A probabilistic solid-porous model to determine the shear strength of unsaturated soils

A probabilistic solid-porous model has been developed to determine the shear strength of unsaturated soils. The probabilistic model was built by analyzing the probability of a certain pore or group of pores of a network to fill or remain filled with water during a wetting or drying process, respectively. This model is used to determine the equivalent stress which represents the stress supported by the solid skeleton of an unsaturated soil and is related to the strength of the material. The probabilistic model is an alternative to the use of computational models and shows some important advantages. The theoretical results of the model are compared with a series of triaxial tests performed at constant suction and constant volume. These comparisons demonstrate that this model is adequate to establish the strength of unsaturated materials.     

A comprehensive reliability allocation method for design of CNC lathes

In the design and development of computerized numerical control lathes, an effective reliability allocation method is needed to allocate system level reliability requirements into subsystem and component levels. During the allocation process, many factors have to be considered. Some of these factors can be measured quantitatively while others have to be assessed qualitatively. In this paper, we consider seven criteria for conducting reliability allocation. A comprehensive failure rate allocation method is proposed for conducting the task of reliability allocation. Example data from field studies are used to illustrate the proposed method.     

Mixture probabilistic PCR model for soft sensing of multimode processes

Principal component regression (PCR) has been widely used for soft sensor modeling and quality prediction in last several decades, which is still very popular for both academy researches and industry applications. However, most PCR models are determined by the projection method, which may lack probabilistic interpretation for the process data. In fact, due to the inevitable process noise, most process data are inherently random variables. Several probabilistic PCA methods have already been proposed in the past years. Compared to the deterministic modeling method, the probabilistic model is more appropriate to characterize the behavior of the random variables in the process. This paper first presents a probabilistic derivation of the PCR model (PPCR) and then extends it to the mixture form (MPPCR). For quality prediction of processes with multiple operation modes, a mixture probabilistic soft sensor is developed based on the MPPCR model. Simultaneously, the information of the operation mode can also be located by the proposed soft sensor. To evaluate the performance of the MPPCR model, a numerical example and a benchmark simulation case study of the Tennessee Eastman process are provided. Different methods have been compared with the proposed model, including the global, local, and multi-local PCR models. As a result, the proposed MPPCR model performs the best among these methods.     

An enhanced probabilistic model for cleavage fracture assessment accounting for local constraint effects

Objective of the present study is the development of an enhanced model for the probabilistic cleavage fracture assessment of ferritic materials considering the conditions for both, nucleation and propagation of micro defects. In a first step, the local load and deformation history at the cleavage initiation spot is analysed numerically for a variety of fracture mechanics specimens. The experimental data base includes experiments on standard deep and shallow crack specimens with different geometries as well as novel small scale cruciform bending specimens. These specimens enable the application of an additional stress component along the crack front. Based on the results, a two-criteria concept for cleavage initiation is proposed, assuming that the propagation of existing micro defects is controlled by the maximum principal stress whereas the nucleation of potentially critical micro defects is governed by a combination of the local plastic strain and the local stress triaxiality at the respective material point. Based on these assumption, a probabilistic cleavage fracture model is formulated and validated against the experimental data base.     

Fragility curve probabilistic model applied to durability and long term mechanical damage of masonry

In view of preserving the historical heritage, the deterioration processes of masonry materials and the long term damage of multiple leaf historic buildings is studied from an experimental and a numerical point of view. Durability tests and pseudo-creep tests have been carried out and interpreted through probabilistic models for the service life prediction. The precocious recognition of structural critical states will allow to design repair and strengthening interventions and to prevent total or partial failure of the constructions.     

Application of classification trees in the sensitivity analysis of probabilistic model results

The complexity of some integrated-system models necessitates using a probabilistic approach to quantify uncertainty in model projections. In this work, we demonstrate how classification trees can be used to perform sensitivity analyses on probabilistic results. The classification tree technique is applied to results from the probabilistic total system performance assessment model used in the Yucca Mountain project. The technique proves effective in delineating the variables that most influence low and high outcomes.     

A probabilistic model for the pearlite-induced cleavage of a plain carbon structural steel

The cleavage fracture of a carbon structural steel is shown to deviate from what is typical of a ferritic matrix. This occurs at temperatures ranging from −150 to 20 °C and affects the scatter of fracture toughness, and fractographic features. To explain the observed discrepancies a probabilistic model is developed by linking physical mechanisms of cleavage with continuum mechanics analysis applied to fracture. The model assumes that cleavage is nucleated and triggered in pearlite within the blunting zone of the crack tip by the mechanism of Miller and Smith. Once the model is calibrated for the steel, its predictions are shown to agree with the experimental results.     

Unified representation of fixtures: clamping,locating and supporting elements in CNC manufacture

A CNC machining operation is the outcome of the application of the integrated capabilities of various resources within the CNC machining centre. Part fixtures, clamping and other location mechanisms are essential subsets of CNC machining resources. Today, various fixturing techniques and attachments available in the market allow manufacturers to enhance their production capability without buying expensive machine tools. This technology-rich fixturing domain is detached while representing and exchanging machine tool resource information for making manufacturing decisions. The research work in this article utilises the STEP-NC compliant unified manufacturing resource model (UMRM) for representing fixtures in conjunction with the parent CNC machining centre. Thus, UMRM is enhanced in this context to represent various fixtures such as universal vises, chucks, pallets and auxiliary rotary tables among others. The major contribution of this article is the application of the extension of the UMRM approach for representing fixturing domain, which allows generic modelling of fixtures and loading devices in addition to machine workpiece and process modelling. This would enable the stage of automated process planning and manufacturing. The universal approach in representing resource information allows the data to be utilised for making a wide variety of manufacturing decisions.     

A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

A combined probabilistic physics-of-failure-based model for pitting and corrosion-fatigue degradation mechanisms is proposed to estimate the reliability of structures and to perform prognosis and health management. A mechanistic superposition model for corrosion-fatigue mechanism was used as a benchmark model to propose the simple model. The proposed model describes the degradation of the structures as a function of physical and critical environmental stresses, such as amplitude and frequency of mechanical loads (for example caused by the internal piping pressure) and the concentration of corrosive chemical agents. The parameters of the proposed model are represented by the probability density functions and estimated through a Bayesian approach based on the data taken from the experiments performed as part of this research. For demonstrating applications, the proposed model provides prognostic information about the reliability of aging of structures and is helpful in developing inspection and replacement strategies.     

浅谈数控加工中心的安装与验收

介绍了数控加工中心的安装调试验收方法,分析了安装验收中应注意的问题．     

数控刀具材料的研究进展

数控刀具是数控加工技术中的重要基础装备之一,在保证制造技术的有效实施方面有着十分重要的作用。本文主要介绍数控机床刀具的分类、数控刀具材料的现状和发展趋势。     

A probabilistic method for multiaxial HCF based on highly loaded regions below the threshold depth

For High Cycle Fatigue (HCF) assessment of structural components, a variety of evaluation methods that take important factors into account are available. The application of probabilistic evaluation methods is not always straightforward. Investigations show that the methods have limited predictive capability and most often poor transferability of fatigue test results for more complex geometries. Here, a new probabilistic HCF model based on the concept of highly loaded region is proposed. A depth parameter is taking on an important role in the proposed model. Different local stress criteria in combination with the new model and two other evaluation methods are studied. The purpose is to determine the combination of criteria and evaluation method that gives the best transferability. The results show that the new model has superior predictive capability than other models regardless the choice of fatigue stress criteria.     

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

The analysis of event sequence data that contains system failures is becoming increasingly important in the design of service and maintenance policies. This paper presents a systematic methodology to construct a statistical prediction model for failure event based on event sequence data. First, frequent failure signatures, defined as a group of events/errors that repeatedly occur together, are identified automatically from the event sequence by use of an efficient algorithm. Then, the Cox proportional hazard model, that is extensively used in biomedical survival analysis, is used to provide a statistically rigorous prediction of system failures based on the time-to-failure data extracted from the event sequences. The identified failure signatures are used to select significant covariates for the Cox model, i.e., only the events and/or event combinations in the signatures are treated as explanatory variables in the Cox model fitting. By combining the failure signature and Cox model approaches the proposed method can effectively handle the situation of a long event sequence and a large number of event types in the sequence. Its effectiveness is illustrated by a numerical study and analysis of real-world data. The proposed method can help proactively diagnose machine faults with a sufficient lead time before actual system failures to allow preventive maintenance to be scheduled thereby reducing the downtime costs.     

Cam Profile Grinding CNC System Based on Open Architecture

This paper studies some key technologies of CNC system for cam grinding. The mathematical motion model for cam grinding is established according to the harmony of re-ciprocating motion of the grinding wheel and the rotating motion of the workpiece. The method of using the linear servomotor to accomplish the grinding wheel tracking is devel-oped and the dynamic model of the system is analyzed. Then the cross-coupled biaxial error compensation model is proposed. Finally,the hardware and software of the control system are designed based on open architecture. Some algorithms, such as spline fitting and inter-polation for cam contour, velocity control and feed control,are presented to improve grinding precision.     

Cam Profile Grinding CNC System Based on Open Architecture

This paper studies some key technologies of CNC system for cam grinding. Themathematical motion model for cam grinding is established according to the harmony of re-ciprocating motion of the grinding wheel and the rotating motion of the woorkpiece. Themethod of using the linear servomotor to accomplish the grinding wheel tracking is devel-oped and the dynamic model of the system is analyzed. Then the cross-coupled biaxial errorcompensation model is proposed. Finally,the hardware and software of the control systemare designed based on open architecture. Some algorithms, such as spline fitting and inter-polation for cam contour, velocity control and feed control, are presented to improvegrinding precision.     

Reliability Analysis of the CNC System Based on Field Failure Data in Operating Environments

Reliability is a measure of how well a product will perform under a certain set of conditions for a specified amount of time especially in the field environments. In this paper, a reliability study of a computer numerical control (CNC) system is described. For this analysis, field failure data from a shop manufacturing factory collected over the course of a year on approximately 20 CNC machine tools during their operating period were analyzed. Based on the field failure data, the two‐parameter exponential distribution was found to be applicable to describe the time between failures of the CNC system from among many distributions including Weibull, gamma, two‐parameter exponential, normal, and logistic using the chi‐squared test. In this paper, we discuss the reliability estimation of the CNC system based on the collected field failure data from a manufacturing factory using the maximum likelihood estimate (MLE) and uniform minimum variance estimate (UMVUE) methods. We also discuss the confidence intervals of the mean residual lifetime and reliability function. The result shows that the UMVUE method can provide much better and more accurate results in estimating the reliability of the CNC system than the MLE. This finding, on the one hand, seems to be obvious because the UMVUE is not only an unbiased estimator but also sufficient statistic with the smallest variance; on the other hand, it is not straightforward to obtain the UMVUE of any complex function, which is the reliability function in this case. This is a very important finding and is very encouraging because it indicates that the reliability analysis of the CNC system based on the UMVUE can be more than compensated by the ability of the complexity of parameter estimation method to better evaluate and predict the reliability of the CNC system. Hence, we believe that it is worth the effort to derive those parameter functions using UMVUE method. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis of truncation limit in probabilistic safety assessment

A truncation limit defines the boundaries of what is considered in the probabilistic safety assessment and what is neglected. The truncation limit that is the focus here is the truncation limit on the size of the minimal cut set contribution at which to cut off. A new method was developed, which defines truncation limit in probabilistic safety assessment. The method specifies truncation limits with more stringency than presenting existing documents dealing with truncation criteria in probabilistic safety assessment do. The results of this paper indicate that the truncation limits for more complex probabilistic safety assessments, which consist of larger number of basic events, should be more severe than presently recommended in existing documents if more accuracy is desired. The truncation limits defined by the new method reduce the relative errors of importance measures and produce more accurate results for probabilistic safety assessment applications. The reduced relative errors of importance measures can prevent situations, where the acceptability of change of equipment under investigation according to RG 1.174 would be shifted from region, where changes can be accepted, to region, where changes cannot be accepted, if the results would be calculated with smaller truncation limit.     

Demonstration of probabilistic ordinal optimization concepts for continuous-variable optimization under uncertainty

A very general and robust approach to solving optimization problems involving probabilistic uncertainty is through the use of Probabilistic Ordinal Optimization. At each step in the optimization problem, improvement is based only on a relative ranking of the probabilistic merits of local design alternatives, rather than on precise quantification of the alternatives. Thus, we simply ask the question: “Is that alternative better or worse than this one?” to some level of statistical confidence we require, not: “HOW MUCH better or worse is that alternative to this one?”. In this paper we illustrate an elementary application of probabilistic ordinal concepts in a 2-D optimization problem. Two uncertain variables contribute to uncertainty in the response function. We use a simple Coordinate Pattern Search non-gradient-based optimizer to step toward the statistical optimum in the design space. We also discuss more sophisticated implementations, and some of the advantages and disadvantages versus other approaches to optimization under uncertainty.     

Statistics and probabilistic modeling of simulated intergranular cracks

Using Monte Carlo simulation, the statistical properties of intergranular crack trajectories in polycrystalline materials are estimated. The polycrystalline microstructures are two dimensional and are modeled by a Poisson–Voronoi tessellation for the grain geometry and a uniform orientation distribution function for the crystallographic orientation. A heuristic is introduced for determining the path of crack propagation when the crack tip arrives at a grain boundary triple junction. This heuristic applies a combination of two criteria for determining the direction of crack propagation, the maximum circumferential stress criterion, and a criterion in which the crack is assumed to propagate in the direction with the least material resistance. The resistance of grain boundaries is assumed to be related to the crystallographic misorientation at the grain boundary. The trajectories of microcracks can be treated as a random process, and simulation results indicate that the crack process exhibits linear variance growth, the rate of which is related to the importance attached to the circumferential stress and the material resistance in determining the direction of propagation. The rate of variance growth is shown to vary with the average grain diameter, so that microcracks in polycrystals with small grain size will exhibit less spatial uncertainty. The statistics and distributions of the increments of the crack process are also given. Through a small change made to the normalization applied to non-dimensionalize the statistics, the results are extended to polycrystals that have spatially varying grain size. Finally, a probabilistic model is proposed that is able to produce synthetic crack trajectories that replicate the important statistical properties of the simulated cracks. Such a model may prove useful in studies of the transition from micro to macrocracking.