Predictive defect detection for prototype additive manufacturing based on multi-layer susceptibility discrimination

This paper presents a predictive defect detection method for prototype additive manufacturing(AM)based on multilayer susceptibility discrimination(MSD).Most current methods are significantly limited by merely captured images,disregarding the differences between layer-by-layer manufacturing approaches,without combining transcendental knowledge.The visible parts,originating from the prototype of conceptual design,are determined based on spherical flipping and convex hull theory,on the basis of which theoretical template image(TTI)is rendered according to photorealistic technology.In addition,to jointly consider the differences in AM processes,the finite element method(FEM)of transient thermal-structure coupled analysis was conducted to probe susceptible regions where defects appeared with a higher possibility.Driven by prior knowledge acquired from the FEM analysis,the MSD with an adaptive threshold,which discriminated the sensitivity and susceptibility of each layer,was implemented to determine defects.The anomalous regions were detected and refined by superimposing multiple-layer anomalous regions and comparing the structural features extracted using the Chan-Vese(CV)model.A physical experiment was performed via digital light processing(DLP)with photosensitive resin of a non-faceted scaled V-shaped engine block prototype with cylindrical holes using a non-contact profilometer.This MSD method is practical for detecting defects and is valuable for a deeper exploration of barely visible impact damage(BVID),thereby reducing the defect of prototypical mechanical parts in engineering machinery or process equipment via intellectualized machinevision.

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

Collisionless tool orientation smoothing above blade stream surface using NURBS envelope

In five-axis machining,tool orientation above a blade stream surface may lead to tool collision and a decrease in workpiece rigidity.Hence,collisionless tool orientation smoothing(TOS)becomes an important issue.On the basis of a constant scallop height tool path,the triangular facets in the faces,vertices format are constructed from cutter contact(CC)using the Voronoi incremental algorithm.The cutter location(CL)points candidate set is represented by an oblique elliptic cone whose vertex lies at CC using NURBS envelope.Whether the CL point is above its CC is judged by the dot product between the normal vector and the point on triangulation nearest to the CL point.The curvatures at CC are obtained by fitting a moving least square(MLS) quadratic patch to the local neighborhood of a vertex and calculating eigenvectors and eigenvalues of the Hessian matrix.Triangular surface elastic energy is employed as the weight in selection from the NURBS envelope.The collision is judged by NURBS surface intersection.TOS can then be expressed by selecting a CL point for each CC point and converted into a numerical control(NC)code automatically according to the postprocessor type of the machine center.The proposed method is verified by finishing of a cryogenic turboexpander impeller of air separation equipment.

A knowledge matching approach based on multiclassification radial basis function neural network for knowledge push system

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations,namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.

考虑结构柔性的多激励机构关联性能强化设计方法（英文）

目的:揭示多场耦合环境下的多自由度系统中柔性结构对机构性能的影响规律,以低压断路器多激励操动机构为例,研究机-电-热-磁耦合的多模态分断性能强化设计方法,提高短路、过载、欠压、缺相和漏电时的瞬时、短延时和长延时分断性能。创新点:1.将多自由度刚柔耦合系统的物理坐标转换为模态坐标,实现模型降阶和关联性能解耦;2.运用类比法获得零部件对关联性能的影响度,通过实验设计将难测属性转化为关联易测量,实现机构的关联性能强化。方法:1.将多自由度的含柔体动力学系统转化为自由度乘模态阶的方阵,通过Craig-Bampton逐层降阶模态矩阵,并由迭代步反馈激活柔体的高阶模态;2.通过类比求解霍尔姆力、洛伦兹力、电动斥力、电磁力和悬臂双金属片激励力,获得多激励系统机构关节的性能关联程度;3.通过低压断路器电气实验和温升实验,验证零件结构柔性对机构的性能影响规律,实现多激励机构多模态分断性能强化设计。结论:1.提出的考虑结构柔性的多激励机构关联性能强化设计方法有助于提高多自由度动力学系统的瞬态性能;2.结构柔性降低分断性能,使高频分断中的峰值段性能退化;3.基于实验设计的电气实验和温升实验,验证了多激励机构关联性能强化方法的有效性。

板翅换热器入口位置不同翅片排列方式对流体流动与换热的影响（英文）

目的:研究板翅换热器入口位置(入口段、导流段以及换热段)在不同翅片排列方式下,换热器流道内流体流动以及温度变化情况,期望得到能够实现最优传热效果的板翅换热器入口位置合理的翅片排列方式。方法:1.构建7种不同入口翅片排列(入口段:导流段:换热段分别为5:8:11,5:8:6,5:8:21,5:4:11,5:16:11,9:8:11和3:8:11),研究不同翅片排列下换热器入口位置流场以及温度场变化。2.分析板翅换热器入口位置6种不同网格划分结果(网格数分别为11962,39344,2120,207998,90738和54496)对计算误差的影响,验证网格收敛性。结论:通过分析板翅换热器入口位置不同翅片排列方式下换热器入口位置流场与温度场变化可以看出,相比入口段与换热段,导流段翅片排列对换热器温度场影响较大。强化导流段翅片排列可以使换热器获得更好的传热效果。

A knowledge push technology based on applicable probability matching and multidimensional context driving

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push＇, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

Material selection combined with optimal structural design for mechanical parts

To reduce the environmental impact of mechanical parts, an approach integrating structural design and material selection was studied. Adding the discrete variable of material, a hybrid optimization model was built with the aim of minimizing environmental impact and based on an ordinary structure optimization model. An optional material set was built by combining measures of qualitative and quantitative screening, while the lifecycle environmental impact of the materials was quantified using the method of Eco-indicator 99. Two groups of structurally optimal solutions were calculated with ideal and negative-ideal materials selected respectively, and then the hybrid model was simplified by comparing the solutions. A material environmental performance index was calculated using an analytic method. By comparing this index for every material in the optional material set, the optimal material can be found and the structural solutions calculated. This method was applied to a dowel bar design process as a case study. The results show that the environmental impact of each material has a significant effect on the optimal structural solution, and it is necessary to study the integration of structural design and material selection.

Estimates for Eigenvalues of Stochastic Matrices

It is well-known that the eigenvalues of stochastic matrices lie in the unit circle and at least one of them has the value one. Let {1, r 2 , ··· , r N } be the eigenvalues of stochastic matrix X of size N × N . We will present in this paper a simple necessary and sufficient condition for X such that ｜r j ｜ 〈 1, j = 2, ··· , N . Moreover, such condition can be very quickly examined by using some search algorithms from graph theory.

Feature-based initial population generation for the optimization of job shop problems

A suitable initial value of a good(close to the optimal value) scheduling algorithm may greatly speed up the convergence rate.However,the initial population of current scheduling algorithms is randomly determined.Similar scheduling instances in the production process are not reused rationally.For this reason,we propose a method to generate the initial population of job shop problems.The scheduling model includes static and dynamic knowledge to generate the initial population of the genetic algorithm.The knowledge reflects scheduling constraints and priority rules.A scheduling strategy is implemented by matching and combining the two categories of scheduling knowledge,while the experience of dispatchers is externalized to semantic features.Feature similarity based knowledge matching is utilized to acquire the constraints that are in turn used to optimize the scheduling process.Results show that the proposed approach is feasible and effective for the job shop optimization problem.