Quantification of subjective attributes expressed as functions of design variables is a significant challenge in creating product design optimization models. For example, the objective assessment of a vehicle’s environmental friendliness is typically based on fuel economy and emissions. But some design variables such as a vehicle’s silhouette (twodimensional body shape) may create a subjective perception of environmental friendliness, which may impact consumer preference along with objective metrics. In this paper, we show a method for assessing subjective attributes in the context of design attributes. We focus on perceived environmental friendliness (PEF) and we develop a model of PEF as a function of vehicle silhouette shape variables. The modeling process consists of stimuli development using design of experiments, survey design including direct assessment of the key subjective attribute PEF, elicitation of consumer preference, measurement of purported subjective mechanisms (for the PEF case, whether the design is “inspired by nature”), measurement of respondent characteristics (for the PEF case, environmental attitudes and demographics), statistical analysis of data, and validation. Results for the PEF example indicate that silhouettes perceived as environmentally friendly are the most preferred. Design variables that correlated with PEF were identified and used to generate new designs, which were validated in a follow-up study. Implications of using the general methodology in engineering design are discussed. �DOI: 10.1115/1.4002290�

Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

This paper explores improving sketching skills and reducing the inhibition to sketch for student designers. In the first study, students were taught sketching skills through an in-class workshop. The effect was evaluated using a pre-midpost test (n=40). In the second study, students were led through art activities to reduce their inhibition to sketch. The effect was tested using another pre-midpost test (n=26). The first study found sketching skills increased, but declined with disuse. The second study found reduced inhibition immediately after the workshop, an increase after the sketch skills workshop, and a decrease over the semester. This suggests that sketch training and inhibition-reducing exercises are effective in the short term, but must be emphasized over time for a permanent change.

Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines (1) assume the user has extensive prior knowledge of the process, (2) apply to only a few AM technologies or a very specific application, or (3) describe benefits of the technology that novices already know. In this paper, we present a one-page, visual design for additive manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using most AM processes and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet.

Additive manufacturing (AM) technologies have become integral to the modern manufacturing process. These roles are filled both in prototyping and production. Many studies have been conducted and lists been written on guidelines for AM. While these lists are useful, virtually none are written in a way that is accessible to novice users of AM, such as Makers. Most guidelines assume the user has extensive prior knowledge of the process, apply to only a few AM technologies, or describe benefits of the technology that novices already know. In this paper, we present a short, visual design-for-additive-manufacturing worksheet for novice and intermittent users. It addresses common mistakes and problems as identified by various expert machinists and additive manufacturing facilities. The worksheet helps designers accurately assess the potential quality of a part that is to be made using an AM process by giving intuitive feedback and indirectly suggest changes to improve a design. The immediate benefit of this worksheet is that it can help to streamline designs and reduce manufacturing errors. We validated it in a high-volume 3D-printing facility (Boilermaker Lab) where users are predominantly novice or intermittent. After the worksheet was implemented in the Boilermaker Lab, both the rate of print failures and reprinted parts fell roughly 40%.