Knowledge acquisition for decision support systems on an electronic assembly line

Increasing global competition has made many manufacturing companies recognize that competitive manufacturing in terms of low cost and high quality is crucial for success. Real-time process control and production optimization are, however, extremely challenging areas because manufacturing processes are getting ever more complex and involve many different parameters. This is a major problem when building decision support systems especially in electronics manufacturing. Although problem-solving is a knowledge intensive activity undertaken by people on the production floor, it is quite common to have large databases and run blindly feature extraction and data mining methods. Performance of these methods could, however, be drastically increased when combined with knowledge or expertise of the process.This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the efficient acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the factory-wide sharing of the resulting information for an improved defect management. Software with dedicated interfaces has been developed using a knowledge representation that supports portability and flexibility of the system. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.     

A data model for processes based on relative time

Advanced database applications such as automated manufacturing, scheduling, and computer-aided software engineering, demand an explicit representation of processes, including their decomposition into subprocesses, where subprocesses may be repeated or shared. Temporal information on these processes is inherently relative to particular temporal frames of reference, that may be different from that of a complex process containing them. We suggest the Rtime object-oriented data model in which processes are first-class citizens and complex processes are built, using standard type constructors, from their component processes. The relative timing of component processes is a key feature of the suggested model. It allows for a modular construction of complex process objects that may be repeated and shared. Standard object-oriented query languages can be used for temporal queries on processes, by providing an operator for translating timing information between different temporal frames of reference.     

Establishing information requirements for supervisory controllers in a flexible manufacturing system using GTA

In this article we consider the technological change that has occurred in complex manufacturing systems within the past two decades and the implications it has had on the role of human operators in manufacturing systems control. Our examination ranges from the traditional production line manned by skilled machinists to flexible manufacturing systems (FMS) under supervisory control. On the basis of this study, we raise the question as to whether new advanced manufacturing technology interfaces are supportive of human operators in their responsibilities to manufacturing systems. We address this problem by analyzing supervisory controller information requirements for intervening in complex process control tasks as part of FMS operation. This analysis was conducted using a cognitive engineering research methodology, which has not previously been applied, in the domain of manufacturing. The method of GTA was applied to supervisory control of an FMS and produced detailed information requirements, which facilitated the formulation of general design guidelines for FMS interface design. The guidelines are aimed at supporting human operator process strategy development and decision making. © 2000 John Wiley & Sons, Inc.     

A decision support algorithm for flexible manufacturing systems control

An application of a modification of Petri nets to mathematical modeling of discrete production processes utilized for a simulation of the flexible manufacturing systems is presented and an algorithm for the support of decision making in the control process of such systems based on the system model analysis is created. The algorithm makes the choice of a suitable control strategy possible in such a way that it automatically generates all potentially possible control strategies, automatically chooses the really possible ones and gives to an operator the chance to choose the most suitable realizable control strategy from the technological point of view.     

A data/knowledge paradigm for the modeling and design of operationssupport systems

The paper develops the Smart Object paradigm and its instantiation, which provide a new conceptualization for the modeling, design, and development of an important but little researched class of information systems, operations support systems (OSS). OSS is the authors' term for systems which provide interactive support for the management of large, complex operations environments, such as manufacturing plants, military operations, and large power generation facilities. The most salient feature of an OSS is its dynamic nature. The number and kind of elements composing the system as well as the mode of control of those elements change frequently in response to the environment. The abstraction of control and the ease with which complex dynamic control behavior can be modeled and simulated is one of the important aspects of the paradigm. The framework for the Smart Object paradigm is the fusion of object-oriented design models with declarative knowledge representation and active inferencing from AI models. Additional defining concepts from data/knowledge models, semantic data models, active databases, and frame based systems, are added to the synthesis as justified by their contribution to the ability to naturally model OSS at a high level of abstraction. The model assists in declaratively representing domain data/knowledge and its structure, and task or process knowledge, in addition to modeling multilevel control and interobject coordination     

Preliminary design and manufacturing planning integration using web-based intelligent agents

Software agents have been increasingly used in the product and process development in industry over the past years due to the rapid evolvement of the Internet technology. This paper describes agents for the integration of conceptual design and process planning. Agents provide mechanisms to interact with each other. This mechanism is important since both of those processes involve negotiations for optimization. A set of design and planning software agents has been developed. These agents are used in a computer-based collaborative environment, called a multi-agent platform. The main purpose of developing such a platform is to support product preliminary design, optimize product form and structure, and reduce the manufacturing cost in the early design stage. The agents on the platform have access to a knowledge base that contains design and planning rules. These rules are derived from an analysis of design factors that influence process and resource planning, such as product material, form, shape complexity, features, dimension, tolerance, surface condition, production volume, and production rate. These rules are used by process planning agents to provide process planners with information regarding selecting preliminary manufacturing processes, determining manufacturing resources, and constructing feedback information to product designers. Additionally, the agents communicate with WEB servers, and they are accessible by users through Internet browsers. During performing design and planning tasks, agents access the data pertinent to design and manufacturing processes by the programming interfaces of existing computer-aided design (CAD) and manufacturing system. The agents are supported by a developed prototype agent platform. The agents and the platform enable the information exchange among agents, based on a previously developed integrated design and manufacturing process object model.     

Perspective: A Standards-Based System for Manufacturing Information Integration

This paper presents an infrastructure and a prototype system for a manufacturing information system, which is distributed its nature and is able to store, index, manage, retrieve and present business data, inventory data, and manufacturing processes data. The system works with all kinds of information, such as continuous (i.e., stream oriented) data, production (e.g., decision support) data, legacy data, and multimedia data (say, drawings, pictures, audio signals, voice annotations, and video streams). A key criterion is support for content-based information retrieval across all application areas. The main objective is to provide support for automated information transactions. The prototype of our architecture uses JAVA, STEP (ISO 10303) standard, the Internet, and CORBA. A fully functional system, called Perspective, for retrieval of part and manufacturing process has been designed and implemented in a distributed environment. Among other capabilities, the system can retrieve a set of parts or manufacturing processes based on similarity to some desired criteria.     

Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing resources. To maximize the production efficiency and balance the trade-off among different production orders, this paper studies multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, which containing order priority assignment, supplier and production process selection, and production line scheduling. Five key objectives are taken into account to analyze the interconnections among different resources and production processes. Six representative multi-objective evolutionary algorithms are adopted to solve the integrated scheduling problem. Experimental results on six production cases show that integrated scheduling is more effective than the traditional step-by-step decision, leading to less production cost and time. In addition, a comparison among the six algorithms is carried out to determine the one best suited for the integrated scheduling problem in different circumstances.     

QEX: An in-process quality control expert system

True computer-integrated manufacturing requires that many links between factory processes become more automated, more computerized, and more intelligent. Many links between processes, however, are based on human judgement, experience, and intuition. These are naturally difficult tasks to automate, but the advent of artificial intelligence, and specifically expert system programming, has made such links possible. This paper presents a research effort toward one of these links, that of quality control judgements of ongoing processes. The use of expert system programming, combined with in-process metrology and system integration, allows the factory to be more fully automated and computer-integrated, resulting in higher precess precision and lower production errors. This paper will discuss how the expert system approach is used to integrate the quality judgement process into the production process, and bring the factory one step further toward total CIM.     

An experimental study on augmented reality assisted manual assembly with occluded components

Augmented Reality (AR) technology has increasingly been applied to facilitate manufacturing tasks such as training, maintenance, and safety management, in which manual assembly frequently occurs. Previously, studies confirmed the values of AR-based assembly systems, but few explored support of spatially restricted assembly where components are visually occluded from operator. In this regard, this research aims to develop new AR functions that assist manual assembly in such situations. The focus is on validating the effectiveness of various assistive information in AR, including assembly interface, operator’s hand movement, and operator held components. Subjective and objective measures are used to evaluate assembly experiments of different degrees of difficulty. Analyses of the experimental data reveal whether or not and how effectively each information offers guidance in occluded condition. In particular, a time reduction in more difficult assembly is realized by showing operator hand movement in AR. The hand model initially offers an operator a visual clue for quickly and roughly locating the assembly interface in a large unseen area, prior to precisely localizing in a smaller region guided by tactile sensing. However, the effectiveness of incorporating the held component is not evident, as positional deviation between real and virtual objects may reduce human’s hand-eye coordination. These findings not only provide preliminary design guidelines of AR assembly functions for occluded components but also demonstrates a novel yet practical application of AR technology in smart manufacturing.     

面向实时嵌入式操作系统的进程机制

面向通信领域的嵌入式程序必须在资源受限的硬件环境中应对不断增加的通信业务,单纯依靠商用嵌入式操作系统的任务机制已不能提供足够的业务并行度和吞吐量。针对该问题,基于嵌入式操作系统任务机制提出一种更小粒度的进程解决方案,相对于任务对象,使用进程作为执行单元不仅内存资源占用少,且进程之间切换速度快,系统可以支持大量进程并行。该进程机制能够提供有效的系统监测和故障诊断手段,从而保证系统的健壮性。     

Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting

Decision trees (DTs) are effective in extracting linguistically interpretable models from data. This paper shows that DTs can also be used to extract information from process models, e.g. they can be used to represent homogenous operating regions of complex process. To illustrate the usefulness of this novel approach a detailed case study is shown where DTs are used for forecasting the development of runaway in an industrial, fixed bed, tube reactor. Based on first-principles knowledge and historical process data the steady-state simulator of the tube reactor has been identified and validated. The runaway criterion based on Ljapunov's indirect stability analysis has been applied to generate a data base used for DT induction. Finally, the logical rules extracted from the DTs are used in an operator support system (OSS), since they are proven to be useful to describe the safe operating regions. A simulation study based on the dynamical model of the process is also presented. The results confirm that by the synergistic combination of a DT based on expert system and the dynamic simulator a powerful tool for runaway forecasting and analysis is achieved and it can be used to work safe operating strategies out.     

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.     

An investigation of a human material handler on part flow in automated manufacturing systems

This paper presents a formal approach to resolve an important question concerning changes in the control of computerized manufacturing systems when a human operator is involved as a task-performing agent. It requires building a model of human functional specifications used in executing tasks and integrating it into a control scheme for the model. More importantly, analysis of control complexity needs to be conducted to build an effective control mechanism. In this paper, a human material handler is considered, and an assessment of part flow complexity affected by human tasks in a highly automated manufacturing system is presented. For this purpose, a formal model of human task-performing processes is proposed in terms of a part and location(s) of a task. A classification for human material handling tasks is presented based on the proposed model. Furthermore, human errors and the impact of human errors on part flow are considered. Part flow complexity of a manufacturing system from the control perspective is then investigated in terms of the human tasks and errors. A shop floor control example where a human operator performs material handling tasks is provided to illustrate the proposed model.     

A knowledge-based inspection process planning system for coordinate measuring machines

The coordinate measuring machine (CMM) is one of the most effective geometry inspection facilities used in manufacturing industry. To fully utilize its capabilities in a computer-integrated manufacturing (CIM) environment, we should integrate CMM with other systems and facilities. This paper presents the development of a knowledge-based inspection planner based on the fundamental principles of AI planning to integrate computer-aided design systems and CMMs. The issues involved in CAD-directed inspection process planning are examined; the task of inspection process planning is decomposed into a number of sub-tasks. According to the task decomposition, a knowledge-based planning system was designed with several modules. Each of these modules consists of a knowledge base, a control operator, a context and a communication interface. The knowledge base is the local knowledge source for problem solving; the control operator determines when and where the knowledge is applied; the context contains the initial planning state which is essential input part information, the intermediate planning states which result from the tentative decision made by the modules, and the goal state. The module interfacing was realized by directly calling procedures defined in other modules to pass the planning tasks and decisions. Examples are included to explain the planning knowledge and strategy.     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

A decision support system for product design in concurrent engineering

Compared with the traditional sequential design method, concurrent engineering is a systematic approach to integrate concurrent design of products and their related processes. One of the key factors to successfully implement concurrent engineering is information technology. In order to design a product and its manufacturing process simultaneously, information on product features, manufacturing requirements, and customer demands must be processed while the design is concurrently going on. There is an increased understanding of the importance of the correct decisions being made at the conceptual design and development stages that involve many complex evaluation and decision-making tasks. In order to promote the efficiency in concurrent product development, appropriate evaluation and decision tools need to be provided. In this paper, the characteristics of fuzzy, multi-stage evaluation and decision making in concurrent product development process are analyzed and a decision support system for product design in concurrent engineering is presented. An example is given to illustrate the application of the system.     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

大数据驱动的投诉预测模型

随着电信行业市场竞争的不断加剧,用户对服务质量要求逐步提高,导致用户投诉率不断攀升。在此情况下,通过准确预测用户投诉行为来降低用户投诉率成为运营商关注的重点。目前传统的投诉预测模型仅从分类算法和人工调研特征来讨论,而没有充分利用运营商的大数据。因此,提出了在Hadoop/Spark大数据平台上使用并行随机森林来构建用户预测投诉模型,它不仅用到了业务支持系统数据,而且还用到了运营支持系统数据和客服工单数据,并在此基础上进一步增加了反映用户相互关系的图特征和二阶特征。基于上海市某运营商数据的实验结果表明,利用多来源、高维度的特征来训练用户投诉预测模型的精度会明显高于传统方法,在此基础上有针对性地对目标用户采取安抚措施,可以降低用户投诉率,获得较高的商业价值。