, collaborative group setting. We assert that two of the core competencies required for success in the dynamically changing workplace are the competencies to first identify and then to manage dilemmas. In the third paper, we illustrate how students have gone about identifying dilemmas and in the fourth paper how they have attempted to manage dilemmas. In papers three and four students have briefly described the challenges that they faced and their takeaways in the form of team learning and individual learning. In this the last of four papers in this session, we focus on how students learned to manage dilemmas associated with the realization of complex, sustainable, socio-techno-eco systems, namely, energy policy design. The example involves the identification of a bridging fuel that balances environmental, economic and socio-cultural concerns. The principal outcome is clearly not the result attained but a student’s ability to learn how to learn as illustrated through the development of personal competencies in a collaborative learning framework and environment.

A Method to Calculate Function and Component Failure Distributions Using a Hierarchical Bayesian Model and Frequency Weighting

This paper presents a method to calculate function and component parameter distributions during the design process. Frequency Weighting, a unique style of weighting proposed in this research, is applied to a Hierarchical Bayesian model to account for the number of times a component has solved a function. During the design process, functions are systematically solved by components to transition from a functional model to a physical design. This research contributes to an ongoing effort toward predicting reliability in early design, specifically during functional modeling and concept generation. In general, reliability prediction methods are applied after concept generation. There currently does not exist a statistical method to calculate functional failure rates to aid reliability prediction during and before concept generation. The method presented in this paper also captures uncertainty in the early stages of design. This is important because uncertainty in this stage of the design process can be significant. A description of the process used to calculate the function and component level failure rate distributions is presented. The level of detail provided is meant for reapplication to other examples. Three examples are worked out and graphical results are presented. These results show an effect of the Frequency Weighting on the function level distribution. Changing the occurrence vector, which is used to show the number of times a set of components has solved a function, from (1, 1, 1, 1) to (1, 1, 2, 5) results in the function level distribution mean value shifting from 5.53E−06 to 4.84E−06. In addition, an example is provided to demonstrate how this method can be applied while components are being selected during the design process. A two part reliability goal is generated for the combined failure rate of the design and the probability a design will meet that goal. Function level distributions are used to show which components should initially be selected to maintain reliability values that meet the reliability goal. Combinations of compatible component level distributions are also used to calculate a combined failure rate distribution for each design. A probability is calculated for each distribution to show which designs meet the probability portion of the reliability goal.

Perception and Interpretation: Points of Focus in Design Sketches

During the design of a product, designers may show a potential customer or other stakeholder a drawing of a design concept in order to elicit feedback that can be used to inform further development of the design. Designers may desire feedback on specific aspects of a concept, such as its shape or size, but viewers may in fact focus on other elements of the drawing itself, such as color or surface texture. Viewers translate their visual perception of these representations into perceived understanding, but how can we know whether their interpretations are consistent with the designer’s intention? This paper evaluates the translation of four different product sketches by 163 participants. This study also considers how aesthetic preference and concrete information might influence a viewer’s opinion of an object. Results suggest that viewers were likely to recall physical aspects from a sketch of a product (material, shape) as well as its function. Findings also suggest that individuals preferred images that were overall more informative rather than aesthetically pleasing. Additionally, our research suggests that individuals were more likely to recall the texture, material or perceived efficiency of an object than recall the name of the object, its function, or its shape.

Philosophical Perspectives for Strategic Innovation Models and Comprehensive Analysis Processes

Many tools and methods claim to be “innovative”. Most belong either to project management, engineering design or creativity approaches. “Innovation Management” literature usually discusses “success patterns” for Innovation based on case studies, but hardly process the comprehensive support of innovation activities. It seems that there is a strategic gap between traditional idea-realization processes that focus on reliable project management and the diffuse situation in ever faster changing environments with unclear opportunities and risks. To professionally reinforce strategic innovation activities it is necessary to define a resilient framework. This paper discusses a new view on the field of innovation that is based on the comprehensiveness of philosophy. Fundamental definitions of early philosophers on the interdependencies of the “co-evolution of the world” are applied to define an “Innovation Philosophy”. This is transformed into an “Innovation Strategy” that comprises a repeatable “Innovation Process” for guiding teams through Innovation Projects.