Material selection is a fundamental step in mechanical design that has to meet all the functional requirements of the component. Multiple-attributed decision-making (MADM) processes are already well established to choose the preeminent alternative from the finite set of alternatives, but there is some lack of geometrical evidence if the alternatives are considered as multi-dimensional points. In this paper, a fresh spatial approach is proposed based on nearest neighbor search (NNS) in which the nearness parameter is considered as a Manhattan norm (Taxicab geometry) in turn which is a function of the Euclidean norm and cosine similarity to raise a preeminent alternative under the MADM framework. Cryogenic storage tank and flywheel are considered as two case studies to check the validity of the proposed spatial approach based on NNS in material selection. The result shows the right choice for the cryogenic storage tank is the austenitic steel (SS 301 FH), and for the flywheel, it is a composite material (Kevler 49-epoxy FRP) those are consistent with the real-world practice and the results are compared with other MADM methods of previous works.

This paper presents a novel approach to sensor-based feature evaluation and selection using a self-organizing map and spatial statistics as a combined technique applied to tool condition monitoring of the turning process. This approach takes advantage of the unique features of unsupervised neural networks combined with spatial statistics to perform analyses into the contributions of the different sensor-based features, carrying large quantities of noise, to achieve a classification of tool wear and a quantitative measure of each feature's suitability. This method does not assume a prior direct correlation between features avoiding misconstructions inherent to common approaches that assume that only obviously correlated features should be considered for condition monitoring. Instead, and taking advantage of neural networks ability to perform non-linear modeling, it has allowed a prior modeling of the process and then analyzed each feature's contribution toward classification. It was found that some of the commonly used features have proven to have a significant contribution to the classification of cutting tool wear, whereas others adversely affect classification performance. Further, it is demonstrated that the proposed combined technique can be used extensively to quantitatively evaluate the contribution of different features toward system monitoring in the presence of noisy data.

The aim of our study is to investigate how innovation is taking place through different research and development (R&D) activities and to establish a link between innovation and business sustainability in the context of Indian pharmaceutical companies. Our study is based on the secondary data. Sample data of 37 Indian pharmaceutical companies listed on the National Stock Exchange have been used based on the stratified sampling technique. For empirical analysis we have performed descriptive statistics, correlation matrix, and panel regression analysis as statistical techniques with the help of STATA 12.0 statistical package. R2 value can predict 100 and 98.20% variability in return on assets (ROA) and return on equity (ROE) in model 1 and model 2, respectively. In model 1, the value of c2 is 1.48 and its corresponding p value is 0.00 (<0.05) which means that the model is a good fit for interpretation. R&D intensity is having a positive effect on ROA and the effect is statistically significant at 1% level. Advertising and marketing intensity, capital intensity, leverage ratio and operating expenditure to the total assets ratio are having positive effect on ROA but the effect is not statistically significant. In model 2, the value of F statistics is 8025.62 and its corresponding p value is 0.00 which is <0.05. It means that the model is a good fit for study. R&D intensity is having a positive effect on ROE and the effect is statistically significant at 1% level. Advertising and marketing intensity, capital intensity and operating expenditure to the total assets ratio have positive effect on ROE and the effect is statistically significant at 1% level. Leverage ratio is having a negative effect on ROE but the effect is statically significant at 10% level.

Design protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.

The decisions made during the early stages of a design process have a huge impact on a product. Owing to the explosion of preliminary ideas, however, designers easily lose track of important ideas and significant information and end up being buried in a pile of plain words. Failing to locate an idea in the context of idea generation makes it difficult to generate new ideas or take optimized decisions. In this study, the authors propose the term map approach to provide a complete bird's eye view of all ideas, which is a higher-dimension graphical representation that helps in inspiring ideas and making decisions among design team members. A software application named CoStorm is developed. Through the case study of the cash-flattener module, which is a crucial component of an automated teller machine, this method is found to contribute in facilitating the ideation and decision-making progress.

Sketching is a natural and intuitive communication tool used for expressing concepts or ideas which are difficult to communicate through text or speech alone. Sketching is therefore used for a variety of purposes, from the expression of ideas on two-dimensional (2D) physical media, to object creation, manipulation, or deformation in three-dimensional (3D) immersive environments. This variety in sketching activities brings about a range of technologies which, while having similar scope, namely that of recording and interpreting the sketch gesture to effect some interaction, adopt different interpretation approaches according to the environment in which the sketch is drawn. In fields such as product design, sketches are drawn at various stages of the design process, and therefore, designers would benefit from sketch interpretation technologies which support these differing interactions. However, research typically focuses on one aspect of sketch interpretation and modeling such that literature on available technologies is fragmented and dispersed. In this paper, we bring together the relevant literature describing technologies which can support the product design industry, namely technologies which support the interpretation of sketches drawn on 2D media, sketch-based search interactions, as well as sketch gestures drawn in 3D media. This paper, therefore, gives a holistic view of the algorithmic support that can be provided in the design process. In so doing, we highlight the research gaps and future research directions required to provide full sketch-based interaction support.

Information technologies are a driving force for progress in the design field, allowing new modes of creativity. However, most of the existing computational design tools are focused on the latest stages of the design process and especially directed to drafting operations. Conceptual design tools that support the designer in the creative and inventive early stages of the design project are still in their early development. Shape grammars (SG) were introduced by George Stiny in the 1970s, allowing the generation of designs according to a set of predefined rules. SG computational implementations have the potential to answer the need for tools that can assist designers, architects, and artists in the creative process, offering design alternatives, stimulating new ideas and encouraging the search for new design generation processes. Acknowledging this potential, a user-friendly interface seems essential for the adoption of these tools. Taking Scott Chase's interaction model as background, the aim of the present investigation is to define guidelines and begin to design a graphical-user interface for SG implementations. Inspection methods of human–computer interaction (HCI) were used to analyze existing SG implementations and understand usability issues. Subsequently, HCI ergonomic criteria for interface evaluation were adapted to establish guidelines for the design of an SG implementation interface, called IM-sqi. These guidelines take into account different user groups, adjustable interaction modes for each user group, and the nature of each task performed by the user.

The proposed methodology is based on a (global and multi-criteria) simplified environmental but thorough assessment. In this stage we do not directly give the solution to designers. It will therefore translate the results of evaluation design axes, but in general, the lines proposed are inconsistent or contradictory. Therefore, what we find is a compromise given to the solution. The challenge we are facing in an industrial reality is that one should not go for a compromise solution. TRIZ (Teorija Reshenija Izobretateliskih Zadatch) or the theory of solving inventive problems, in the field, will be reformulated and go through the contradiction matrix and then intervene with the principles of interpretation resolutions to give possible solutions. To assist small and medium enterprises (SMEs) in their product development, the objective of this paper is to propose a methodological approach named Ecatriz, that will allow us to achieve our eco-innovative goal. The applicability of this method is justified by the many contradictions in the choices in a study of the life cycle. As a starting point, a qualitative multi-criteria matrix will allow the prioritization of all impacts on the environment. A customized implementation of the inventive TRIZ (Teorija Reshenija Izobretateliskih Zadatch, Russian acronym for theory of solving inventive problems) principles will help us choose eco-innovative solutions. To that end, we have created a new approach named Ecatriz (ecological approach TRIZ), based on a new contradiction matrix. It was tested in various contexts, such as the “24 h of Innovation” competition and eco-innovative patents.

Assembly optimization of printed circuit boards (PCBs) has received considerable research attention because of efforts to improve productivity. Researchers have simplified complexities associated with PCB assembly; however, they have overlooked hardware constraints, such as pick-and-place restrictions and simultaneous pickup restrictions. In this study, a hybrid group search optimizer (HGSO) was proposed. Assembly optimization of PCBs for a multihead placement machine is segmented into three problems: the (1) auto nozzle changer (ANC) assembly problem, (2) nozzle setup problem, and (3) component pick-and-place sequence problem. The proposed HGSO proportionally applies a modified group search optimizer (MGSO), random-key integer programming, and assigned number of nozzles to an ANC to solve the component picking problem and minimize the number of nozzle changes, and the place order is treated as a traveling salesman problem. Nearest neighbor search is used to generate an initial place order, which is then improved using a 2-opt method, where chaos local search and a population manager improve efficiency and population diversity to minimize total assembly time. To evaluate the performance of the proposed HGSO, real-time PCB data from a plant were examined and compared with data obtained by an onsite engineer and from other related studies. The results revealed that the proposed HGSO has the lowest total assembly time, and it can be widely employed in general multihead placement machines.

Detecting and recognizing objects is one of the most important uses of vision systems in nature and is consequently highly evolved. This paper aims to accurately detect an object using its shape and color information from a complex background. In particular, we evaluated our algorithm to detect 19 different integrated circuits (IC) from 10 different printed circuit boards (PCB) of different colors. We have compared three different shape descriptors for four different color space models. We have evaluated shape detection algorithms in different lighting conditions (indoor, outdoor, and controlled light source) to find suitable illumination for image acquisition. We undertook statistical hypothesis testing to find the effect of color space models and shape descriptors on the accuracy, false positive and false negative rates. While measuring accuracy, we have noted that L*a*b* color space is significantly worse, and the best result is obtained in YCbCr color space using bounding box shape descriptors for 2500 Lux using LED.

To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.

Speech- and gesture-based interfaces for computer-aided design (CAD) modeling must employ vocabulary suitable for target professional groups. We conducted an experiment with 40 participants from architecture and engineering backgrounds to elicit their speech preferences for four CAD manipulation tasks: Scale, Rotate, Copy, and Move. We compiled speech command terms used by participants and analyzed verbalizations based on three analytic themes: the exactness of descriptions, the granularity of descriptions, and the use of CAD legacy terms. We found that participants from both groups used precise and vague expressions in their verbalizations and used a median of three parameters in their verbalizations. Architects used CAD legacy terms more than Engineers in the tasks Scale and Rotate. Based on these findings, we give recommendations for the design of speech- and gesture-based interface for conceptual CAD modeling.

This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all.

We present a novel and trainable gesture-based system for next-generation intelligent interfaces. The system requires a non-contact depth sensing device such as an RGB-D (color and depth) camera for user input. The camera records the user's static hand pose and palm center dynamic motion trajectory. Both static pose and dynamic trajectory are used independently to provide commands to the interface. The sketches/symbols formed by palm center trajectory is recognized by the Support Vector Machine classifier. Sketch/symbol recognition process is based on a set of geometrical and statistical features. Static hand pose recognizer is incorporated to expand the functionalities of our system. Static hand pose recognizer is used in conjunction with sketch classification algorithm to develop a robust and effective system for natural and intuitive interaction. To evaluate the performance of the system user studies were performed on multiple participants. The efficacy of the presented system is demonstrated using multiple interfaces developed for different tasks including computer-aided design modeling.

Accessibility in immersive media is a relevant research topic, still in its infancy. This article explores the appropriateness of two rendering modes (fixed-positioned and always-visible) and two guiding methods (arrows and auto-positioning) for subtitles in 360° video. All considered conditions have been implemented and integrated in an end-to-end platform (from production to consumption) for their validation and evaluation. A pilot study with end users has been conducted with the goals of determining the preferred options by users, the options that result in a higher presence, and of gathering extra valuable feedback from the end users. The obtained results reflect that, for the considered 360° content types, always-visible subtitles were more preferred by end users and received better results in the presence questionnaire than the fixed-positioned subtitles. Regarding guiding methods, participants preferred arrows over auto-positioning because arrows were considered more intuitive and easier to follow and reported better results in the presence questionnaire.

Manufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rule-definition or semantically invalid expression, standard NLP techniques cannot selectively derive precise information for the extraction of the desired formal manufacturing rule. To address the gap, we developed the feedback generation method based on Constraint-based Modeling (CBM) coupled with NLP and domain ontology, designed to support formal manufacturing rule extraction. Specifically, the developed method identifies the necessity of input text validation based on the predefined constraints and provides the relevant feedback to help the user modify the input text, so that the desired rule can be extracted. We proved the feasibility of the method by extending the previously implemented formal rule extraction framework. The effectiveness of the method is demonstrated by enabling the extraction of correct manufacturing rules from all the cases that need input text validation, about 30% of the dataset, after modifying the input text based on the feedback. We expect the feedback generation method will contribute to the adoption of semantics-based technology in the manufacturing field, by facilitating precise knowledge acquisition from manufacturing-related documents in a practical scenario.

Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.