This paper presents the results of a study that explores the effect of a visual constraint on design behaviors of architecture students. To examine this effect, 24 second-year architecture students volunteered to participate. Each of them undertook similar conceptual design briefs in two different conditions, one with and another without a visual constraint. Retrospective reporting was used to collect the verbalization of participants. The FBS ontology was used to model the design cognition of the participants by coding their design protocols. A dynamic analysis was used to study the differences between the two conditions based on the problem–solution index. A further index, the pre-structure–post-structure index, was proposed to measure design behavior differences between the two conditions. The correspondence analysis was used to explore the effect of gender. There were statistically significant differences in the distributions of cognitive effort between the two groups. These differences include in the visual constraint group a decrease in the focus on behavior before structure and in the processes related to it, compared to the non-visual constraint group. The non-visual constraint group changed their focus on problem framing and solving while adding a visual constraint led participants to focus simultaneously on both framing and solving. The visual constraint group had a different attention temporally to pre- and post-structure design processes during designing than the non-visual constraint group. The order of experiencing the two design sessions had only a small effect. The results of correspondence analysis demonstrate that there are categorical gender differences not found using statistical testing.

Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.

Ti–6Al–4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti–6Al–4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost- and time-effective way.

The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.

Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

A deeper understanding of creativity and design is essential for the development of tools to improve designers’ creative processes and drive future innovation. The objective of this research is to evaluate the effect of physical activity versus movement in a virtual environment on the creative output of industrial design students. This study contributes a novel assessment of whether the use of virtual reality can produce the same creative output within designers as physical activity has been shown to produce in prior studies. Eighteen industrial design students at the Georgia Institute of Technology completed nine design tasks across three conditions in a within-subjects experimental design. In each condition, participants independently experienced one of three interventions. Solutions were scored for novelty and feasibility, and self-reported mood data was correlated with performance. No significant differences were found in novelty or feasibility of solutions across the conditions. However, there are statistically significant correlations between mood, interventions, and peak performance to be discussed. The results show that participants who experienced movement in virtual reality prior to problem solving performed at an equal or higher level than physical walking for all design tasks and all designer moods. This serves as motivation for continuing to study how VR can provide an impact on a designer's creative output. Hypothesized creative performance with each mode is discussed using trends from four categories of mood, based on the combined mood characteristics of pleasantness (positive/negative) and activation (active/passive).

A user interface (UI) design that meets the preferences, differences, and needs of the group of users can potentially increase the usability of a system. Users, in general, feel more familiar with the context that reflects their cultural values and practices. The Arabic culture plays a significant role in how Arab users interact and communicate with technologies. The customs, artifacts, and traditions of the Arab world are different in nature from the Western cultures. Thus, it is essential to consider these differences when designing the UI prototype. This study investigated the role of certain cultural preferences in the design of UI for Arab users. A think-aloud approach and Hofstede's cultural dimensions were used on 23 Arab users to generate the necessary design guidelines for the UI of mobile health application. Then, 78 participants were recruited to evaluate the proposed UI design. The usability results showed high satisfaction among Arab users about the role of culture in the design of the UI. Findings from this study can be used by designers and developers to aid their design of UI for group-specific cultural preferences and values.