Design Theory and Method of Complex Products:A Review

Design is a high-level and complex thinking activity of human beings,using existing knowledge and technology to solve problems and create new things.With the rise and development of intelligent manufacturing,design has increasingly reflected its importance in the product life cycle.Firstly,the concept and connotation of complex product design is expounded systematically,and the different types of design are discussed.The four schools of design theory are introduced,including universal design,axiomatic design,TRIZ and general design.Then the research status of complex product design is analyzed,such as innovative design,digital design,modular design,reliability optimization design,etc.Finally,three key scientific issues worthy of research in the future are indicated,and five research trends of“newer,better,smarter,faster,and greener”are summarized,aiming to provide references for the equipment design and manufacturing industry.

Decomposition Method of Complex Optimization Model Based on Global Sensitivity Analysis

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.

Force Feedback Coupling with Dynamics for Physical Simulation of Product Assembly and Operation Performance

Most existing force feedback methods are still difficult to meet the requirements of real-time force calculation in virtual assembly and operation with complex objects. In addition, there is often an assumption that the controlled objects are completely flee and the target object is only completely fixed or flee, thus, the dynamics of the kinematic chain where the controlled objects are located are neglected during the physical simulation of the product manipulation with force feedback interaction. This paper proposes a physical simulation method of product assembly and operation manipulation based on statistically learned contact force prediction model and the coupling of force feedback and dynamics. In the proposed method, based on hidden Markov model （HMM） and local weighting learning （LWL）, contact force prediction model is constructed, which can estimate the contact force in real time during interaction. Based on computational load balance model, the computing resources are dynamically assigned and the dynamics integral step is optimized. In addition, smoothing process is performed to the force feedback on the synchronization points. Consequently, we can solve the coupling and synchronization problems of high-frequency feedback force servo. low-frequency dynamics solver servo and scene rendering servo, and realize highly stable and accurate force feedback in the physical simulation of product assembly and operation manipulation. This research proposes a physical simulation method of product assembly and operation manipulation.

A sketch-based semantic retrieval approach for 3D CAD models

During the new product development process, reusing the existing CAD models could avoid designing from scratch and decrease human cost. With the advent of big data,how to rapidly and efficiently find out suitable 3D CAD models for design reuse is taken more attention. Currently the sketch-based retrieval approach makes search more convenient, but its accuracy is not high enough; on the other hand, the semantic-based retrieval approach fully utilizes high level semantic information, and makes search much closer to engineers＇ intent.However, effectively extracting and representing semantic information from data sets is difficult.Aiming at these problems, we proposed a sketch-based semantic retrieval approach for reusing3 D CAD models. Firstly a fine granularity semantic descriptor is designed for representing 3D CAD models; Secondly, several heuristic rules are adopted to recognize 3D features from 2D sketch, and the correspondences between 3D feature and 2D loops are built; Finally, semantic and shape similarity measurements are combined together to match the input sketch to 3D CAD models. Hence the retrieval accuracy is improved. A sketch-based prototype system is developed.Experimental results validate the feasibility and effectiveness of our proposed approach.

Generating Quantitative Product Profile Using Char-Word CNNs

The online customer reviews provide important information for product improvement and redesign.However,many reviews are redundant with only several short sentences,which may even conflict with each other on the same aspect of a product.Thus it is usually a very challenging task to extract useful design information from the reviews and provide a clear description on the product’s various aspects amongst its competitors.In order to resolve this issue,we propose an approach to build hierarchical product profiles to describe a product’s kernel design aspects quantitatively.It is achieved via three main strategies:a double propagation strategy to achieve the associated features and customers’descriptions;a deep text processing network to build the aspect hierarchy;an aspect ranking approach to quantify each kernel design aspect.Experimental results validate the effectiveness of the proposed approach on online reviews.

Optimization of Uncertain Structures with Interval Parameters Considering Objective and Feasibility Robustness

For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.

Watermarking on 3D mesh based on spherical wavelet transform

In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.

Mesh Smoothing Method Based on Local Wave Analysis

As the mesh models usually contain noise data,it is necessary to eliminate the noises and smooth the mesh.But existed methods always lose geometric features during the smoothing process.Hence,the noise is considered as a kind of random signal with high frequency,and then the mesh model smoothing is operated with signal processing theory.Local wave analysis is used to deal with geometric signal,and then a novel mesh smoothing method based on the local wave is proposed.The proposed method includes following steps：Firstly,analyze the principle of local wave decomposition for 1D signal,and expand it to 2D signal and 3D spherical surface signal processing;Secondly,map the mesh to the spherical surface with parameterization,resample the spherical mesh and decompose the spherical signals by local wave analysis;Thirdly,propose the coordinate smoothing and radical radius smoothing methods,the former filters the mesh points＇ coordinates by local wave,and the latter filters the radical radius from their geometric center to mesh points by local wave;Finally,remove the high-frequency component of spherical signal,and obtain the smooth mesh model with inversely mapping from the spherical signal.Several mesh models with Gaussian noise are processed by local wave based method and other compared methods.The results show that local wave based method can obtain better smoothing performance,and reserve more original geometric features at the same time.

Quantitative information measurement and application for machine component classification codes

Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on Shannon’s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.

Multi-scale Simulation Method with Coupled Finite/Discrete Element Model and Its Application

The existing research on continuous structure is usually analyzed with finite element method (FEM) and granular medium with discrete element method (DEM), but there are few researches on the coupling interaction between continuous structure and discrete medium. To the issue of this coupling interaction, a multi-scale simulation method with coupled finite/discrete element model is put forward, in their respective domains of discrete and finite elements, the nodes follow force law and motion law of their own method, and on the their interaction interface, the touch type between discrete and finite elements is distinguished as two types: full touch and partial touch, the interaction force between them is calculated with linear elastic model. For full touch, the contact force is proportional to the overlap distance between discrete element and finite element patch. For partial touch, first the finite element patch is extended on all sides indefinitely to be a complete plane, the full contact force can be obtained with the touch type between discrete element and plane being viewed as full touch, then the full overlap area between them and the actual overlap area between discrete element and finite element patch are computed, the actual contact force is obtained by scaling the full contact force with a factor which is determined by the ratio of the actual overlap area to the full overlap area. The contact force is equivalent to the finite element nodes and the force and displacement on the nodes can be computed, so the ideal simulation results can be got. This method has been used to simulate the cutter disk of the earth pressure balance shield machine (EPBSM) made in North Heavy Industry (NHI) with its excavation diameter of 6.28 m cutting and digging the sandy clay layer. The simulation results show that as the gradual increase of excavating depth of the cutter head, the maximum stress occurs at the roots of cutters on the cutter head, while for the soil, the largest stress is distributed at the region which directly contacted with the cutters. The proposed research can provide good solutions for correct design and installation of cutters, and it is necessary to design mounting bracket to fix cutters on cutter head.

Constrained branch-and-bound algorithm for image registration

In this paper, the authors propose a refined Branch-and-Bound algorithm for affine-transformation based image registration. Given two feature point-sets in two images respectively, the authors first extract a sequence of high-probability matched point-pairs by considering well-defined features. Each resultant point-pair can be regarded as a constraint in the search space of Branch-and-Bound algorithm guiding the search process. The authors carry out Branch-and-Bound search with the constraint of a pair-point selected by using Monte Carlo sampling according to the match measures of point-pairs. If such one cannot lead to correct result, additional candidate is chosen to start another search. High-probability matched point-pairs usually results in fewer loops and the search process is accelerated greatly. Experimental results verify the high efficiency and robustness of the author’s approach.

Multi-Directional Reconstruction Algorithm for Panoramic Camera

A panorama can reflect the surrounding scenery because it is an image with a wide angle of view.It can be applied in virtual reality,smart homes and other fields as well.A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera,as the distortion inevitably exists in the captured panorama.First,parameters of a panoramic image are calculated.Then,a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels.Six directions of the mapping model are built,which include up,down,left,right,front and back,according to the correspondence between cylinder and spherical coordinates.Finally,multi-directional image reconstruction can be realized.Various experiments are performed in panoramas(1024×1024)with 30 different shooting scenes.Results show that the azimuth image can be reconstructed quickly and accurately.The fuzzy edge can be alleviated effectively.The rate of pixel utilization can reach 84%,and it is 33%higher than the direct mapping algorithm.Large scale distortion is also further studied.

NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.

Computational Approaches for Traditional Chinese Painting: From the “Six Principles of Painting” Perspective

Traditional Chinese painting(TCP)is an invaluable cultural heritage resource and a unique visual art style.In recent years,there has been a growing emphasis on the digitalization of TCP for cultural preservation and revitalization.The resulting digital copies have enabled the advancement of computational methods for a structured and systematic understanding of TCP.To explore this topic,we conduct an in-depth analysis of 94 pieces of literature.We examine the current use of computer technologies on TCP from three perspectives,based on numerous conversations with specialists.First,in light of the“Six Principles of Painting”theory,we categorize the articles according to their research focus on artistic elements.Second,we create a four-stage framework to illustrate the purposes of TCP applications.Third,we summarize the popular computational techniques applied to TCP.This work also provides insights into potential applications and prospects,with professional opinion.

The Convergence of Least-Squares Progressive Iterative Approximation for Singular Least-Squares Fitting System

Data fitting is an extensively employed modeling tool in geometric design. With the advent of the big data era, the data sets to be fitted are made larger and larger, leading to more and more least-squares fitting systems with singular coefficient matrices. LSPIA (least-squares progressive iterative approximation) is an efficient iterative method for the least-squares fitting. However, the convergence of LSPIA for the singular least-squares fitting systems remains as an open problem. In this paper, the authors showed that LSPIA for the singular least-squares fitting systems is convergent. Moreover, in a special case, LSPIA converges to the Moore-Penrose (M-P) pseudo-inverse solution to the least- squares fitting result of the data set. This property makes LSPIA, an iterative method with clear geometric meanings, robust in geometric modeling applications. In addition, the authors discussed some implementation detail of LSPIA, and presented an example to validate the convergence of LSPIA for the singular least-squares fitting systems.

Geometric meanings of the parameters on rational conic segments

Using algebraic and geometric methods,functional relationships between a point on a conic segment and its corresponding parameter are derived when the conic segment is presented by a rational quadratic or cubic Bézier curve.That is,the inverse mappings of the mappings represented by the expressions of rational conic segments are given.These formulae relate some triangular areas or some angles,determined by the selected point on the curve and the control points of the curve,as well as by the weights of the rational Bézier curve.Also,the relationship can be expressed by the corresponding parametric angles of the selected point and two endpoints on the conic segment,as well as by the weights of the rational Bézier curve.These results are greatly useful for optimal parametrization,reparametrization,etc.,of rational Bézier curves and surfaces.

Visual analytics of taxi trajectory data via topical sub-trajectories

GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.

An Online Visualization System for Streaming Log Data of Computing Clusters

Monitoring a computing cluster requires collecting and understanding log data generated at the core, computer, and cluster levels at run time. Visualizing the log data of a computing cluster is a challenging problem due to the complexity of the underlying dataset： it is streaming, hierarchical, heterogeneous, and multi-sourced. This paper presents an integrated visualization system that employs a two-stage streaming process mode. Prior to the visual display of the multi-sourced information, the data generated from the clusters is gathered, cleaned, and modeled within a data processor. The visualization supported by a visual computing processor consists of a set of multivariate and time variant visualization techniques, including time sequence chart, treemap, and parallel coordinates. Novel techniques to illustrate the time tendency and abnormal status are also introduced. We demonstrate the effectiveness and scalability of the proposed system framework on a commodity cloud-computing platform.

A computational model of topological and geometric recovery for visual curve completion

Visual curve completion is a fundamental problem in understanding the principles of the human visual system. This problem is usually divided into two problems: a grouping problem and a shape problem.On one hand, though perception of the visually completed curve is clearly a global task(for example,a human perceives the Kanizsa triangle only when seeing all three black objects), conventional methods for solving the grouping problem are generally based on local Gestalt laws. On the other hand, the shape of the visually completed curve is usually recovered by minimizing shape energy in existing methods.However, not only do these methods lack mechanisms to adjust the shape of the recovered visual curve using perceptual, psychophysical, and neurophysiological knowledge, but it is also difficult to calculate an explicit representation of the visually completed curve. In this paper, we present a systematic computational model for generating a visually completed curve. Firstly, based on recent studies of perception, psychophysics, and neurophysiology, we formulate a grouping procedure based on the human visual system by seeking a minimum Hamiltonian cycle in a graph, solving the grouping problem in a global manner. Secondly, we employ a B′ezier curve-based model to represent the visually completed curve. Not only is an explicit representation deduced, but we also present a means to integrate knowledge from related areas, such as perception, psychophysics, and neurophysiology, and so on. The proposed computational model has been validated using many modal and amodal completion examples, and desirable results were obtained.

Window Detection in Facades Using Heatmap Fusion

Window detection is a key component in many graphics and vision applications related to 3D city modeling and scene visualization.We present a novel approach for learning to recognize windows in a colored facade image.Rather than predicting bounding boxes or performing facade segmentation,our system locates keypoints of windows,and learns keypoint relationships to group them together into windows.A further module provides extra recognizable information at the window center.Locations and relationships of keypoints are encoded in different types of heatmaps,which are learned in an end-to-end network.We have also constructed a facade dataset with 3418 annotated images to facilitate research in this field.It has richly varying facade structures,occlusion,lighting conditions,and angle of view.On our dataset,our method achieves precision of 91.4%and recall of 91.0%under 50%IoU(intersection over union).We also make a quantitative comparison with state-of-the-art methods to verify the utility of our proposed method.Applications based on our window detector are also demonstrated,such as window blending.