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This paper aims to study the appearance of drills from one brand by using currently available design tools. It aims to find and discuss the relationship between appearance innovation and maintaining key design features.

The innovation process is studied on drills of a Czech power tool maker and a previously created concept of a new drill. First, the authors explore the similarities between the designed concept and previous models of the brand by calculating the degree of similarity of given shape features. Second, they capture the drills simple shape grammar and strive to generate a sketch of the concept.

Results show the use of several similar shape features from previous models in the innovated design. Shape grammar can create a principally similar concept, but some innovations cannot be achieved this way. A description of appearance innovation within brand identity in terms of shape grammar is given.

The research is limited mainly to a small group of previous products that can be analyzed. It is done only for one particular brand identity. When used with the shape grammars, design generation is limited.

Better understanding of the innovative process aids designers in working with designs for brand identity and may serve to shape grammar enhancement.

The paper describes what happens during the innovation of product appearance and implicates enhancement and meaning of design analysis done by shape grammars and exploring similarities.

This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

This study focused on how to quantify the similarities of body shape based on the front and side images, and a shape comprehensive index (I_SC) of female upper body shape based on 2D images was proposed.

In total, 190 young women were shot for front and side images, and 18 shape parameters were automatically extracted, including seven angles and 11 ratio parameters. The coefficient of variation method was used to assign different weights for related parameters, and the I_SC was calculated to describe the body shape of each subject. Five cross-sectional curves of the upper body (e.g. shoulder, chest, waist, abdomen and hip) were selected for exploring the range of shape similarity.

According to the value of I_SC, if the difference among the subjects is within the range of ±0.02, their body shapes can be regarded as similar, and the subject with the minimum distance is considered as the most similar. Error results show that the error range of the angle parameter is from 0.2° to 3.6° and the ratio range is from 0.001 to 0.119. Moreover, the t-test value among the parameters of the similar body is above 0.05, indicating that there is no significant difference for the upper body shape of the similar groups.

This method can quantify body shapes with the upper body characteristics of young women instead of subjective judgment. The study can be extended to other parts of the body and can also provide a new thought for shape similarity retrieval based on 2D images.

The purpose of this study is the use of 3D printing technology to perform maintenance on damaged parts on site. To maintain damaged parts, the user needs experience in the parts design and 3D printing technology. To help users who have little or no experience on 3D printing, a part library-based information retrieval and inspection framework was proposed to support the process of manufacturing replaceable parts using a 3D printer.

To establish the framework, 3D printing-based maintenance procedure was first defined, comprising retrieval, manufacturing and inspection steps, while identifying the technical components required to perform the procedure. Once the technical components are identified, part library-based information retrieval and inspection framework was defined based on the technical components and the relationships between the components. For validation of the concept of the framework, prototype system is developed according to the proposed framework.

The feasibility of the proposed framework is proved through maintenance experiments on gaskets and O-rings.

The main contribution of this study is the proposal of the framework, which aims to support the maintenance of damaged parts for the user who has little or no experience in part design or does not know how to operate a 3D printer.

The purpose of this paper is to develop a similarity evaluation method between virtual and actual clothing.

The paper analyzes the subjective and objective evaluation results of virtual and actual clothing.

In this paper, significant factors affecting the evaluation of similarity between actual and virtual clothing have been found.

Evaluation experiment was performed only for a skirt. However, the method can be easily applied for other types of garments.

The evaluation of similarity between actual and virtual garment will be facilitated.

The garment design process can be facilitated by simulating garments in virtual space.

There has not been any quantitative evaluation method for the similarity of virtual and actual garment.

This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files.

In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching.

This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm.

The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

The aim of this paper is to advance the understanding of the droplet deposition process to better predict and control the manufacturing results for ink-jet deposition.

As material interface has both geometric and physical significance to manufacturing, the approach the authors take is to study the interface evolution during the material joining process in ink-jet deposition using a novel shape metric and a previously developed powerful simulation tool. This tool is an experimentally validated numerical solver based on the combination of the lattice Boltzmann method and the phase-field model that enabled efficient simulation of multiple-droplet interactions in three dimensions.

The underlying physics of two-droplet interaction is carefully examined, which provides deep insights into the effects of the printing conditions on the interface evolution of multiple-droplet interaction. By studying line printing, it is found that increasing impact velocity or decreasing fluid viscosity can reduce manufacturing time. For array printing, the authors have found the issue of air bubble entrapment that can lead to voids in the manufactured parts.

The array of droplets impinges simultaneously, in contrast to most ink-jet printers. Sequential impingement of lines of droplet needs to be studied. Also, impingement on non-planar surfaces has not been investigated yet, but is important for additive manufacturing. Finally, it is recognized that the droplet hardening mechanisms need to be incorporated in the simulation tool to predict and control the final shape and size of the arbitrary features and manufacturing time for ink-jet deposition.

The research findings in this paper imply opportunities for optimization of printing conditions and print head design. Furthermore, if precise droplet control can be achieved, it may be possible to eliminate the need for leveling roller in the current commercial printers to save machine and manufacturing cost.

This work represents one of the first attempts for a systematic study of the interface dynamics of multiple-droplet interaction in ink-jet deposition enabled by the novel shape metric proposed in the paper and a previously developed numerical solver. The findings in this paper advanced the understanding of the droplet deposition process. The physics-based approach of analyzing the simulation results of the interface dynamics provides deep insights into how to predict and control the manufacturing relevant outcomes, and optimization of the deposition parameters is made possible under the same framework.

This paper aims to clarify the relationship between mechanical behaviors and the underlying geometry of periodic cellular structures. Particularly, the answer to the following research question is investigated: Can seemingly different geometries of the repeating unit cells of periodic cellular structure result in similar functional behaviors? The study aims to cluster the geometry-functional behavior relationship into different categories.

Specifically, the effects of the geometry on the compressive deformation (mechanical behavior) responses of multiple standardized cubic periodic cellular structures (CPCS) at macro scales are investigated through both physical tests and finite element simulations of three-dimensional (3D) printed samples. Additionally, these multiple CPCS can be further nested into the shell of 3D models of various mechanical domain parts to demonstrate the influence of their geometries in practical applications.

The paper provides insights into how different CPCS (geometrically different unit cells) influence their compressive deformation behaviors. It suggests a standardized strategy for comparing mechanical behaviors of different CPCS.

This paper is the first work in the research domain to investigate if seemingly different geometries of the underlying unit cell can result in similar mechanical behaviors. It also fulfills the need to infill and lattify real functional parts with geometrically complex unit cells. Existing work mainly focused on simple shapes such as basic trusses or cubes with spherical holes.

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.

The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.

The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.