Development of real-time sketch-based on-the-spot process modeling and analysis system

Contemporary manufacturing processes require faster real-time controls against dynamic and volatile production environments. While a corresponding simulation model is considered a prerequisite system for the real-time control of a contemporary manufacturing process, simulation modeling and relevant analysis have supported these real-time features comparatively less. These issues might cause procrastination of the simulation modeling, and result in wrong decisions and inaccurate controls. In order to overcome these issues, a new real-time simulation modeling and analysis system is proposed. The proposed system supports sketch-based simulation modeling. The simulation model is constructed using modelers’ sketches of predefined simulation symbols. The sketches are converted automatically into a corresponding stochastic queueing network using Self-organizing Map, a type of neural network. Then, the model is simulated and analyzed using the embedded stochastic queueing analyses. The effectiveness of the proposed system is proven with the modeling, simulation and analyses of several real-time manufacturing cases.     

A knowledge-based system for information management in an automated and integrated manufacturing system

In recent years there has been considerable interest in flexible automation of manufacturing processes in an effort to improve the productivity of manufacturing industry. Central to an integrated, flexible and intelligent manufacturing system is the information management function. The sharing of data is the basis of flexible and intelligent decision-making, the means of integrating the system components, the mode of synchronizing their processes and the method of maintaining the consistency of their states.

It appears that the data management and manipulation function in manufacturing systems is increasingly becoming a bottleneck for further performance improvement of manufacturing systems. New information management systems need to be developed that meet the needs of modern manufacturing systems in terms of efficiency, flexibility, reliability and intelligence. A new model for an integrated manufacturing information system is presented. A double structure of two parallel but independent system is proposed for manufacturing control and manufacturing information management. The objective is to improve service efficiency. representational adequacy, consistency of information and reliability in the information system.     

Stochastic models for performance analysis of multistate flexible manufacturing cells

Performance analysis of flexible manufacturing cells (FMCs) can help companies find the pros and cons of production processes. However, the emphasis has been on issues like cell formation, layout design and scheduling optimization. Little seems to have been done to assess the reliability of an FMC. In this paper, we develop the stochastic models for the performance analysis mainly on the reliability of two different FMCs configured from a set of teaching intelligent flexible manufacturing system (TIFMS). The closed form solutions of probabilities of system states are obtained. Then, utilization rate of equipment in the cell and productivities of the two FMCs as the performance indexes are calculated and optimized. Compared to simulation methods, the closed form solutions make calculations of the performance indexes faster and more accurate. When random variables in the stochastic models are assumed to follow non-exponential distributions, the effects of them on the performance indexes are discussed. The objective of this paper is to fill up the gap that the closed form solutions are difficult to obtain as the number of machine tool increases. Another objective is to optimize the performance indexes to help engineers better evaluate the performance of FMC. Numerical analysis cases are used to illustrate the proposed stochastic models.     

A real-time production operations decision support system for solving stochastic production material demand problems

Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks not only involve stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problems. Based on Poon et al. (2009), three additional tests are proposed to evaluate RFID reading performance. Besides, by using RPODS, the real-time status of production and warehouse operations are monitored by Radio Frequency Identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible solutions for tackling these stochastic production demand problems. The capability of the RPODS is demonstrated in a mould manufacturing company. Through the case study, the objectives of reducing the effect of stochastic production demand problems and enhancing productivity both on the shop floor and in the warehouse are achieved.     

Data-Enabled Modeling and Analysis of Multistage Manufacturing Systems with Quality Rework Loops

In a multistage serial production line, multiple inspection stations and repair processes are typically involved to ensure high product quality. Quality rework is the activity to repair or repeat the work on the defect parts during manufacturing processes. The rework process after each inspection can add cost and cycle time to the normal process and impose negative impact on the throughput. This paper studies real-time performance of multistage serial manufacturing systems with quality rework loops and machine random failures. A production line with multiple quality rework loops is first unify by segmenting it into a set of serially connected quality rework loops. An event-based data-enabled mathematical model is developed to evaluate real-time production rate of each machine for such a system. In addition, the system properties are analyzed and permanent production loss due to quality rework loops and random machine failures are identified respectively. The permanent production loss attribution to each disruption event and machine can be used as real-time performance indicators to diagnose production system inefficiency. The mathematical model and system performance identification methodology are studied analytically and validated through numerical case studies.     

An integrated modelling framework to support manufacturing system diagnosis for continuous improvement

This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system.     

基于正态Petri网的模型参数识别研究

时间因素在对实时系统的分析中占据着重要的位置。随着时间因素在Petri网应用分析中的地位越发重要,各种与时间相关的Petri网模型相继被提出。文中结合动态实时系统的运行特征,设计了一种变迁点火时间服从正态分布的随机Petri网模型,并基于对系统行为的统计分析,提出了一种用于对变迁时间参数进行识别的数值算法。以一种随机制造单元为例,建立正态随机Petri网模型,并运用参数识别算法确定变迁时间参数。仿真结果符合系统的实际运行结果,证明了该方法的有效性。     

智能制造系统基于数据驱动的车间实时调度

智能制造系统采用大量先进的信息技术,为车间实时调度提供技术基础.各类信息技术在生产制造过程中的广泛应用使得制造系统积累了大量与生产调度相关的数据,因此,通过利用历史生产调度数据和智能装备收集到的实时生产数据,建立基于数据驱动的生产实时调度方法成为新型制造环境下实现高效调度的新思路.针对智能制造环境下的混合流水车间实时调度问题,提出基于BP神经网络的数据驱动的实时调度方法,从历史近优的调度方案中提取用于调度知识挖掘的样本数据,通过BP神经网络训练学习获取生产系统状态与调度规则的映射关系网络,并将其应用于生产在线实时调度.数值实验表明,所提出的方法优于固定单一调度规则,在不同的调度性能指标下其效果均稳定且良好.     

Academic excellence in manufacturing engineering

Manufacturing Systems are inherently complex and interdisciplinary, and are normally analyzed in a piecewise fashion using experimental techniques which provide relatively little physical insight or theoritical methods brrowed from other disciplines (e.g., structural mechanics, control theory, etc.). For these reasons Manufacturing Engineering is often considered an unscientific and intuitive subject. With the increasing demand for manufacturing systems to operate at higher production rates without human intervention to reduce manufacturing costs, it is becoming increasingly important to develop scientifically based, general, and efficient analysis tools specifically tailored to the complex interdisciplinary problems encountered in Manufacturing Engineering. In addition to having direct practical benefits, such tools would stimulate academic interest in this field and help alleviate current academic and industrial personnel shortages in the manufacturing area.

This paper describes one such analysis tool, the Dynamic Data System (D.D.S.) methodology, which has been developed at the University of Wisconsin. The D.D.S. methodology combines time series and systems analysis concepts in a computer-based modeling strategy for obtaining a physically meaningfully model of a system directly from input and output data in the form of stochastic difference/differential equations. The methodology can be applied to forecasting, control, system identification, characterization, signature analysis, and design. The basic features of the methodology, representative applications to the on-line detection and suppression of chatter in turning and the active compensation for roundness errors in boring, and areas for future development are discussed.     

Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems

This paper describes a simulation-based decision support system (DSS) to production control of a stochastic flexible job shop (SFJS) manufacturing system. The controller design approach is built around the theory of supervisory control based on discrete-event simulation with an event–condition–action (ECA) real-time rule-based system. The proposed controller constitutes the framework of an adaptive controller supporting the co-ordination and co-operation relations by integrating a real-time simulator and a rule-based DSS. For implementing SFJS controller, the proposed DSS receives online results from simulator and identifies opportunities for incremental improvement of performance criteria within real-time simulation data exchange (SDX). A bilateral method for multi-performance criteria optimization combines a gradient based method and the DSS to control dynamic state variables of SFJS concurrently. The model is validated by some benchmark test problems.     

On asymptotic properties of real-time identification algorithms based on dynamic stochastic approximation

The algorithms of dynamic stochastic approximation type are proposed for real-time identification of multivariable linear dynamic discrete-time systems with stochastic parameters. These algorithms can be considered as the general representatives of a class of gradient-type equation-error recursive identification methods. The analysis of their asymptotic properties is presented. It is proved that the algorithms converge either in the mean-square sense, or in the sense of keeping the mean-square error bounded, depending on system parameter properties. Convergence conditions are expressed in terms of inherent system characteristics, e.g., properties of the impulse response matrices and their realizations. A large class of input random processes is supposed.     

Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment

The lack of timely feedback shopfloor information during manufacturing execution stage leads to significant difficulties in achieving real-time production scheduling. To address this problem, an overall architecture of multi-agent based real-time production scheduling is presented to close the loop of production planning and control. Several contributions are significant. Firstly, wireless devices such as radio frequency identification (RFID) are deployed into value-adding points in a ubiquitous shopfloor environment to form Machine Agent for the collection and processing of real-time shopfloor data. Secondly, Capability Evaluation Agent is designed to optimally assign the tasks to the involved machines at the process planning stage based on the real-time utilization ration of each machine. The third contribution is a Real-time Scheduling Agent for manufacturing tasks scheduling/re-scheduling strategy and methods according to the real-time feedback. Fourthly, a Process Monitor Agent model is designed for tracking and tracing the manufacturing execution based on a critical event structure. Finally, a case is used to demonstrate the proposed multi-agent based real-time production scheduling models and methods.     

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.     

iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems

We describe an intelligent co-simulator for real time production control of a complex flexible manufacturing system (CFMS) having machine and tool flexibility. The manufacturing processes associated with the CFMS are complicated with each operation being possibly done by several machining centers. The co-simulator design approach is built upon the theory of dynamic meta-model based supervisory control with the cooperation of its own embedded intelligent blocks. The system is implemented by coupling of the centralized simulation controller (CSC) and real-time simulator for enforcing dynamic strategies of shop floor control. The posteriori adaptive co-simulator is equipped with a concurrent bilateral mechanism for simulation optimization based on appropriate control rules enhancing performance criteria simulation efficiency. A working intelligent adaptive controller prototype (iCoSim-FMS) has been developed to validate the proposed approach and compare its performance with well known FMS heuristic methods.     

基于移动数据窗的传递函数多新息随机梯度辨识方法

一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证.     

运行时代码随机化防御代码复用攻击

代码复用攻击日趋复杂,传统的代码随机化方法已无法提供足够的防护.为此,提出一种基于运行时代码随机化的代码复用攻击防御方法LCR.该方法在目标程序正常运行时,实时监控攻击者企图获取或利用gadgets的行为,当发现监控的行为发生时,立即触发对代码进行函数块级的随机化变换,使攻击者最终获取或利用的gadgets信息失效,从而阻止代码复用攻击的实现.设计实现了LCR原型系统,并对提出的方法进行了测试.结果表明：LCR能够有效防御基于直接或间接内存泄漏等实现的代码复用攻击,且在SPEC CPU2006上的平均开销低于5%.     

RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises

In extended enterprises, real-time manufacturing information tracking plays an important role and aims to provide the right information to the right person at the right time in the right format to achieve optimal production management among the involved enterprises. However, many enterprises are caused by lack of timely, accurate and consistent manufacturing data. The laggard information transfer flow and the unmatched information transfer method bring extended enterprises much more uncertainty and unknowingness. This paper proposes a RFID-enabled real-time manufacturing information tracking infrastructure (RTMITI) to address the real-time manufacturing data capturing and manufacturing information processing methods for extended enterprises. Following the proposed infrastructure, the traditional manufacturing resources such as employees, machines and materials are equipped with RFID devices (Readers and Tags) to build the real-time data capturing environment. In addition, a series of manufacturing information processing methods are established to calculate and track the real-time manufacturing information such as real-time manufacturing cost, progress, WIP (Work-in-progress) inventory etc. in parts/assemblies/products at machines/shop floors/enterprises/ extended enterprises levels. Finally, a case study is given to demonstrate the developed framework and corresponding methodologies.     

Process-data-warehousing-based operator support system for complex production technologies

Process manufacturing is increasingly being driven by market forces, customer needs, and perceptions, resulting in more and more complex multiproduct manufacturing technologies. The increasing automation and tighter quality constraints related to these processes make the operator's job more and more difficult. This makes decision support systems (DSSs) for the operator more important than ever before. A traditional operator support system (OSS) focuses only on specific tasks that are performed. In the case of complex processes, the design of an integrated information system is extremely important. The proposed data-warehouse-based OSS makes possible linking complex and isolated production units based on the integration of the heterogenous information collected from the production units of a complex production process. The developed OSS is based on a data warehouse designed by following the proposed focus-on-process data-warehouse-design approach, which means stronger focus on the material and information flow through the entire enterprise. The resulting OSS follows the process through the organization instead of focusing separate tasks of the isolated process units. For human-computer interaction, front-end tools have been worked out, where exploratory data analysis and advanced multivariate statistical models are applied to extract the most informative features of the operation of the technology. The concept is illustrated by an industrial case study, where the OSS is designed for the monitoring and control of a high-density polyethylene (HDPE) plant.     

基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护

在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.如何利用现有资源,提高生产效率,实时地对系统性能进行评估与预测,并对基于小批量生产的实时调度进行优化改进,在分布式柔性生产系统中具有重要的研究意义.因此,基于退化机器模型的多批次串行生产线的性能进行分析,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先采用马尔科夫分析方法建立数学模型;随后,提出精确方法来计算此生产系统模型实时的性能指标,并针对该模型下的调度问题,设计最优完成时间指标优化算法;此外,提出基于退化机器模型的预测性维护策略以减少完成时间;最后,通过数值实验验证该算法的可行性和有效性.     

A method for identification of automation potential through modelling of engineering processes and quantification of information waste

Context: Automation of engineering processes is increasingly prevalent in multiple lifecycle phases such as design, manufacturing and service support. Though methodological and technological support for developing these systems has been achieved to a relatively high degree, means to provide objective identification and justification of automation opportunities are currently lacking. Objective: In this paper, a method for the identification of automation opportunities is introduced. Method: This method relies on the quantification of information waste in existing engineering processes. Results and Conclusions: A detailed case study application in the service engineering domain shows the feasibility and applicability of the developed method. Summaries of two other case studies are provided to highlight the generalizability to engineering processes from other lifecycle domains (design; production).