Automated extraction of symmetric microstructure features in serial sectioning images

Serial sectioning methods continue to produce a wealth of image data for quantifying the three-dimensional nature of material microstructures. In this work, we discuss a computational methodology for automated detection and 3D characterization of dendrite cores from images taken from slices of a production turbine blade made of a heat-treated single crystal Ni-based superalloy. The dendrite core locations are detected using an automated segmentation technique that incorporates information over multiple length scales and exploits the four-fold symmetry of the dendrites when viewed down the 〈100〉 growth direction. Additional rules that take advantage of the continuity of the dendrites from slice to slice help to exclude segmentation artifacts and improve dendrite core segmentation. The significance of this technique is that it may be extended to include any symmetric features.     

用于内表面测量的圆结构光视觉图像处理方法

简要介绍了用于内表面测量的圆结构光视觉系统,分析了内表面圆结构光视觉图像特征,并给出了干扰光条的产生原因。在此基础上,提出了干扰光条的剔除方法与有用光条图像中心的子像素级提取方法。介绍了基于阶跃边缘轮廓模型的边缘提取方法,该方法采用非线性优化方法进行模型参数估计,具有速度快、鲁棒性强、精度高的特点。     

Automated generation of NC part programs for turned parts based on 2-D drawing image files

This paper describes a novel method for automated generation of process plans and numerical control (NC) part programs from 2D drawing image files. Four algorithms are introduced in this paper. First algorithm deals with extraction of geometrical data, second algorithm used to recognise turned features from the extracted geometrical data base. Recognised features are then passed to third and fourth algorithms for generation of process plans and NC part programs respectively. Using proposed system, it is possible to achieve fully automation of recognition of turned features, generation of process plans and NC part programs. Thus, it becomes possible to go from scanned image to finished product in a fully automated fashion. These algorithms have been implemented on mini-computer to process image data, and display recognised turned features, process plans and creates output files containing NC part programs. These NC part programs are verified through CNC train simulation package. The results are satisfactory.     

Product complexity and design of inspection strategies for assembly manufacturing processes

In the manufacturing field, the assembly process heavily affects product final quality and cost. Specific studies, concerning the causes of the assembly defects, showed that operator errors account for high percentage of the total defects. Also, models linking the assembly complexity with the operator-induced defect rate were developed. Basing on these models, the present paper proposes a new paradigm for designing inspection strategies in case of short-run productions, for which traditional approaches may not be carried out. Specifically, defect generation models are developed to get a priori predictions of the probability of occurrence of defects, which are useful for designing effective inspection procedures. The proposed methodology is applied to a case study concerning the assembly of mechanical components in the manufacturing of hardness testing machines.     

基于KBE的冲压件自评价工艺方案设计

工艺方案的决策与评估是复杂冲压件工艺设计的关键部分,也是影响工艺设计智能化程度的重要技术问题。根据复杂冲压件工艺方案设计过程和专家经验,借助知识工程多智能决策技术,进行了复杂冲压件可行性工艺方案的设计,并运用价值工程和加权法将量化评价模型有机地融入智能决策系统中,实现了基于KBE的复杂冲压件自评价工艺方案设计。     

Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is maladjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in maladjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.     

Bayesian design of experiments for generalized linear models and dimensional analysis with industrial and scientific application

ABSTRACT

The design of an experiment can always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variables and measured responses. Bayesian methods allow uncertainty in these decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further, a design may be explicitly tailored to the aim of the experiment through a decision-theoretic approach using an appropriate loss function. We review the area of decision-theoretic Bayesian design, with particular emphasis on recent advances in computational methods. For many problems arising in industry and science, experiments result in a discrete response that is well described by a member of the class of generalized linear models. Bayesian design for such nonlinear models is often seen as impractical as the expected loss is analytically intractable and numerical approximations are usually computationally expensive. We describe how Gaussian process emulation, commonly used in computer experiments, can play an important role in facilitating Bayesian design for realistic problems. A main focus is the combination of Gaussian process regression to approximate the expected loss with cyclic descent (coordinate exchange) optimization algorithms to allow optimal designs to be found for previously infeasible problems. We also present the first optimal design results for statistical models formed from dimensional analysis, a methodology widely employed in the engineering and physical sciences to produce parsimonious and interpretable models. Using the famous paper helicopter experiment, we show the potential for the combination of Bayesian design, generalized linear models, and dimensional analysis to produce small but informative experiments.     

A survey of semiconductor supply chain models part I: semiconductor supply chains,strategic network design,and supply chain simulation

Supply chain management issues have become increasingly important to the semiconductor industry over the last two decades due to the global distribution of facilities and increasing numbers of firms specialising in particular stages. This series of three papers reviews the literature on modelling and analysis of the larger semiconductor supply chain. After describing the structure of semiconductor supply chains to provide context for the research efforts, we propose a classification scheme for the relevant literature. The remainder of this paper (Part I) then focuses on Strategic Network Design models for this industry, supply chain coordination through contracting and semiconductor supply chain simulation. Part II discusses Demand Planning, Inventory Management and Capacity Planning, while Part III addresses Master Planning, Production Planning and Demand Fulfilment.