Modular machine tools: Design and barriers to industrial implementation

The Reconfigurable Manufacturing System (RMS) paradigm has been developed to address challenges in the design of manufacturing systems and equipment that will meet the demands of modern manufacturing. This research involved the development of Modular Reconfigurable Machines (MRMs); as an emerging technology in reconfigurable manufacturing. MRMs are mechanically modular machines. The modularity permits the kinematic architecture and processing functions of the machine to be reconfigured to meet changing production requirements. This paper will focus on aspects of the mechanical design and the development of a control system that supported the modularity and reconfigurability of the mechanical platform. A modular electronic system is presented that is characterized by a plug and play approach to control scalability. This is complemented by a software architecture that has been developed with a focus on hardware abstraction for the management of an augmented mechanical and electronic architecture. The implications of MRMs for RMSs are discussed and key inhibitors to industrial implementation are identified.     

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.     

Domain-specific application analysis for customized instruction identification

With the increasing importance of Application Domain Specific Processor (ADSP) design, a significant challenge is to identify special-purpose operations for implementation as a customized instruction. While many methodologies have been proposed for this purpose, they all work for a single algorithm chosen from the target application domain. Such algorithm-specific approaches are not suitable for designing instruction sets applicable to a whole family of related algorithms. For an entire range of related algorithms, this paper develops a methodology for identifying compound operations, as a basis for designing “domain-specific” Instruction Set Architectures (ISAs) that can efficiently run most of the algorithms in a given domain. Our methodology combines three different static analysis techniques to identify instruction sequences common to several related algorithms: identification of (non-branching) instruction sequences that occur commonly across the algorithms; identification of instruction sequences nested within iterative constructs that are thus executed frequently; and identification of commonly-occurring instruction sequences that span basic blocks. Choosing different combinations of these results enables us to design domain-specific special operations with different desired characteristics, such as performance or suitability as a library function. To demonstrate our approach, case studies are carried out for a family of thirteen string matching algorithms. Finally, the validity of our static analysis results is confirmed through independent dynamic analysis experiments and performance improvement measurements.     

Technical investment planning of flexible manufacturing systems—The application of practice-oriented methods

Flexible manufacturing systems (FMS) exhibit a high degree of automation of the machining system and of the workpiece, and for the information flow systems. Therefore, these systems are amongst the most complex production installations and their technical investment planning must be undertaken with great care.This paper introduces a strategic planning procedure and describes auxiliary aids for the selection of the components and for structuring of the overall system. Computer programs for the selection of machine tools and for the simulation of the manufacturing process sequence are part of this procedure. The application of the methods is illustrated by means of several problem situations arising from industrial production companies; the examples show the practical usefulness of the developed auxiliary techniques. The planning accuracy possible by these methods reduces the investment risk associated with the use of these complex manufacturing systems.Also described is an integrated system for the technical investment planning of flexible manufacturing systems which comprises both existing methods and new auxiliary means for planning the machining systems and the technical/organizational overall concept.     

Design of a reconfigurable prognostics platform for machine tools

For decades, researchers and practitioners have been trying to develop and deploy prognostics technologies with ad hoc and trial-and-error approaches. These efforts have resulted in limited success, due to the fact that it lacks a systematic approach and platform in deploying the right prognostics tools for the right applications. This paper introduces a methodology for designing a reconfigurable prognostics platform (RPP) which can be easily and effectively used to assess and predict the performance of machine tools. RPP can be installed on the equipment and it has the prognostic capabilities to convert the data into performance-related information. The equipment performance information can then be integrated into the enterprise asset management system for maintenance decision making through the Internet. Two industrial cases are used to validate the effectiveness of applying the RPP for different prognostic applications as well as the reconfigurable capabilities of the proposed RPP.     

Lowest priority first based feasibility analysis of real-time systems

The feasibility problem of periodic tasks under fixed priority systems has always been a critical research issue in real-time systems and a number of feasibility tests have been proposed to guarantee the timing requirements of real-time systems. These tests can be broadly classified into: (a) inexact and (b) exact tests. The inexact tests are applied to the task sets that present lower utilization, while the exact tests become inevitable when system utilization is high. The exact tests can be further classified into: (a) Scheduling Points Tests (SPT) and (b) Response Time Tests (RTT). The SPT analyze task set feasibility at the arrival times while the RTT utilize fixed-point techniques to determine task feasibility. All of the available exact feasibility tests, whichever class it belongs to, share pseudo-polynomial complexity. Therefore, the aforementioned tests become impractical for online systems. Currently, both SPT and RTT employ the Highest Priority First (HPF) approach, which determines the system feasibility by testing the schedulability of individual tasks in the decreasing order of priority. In contrast, this work exploits the Lowest Priority First (LPF) alternative which is an aggressive solution based on the observation that the system infeasibility is primarily due to the lower priority tasks and not because of the higher priority tasks. For the average case analysis, our technique demonstrates promising results. Moreover, in the worst case scenario our solution is no inferior to the existing state of the art alternatives. We compare our proposed technique with the existing tests: (a) by counting the number of scheduling points used by a test that belongs to the SPT, (b) by counting the number of inner-most loops executed by an algorithm for the RTT, and (c) by measuring the actual running time of the existing alternatives.     

特种车辆驾驶人机界面匹配评价系统的研究

特种车辆驾驶人机界面匹配问题是复杂而重要的问题.根据人机工程学原理和研究方法,对特种车辆驾驶人机界面匹配问题进行了系统研究,提出了一套定量化的驾驶人机界面综合评价计算模型.以此为依据研发了特种车辆驾驶人机界面的计算机评价计算系统,介绍了系统的设计思想及数据处理方法.运用该系统对某军用车辆进行了评价,得出了与实际情况非常吻合的评价结果.     

Stiffness analysis of hexaslide machine tools

This paper presents stiffness analysis of hexaslides, a class of parallel manipulators with six constant length legs, the base joints of which move along six distinct rail-axes. In the design of hexaslides for machine tool applications, stiffness as well as its variation within the workspace is important. The stiffness of any parallel kinematic machine and, thus, the accuracy depends on the pose of the tool platform. A stiffness model of a generic hexaslide is developed here considering the flexibilities in both the actuator and the legs of the hexaslide. The methodology adopted is based on the concept of the determination of stiffness of a group of serially connected linear springs. Based on the proposed model, the average stiffness and its variation along and about the Cartesian coordinate axes offered by three typical hexaslides, having the same foot print size, are estimated and compared. Other typical performance indices, i.e. workspace volume, workspace volume index (ratio of workspace volume to machine size), global dexterity index and global stiffness index, are also compared.     

Technology of the unmanned operation of flexible manufacturing systems

In this paper the hardware structure of a flexible manufacturing system and the information flow for operation of the system are reviewed first. Then the technology required for the unmanned operation of the system, especially monitoring the machining state, control of the geometrical accuracy of a workpiece and monitoring the machine operation are described in detail. Finally the technical and social tasks required for development of a flexible manufacturing system in the future are discussed.     

The application of manufacturing culture to the design of Asian region oriented machine tools

During the last years the demand for regionally and culturally harmonised machine design is increasingly on the agenda. The problem of localising products like machine tools instantly poses the question for new procedures that allow including the regional and cultural adaptations into the design processes of machine tool companies. How to transform the general insight into the necessity of culture- and region-adapted technologies and how to embed it into a design procedure comprising applicable design attributes is the crucial problem addressed. The paper shows in an exemplary way how ambiguous design attributes can eventually be embodied in a prototype design.     

图形处理器在分层聚类算法中的通用计算研究*

ROCK是一种采用数据点间的公共链接数来衡量相似度的分层聚类方法,该方法对于高维、稀疏特征的分类数据具有高效的聚类效果。其邻接度矩阵计算是影响时间复杂度的关键步骤,将图形处理器(GPU)强大的浮点运算和超强的并行计算能力应用于此步骤,而其余步骤由CPU完成。基于GPU的ROCK算法的运算效率在AMD 643500+ CPU和NVIDIA GeForce 6800 GT显卡的硬件环境下经过实验测试,证明其运算速度比完全采用CPU计算速度要快。改进的分层聚类算法适合在数据流环境下对大量数据进行实时高效的聚类的     

An improved physical demand analysis framework based on ergonomic risk assessment tools for the manufacturing industry

Most of the operational tasks in the manufacturing process entail numerous physical involvements, despite the introduction of automated equipment. Due to this high physical demand in manufacturing, the need for proactive risk assessment to decrease potential injury cannot be ignored. Physical Demand Analysis (PDA) is a widely used tool recommended to all manufactures by the Canadian Workers’ Compensation Board to document the physical, cognitive, and environmental demands of essential tasks. However, limitations exist in utilizing the content generated in current PDA practice to conduct risk identification and risk assessment, and it has limited functionality for providing modified work to proactively mitigate risk. This paper summarizes the input requirements of risk assessment tools and proposes an improved PDA form with an integrated framework to facilitate the comprehensive and intelligent use of PDA. This paper focuses on three aspects of PDA implementation—risk identification, risk evaluation, and risk mitigation—targeting the development of modified work for the manufacturing industry. The framework is implemented in a window and door manufacturing facility, and a case study of a window glazing station is described in this paper in order to assess awkward body postures. Four main ergonomic risk assessments and identifications are recommended.     

独立成分分析和支持向量机混合方法在过程监控中的应用

为克服传统过程监控方法需假设过程特征信号服从多元正态分布的缺陷,本文提出了一种将独立成分分析(ICA)与支持向量机结合的故障诊断方法。通过建立独立成分模型确定相应的统计量界限,筛选出需进一步检测的故障数据,再由支持向量机进行故障识别。将该方法用于化工聚合反应的过程监控与故障诊断中,仿真结果表明,这种混合故障诊断方法通过适当地调节统计量控制界限,不仅能够正确识别故障,而且能够纠正由误检数据引起的误报,提高故障诊断的准确率。     

数控机床液压系统故障溯源及分析*

以提高机床液压系统可靠性为目的,分析了液压传动的特点,并对常见的故障现象进行了整理,归纳出了振动异响、压力不足及波动大、流量不稳定、液压元器件损坏四种典型的故障模式。以某磨齿机为例,采用FTA(故障树分析)方法,对四种故障模式进行了故障原因溯源,找出了造成系统故障的根本原因。以压力故障为例进行了定量分析,对每个底事件的概率重要度和相对概率重要度进行了计算,并阐述了重要度分析在可靠性改进和控制过程中的重要作用。     

Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

The aim of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A – EEG signals recorded from healthy volunteers with eyes open and set E – EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive – AR, moving average – MA, least squares modified Yule–Walker autoregressive moving average – ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies.     

Perturbation analysis for production control and optimization of manufacturing systems

We use stochastic fluid models (SFM) to capture the operation of threshold-based production control policies in manufacturing systems without resorting to detailed discrete event models. By applying infinitesimal perturbation analysis (IPA) to a SFM of a workcenter, we derive gradient estimators of throughput and buffer overflow metrics with respect to production control parameters. It is shown that these gradient estimators are unbiased and independent of distributional information of supply and service processes involved. In addition, based on the fact that they can be evaluated using data from the observed actual (discrete event) system, we use them as approximate gradient estimators in simple iterative schemes for adjusting thresholds (hedging points) on line seeking to optimize an objective function that trades off throughput and buffer overflow costs.     

Predicting time series of railway speed restrictions with time-dependent machine learning techniques

In this paper, a hybrid approach to combine conditional restricted Boltzmann machines (CRBM) and echo state networks (ESN) for binary time series prediction is proposed. Both methods have demonstrated their ability to extract complex dynamic patterns from time-dependent data in several applications and benchmark studies. To the authors’ knowledge, it is the first time that the proposed combination of algorithms is applied for reliability prediction.The proposed approach is verified on a case study predicting the occurrence of railway operation disruptions based on discrete-event data, which is represented by a binary time series. The case study concerns speed restrictions affecting railway operations, caused by failures of tilting systems of railway vehicles. The overall prediction accuracy of the algorithm is 99.93%; the prediction accuracy for occurrence of speed restrictions within the foresight period is 98% (which corresponds to the sensitivity of the algorithm). The prediction results of the case study are compared to the prediction with a MLP trained with a Newton conjugate gradient algorithm. The proposed approach proves to be superior to MLP.     

Enhanced pose normalization and matching of non-rigid objects based on support vector machine modelling

The estimation of 3D surface correspondence constitutes a fundamental problem in shape matching and analysis applications. In the presence of non-rigid shape deformations, the ambiguity of surface correspondence increases together with the complexity of registration algorithms.     

机器翻译中句法分析的设计与实现

论述了英汉机器翻译中句法分析的设计与实现方法。首先阐述了编译原理中句法分析的相关理论,并以此理论为依据提出了机器翻译中句法分析的具体实现。实现过程中,采用部分分析的思想,将一个句子划分为多个语法成分,分别对各语法成分进行分析,从而完成对待翻译句子的句法分析,给出句法树。     

Comparing the results of five lifting analysis tools

The objective of this study was to compare the results of the NIOSH, ACGIH TLV, Snook, 3DSSPP and WA L&I lifting assessment instruments when applied to a uniform task (lifting and lowering milk cases with capacities of 15 and 23l). To enable comparisons between the various lifting assessment instruments, the outputs of each method were converted to an exposure index similar to the NIOSH Lifting Index. All instruments showed higher exposures associated with lifting the 23l cases versus the 15l cases. The NIOSH, ACGIH TLV and Snook methods were similar in their results with respect to the pattern of exposure over various height levels and the differences in exposures associated with lifting 15 and 23l cases. However, the WA L&I and 3DSSPP predicted substantially lower exposures. The reasons for instrument differences are presented so that practitioners can better select the methods they need and interpret the results appropriately.