Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process

A hybrid intelligent approach based on relevance vector machines (RVMs) and genetic algorithms (GAs) has been developed for optimal control of parameters of nonlinear manufacturing processes. It concerns the finding of the near-optimal control parameters of the nonlinear discrete manufacturing process with a specific objective. First, the nonlinear process with measurement noise is regressed by the relevance vector learning mechanism based on a kernel-based Bayesian framework. For minimizing the approximate error, uniform design sampling, online incremental learning and cross-validation are used in the learning process of RVMs. Such well-trained models become a specialized process simulation tool, which is valuable in prediction and optimization of nonlinear processes. Next, the near-optimal setpoints of the control system, which maximize the objective function, are sought by GAs from the numerous values of the objective function obtained from the simulation. As a case study, the seed separator system (5XZW-1.5) is used for evaluating the proposed intelligent approach. The control parameters to reach the maximum weighted objective, which combine the system output and evaluation functions, are optimized. The experimental results show the effectiveness of the proposed hybrid approach.     

Designing a new model of distributed quality control for sub-assemble products based on the intelligent web information system

The quality control of sub-assemble products (SAP) in a distributed manufacturing shop (DMS) becomes crucial and complicated when the production of SAPs involves a variety of production technology. In this case, traditional statistical process control methods are not sufficient to control such manufacturing system. Here, we design an intelligent web information system, where quality data are collected from DMS and stored in the central database. The processes of manufacturing SAPs in DMS are then controlled by clustering homogenous SAPs using the quality control of SAP in DMS (QCSD) and process smoothness factor based SAP predefined clustering (PSFSPC) algorithms, respectively. A prototype system called an intelligent web information system quality control (IWIS-QC) has been developed to trace the quality profiles of SAPs. Finally, a case study has been presented to illustrate and validate the proposed approach.     

A CORBA-based agent-driven design for distributed intelligent manufacturing systems

An agent-oriented methodology is presented for representation, acquisition, and processing of manufacturing knowledge along with analysis and modeling of an intelligent manufacturing system (IMS). An intelligent manufacturing system adopts heterarchical and collaborative control as its information system architecture. The behavior of the entire manufacturing system is collaboratively determined by many interacting subsystems that may have their own independent interests, values, and modes of operation. The subsystems are represented as agents. An agent's architecture and task decomposition method are presented. The agent-oriented methodology is used to analyze and model an intelligent machine cell. An intelligent machine center is considered as an autonomous, modular, reconfigurable and fault-tolerant machine tool with self-perception, decision making, and self-process planning, able to cooperate with other machines through communication. The common object request broker architecture (CORBA) distributed software control system was developed as a simple prototype. A case study illustrates an intelligent machine center.     

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.     

Knowledge based prediction of nonlinear phenomena

 A pervasive task in decision making lies in the prediction of unknown phenomena. In the past, the majority of decision support tools has addressed linear phenomena or simple nonlinear tasks such as discriminatory analysis. However, recent advances in nonlinear theory and software techniques suggest the feasibility of developing generalized tools for analyzing nonlinear signals incorporating chaotic features. The goal of signal processing is to predict some aspect – whether temporal or spatial – of the underlying source. A temporal attribute is exemplified by the trajectory of an object, and a spatial characteristic by its shape. In the manufacturing environment, for instance, such predictive functions arise in the historical yield of a production line or the pattern of breakdowns. The primary functions of the proposed system are as follows: (1) forecasting the trajectory of signals, whether generated by an informational source such as a quality control center, or a physical source such as a tornado or a national economy, and (2) predicting spatial phenomena such as the location of a single object or the geographic dispersal of earthquakes. The feasibility of predicting the behavior of a single unit springs from the partial predictability of chaotic disturbances such as atmospheric turbulence in conjunction with the inertial properties of large objects such as vehicles. The practicality of predicting aggregate behavior derives from the limited dimensionality of interrelated systems, including cognitive and organizational processes in the context of numerous decision making agents. Nonlinear phenomena such as exponential decay, saturation effects, dead time, and precarious dependence on initial conditions, may be addressed by a number of modern computational techniques. Among these are the methods of chaotic analysis and declarative reasoning in conjunction with proven techniques such as Markov modeling and statistical forecasting. The integrated approach provides a systematic methodology for predicting chaotic signals despite environmental uncertainties and internal disturbances. A predictive system for chaotic processes is applicable to many sectors. The applications range from mobile robots to manufacturing plants; from navigation in ships to resource management in communication networks; and from biomedical signal processing to economic forecasting.     

Agent-based intelligent system development for decision support in chemical process industry

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.     

A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

current investigation focused on neural-network-based control of manufacturing processes utilizing an optimization scheme. In an earlier study, Demirci and Coulter introduced the utilization of neural networks for the intelligent control of molding processes. In that study, a forward model neural network, employed with a search strategy based on the factorial design of experiments method, was shown to successfully control the flow progression during injection molding processes. Recently, Demirciet al. showed that the search mechanism based on the factorial design of experiments method can be intolerable in time during on-line control of manufacturing processes, and suggested an inverse model neural network. This inverse model neural network was shown to be beneficial as it totally eliminated time-consuming parameter searches, but it required a harder mapping than the forward model neural network and thus its performance was inferior. In the present study, the authors investigated two different optimization methods that were utilized in making the search method of the forward control scheme more efficient. The first method was Taguchi's method of parameter design, and the second method was a nonlinear optimization method known as Nelder and Mead's downhill simplex method. These two methods were separately utilized in creating an efficient search method to be used with the forward model neural network. The performance of the resulting two control methods was compared with each other as well as with that of the forward control scheme utilizing a search strategy based on the factorial design of experiments method. Although the applications in this study were on molding processes, the method can be applied to any manufacturing process for which a process model and anin-situ sensing scheme exists.     

Implicit knowledge and fault diagnosis in the control of advanced manufacturing technology

. Field studies have shown that increasing operator responsibility for running advanced manufacturing technology can substantially enhance system performance. Improved fault diagnosis is central to such performance gains, and observations suggest this depends on implicit as well as explicit knowledge. However, the question of whether this is the case has not been systematically investigated. Evidence from field settings is circumstantial, and laboratory investigations of implicit knowledge have been based on other types of task. In this paper we described a study of implicit knowledge in fault diagnosis based on laboratory simulation of a robotics line. This confirms the existence of implicit knowledge in fault diagnosis, as well as raising both conceptual and methodological issues relevant to experimental approaches. We discuss the implications of the study for organizational practice and for the interaction between cognitive and organizational psychology.     

Implicit knowledge and fault diagnosis in the control of advanced manufacturing technology

Abstract

Field studies have shown that increasing operator responsibility for running advanced manufacturing technology can substantially enhance system performance. Improved fault diagnosis is central to such performance gains, and observations suggest this depends on implicit as well as explicit knowledge. However, the question of whether this is the case has not been systematically investigated. Evidence from field settings is circumstantial, and laboratory investigations of implicit knowledge have been based on other types of task. In this paper we described a study of implicit knowledge in fault diagnosis based on laboratory simulation of a robotics line. This confirms the existence of implicit knowledge in fault diagnosis, as well as raising both conceptual and methodological issues relevant to experimental approaches. We discuss the implications of the study for organizational practice and for the interaction between cognitive and organizational psychology.     

An exploratory study of chaos in human-Machine system dynamics

The human-machine system behavior and performance are dynamic, nonlinear, and possibly chaotic. Various techniques have been used to describe such dynamic and nonlinear system characteristics. However, these techniques have rarely been able to accommodate the chaotic behavior of such a nonlinear system. Therefore, this study proposes the use of nonlinear dynamic system theory as one possible technique to account for the dynamic, nonlinear, and possibly chaotic human-machine system characteristics. It briefly describes some of the available nonlinear dynamic system techniques and illustrates how their application can explain various properties of the human-machine system. A pilot's heart interbeat interval (IBI) and altitude tracking error time series data are used in the illustration. Further, the possible applications of the theory in various domains of human factors for on-line assessment, short-term prediction, and control of human-machine system behavior and performance are discussed.     

Classification knowledge discovery in mold tooling test using decision tree algorithm

The scale of Taiwan’s mold industry was ranked the sixth in the world. But, under the global competitive pressure, Taiwan has lost its competitive advantage gradually. The only chance of Taiwan’s mold industry lies in improving the competitive abilities in product research, development and design. In mold manufacturing cycle, mold tooling test plays a very important role at accelerating the speed of production. An experienced engineer can minimize the error rate of mold tooling test according to his rich experiences in parameter adjustment. However, this experience is mostly implicit without theoretical basis and its knowledge is difficult to be transmitted. Benefiting from the well development of data mining technologies, this study aimed at constructing an intelligent classification knowledge discovery system for mold tooling test based on decision tree algorithm, so as to explore and accumulate the experimental knowledge for the use of Taiwan’s mold industry. This study took the only high-alloy steel manufacturer in Taiwan for case study, and performed system validation with 66 record data. The results showed the accuracy rates of prediction of training data and testing data are 97.6 and 86.9%, respectively. In addition, this study explored two classification knowledge rules and proposed concrete proposals for tooling test parameter adjustment. Moreover, this study provided two ways, rule verification and effectiveness comparison of four mining algorithms, to conduct model verification. The experimental results showed the decision tree algorithm has an excellent discriminatory power of classification and is able to provide clear and simple reference rules for decisions.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions

Recent developments in intelligent manufacturing have validated the use of probabilistic Boolean networks (PBN) to model failures in manufacturing processes and as part of a methodology for Design Failure Mode and Effects Analysis (DFMEA). This paper expands the application of PBNs in manufacturing processes by proposing the use of interventions in PBNs to model an ultrasound welding process in a preventive maintenance (PM) schedule, guiding the process to avoid failure and extend its useful work life. This bio-inspired, stochastic methodology uses PBNs with interventions to model manufacturing processes under a PM schedule and guides the evolution of the network, providing a new mechanism for the study and prediction of the future behavior of the system at the design phase, assessing future performance, and identifying areas to improve design reliability and system resilience. A process engineer designing manufacturing processes may use this methodology to create new or improve existing manufacturing processes, assessing risk associated with them, and providing insight into the possible states, operating modes, and failure modes that can occur. The engineer can also guide the process and avoid states that can result in failure, and design an appropriate PM schedule. The proposed method is applied to an ultrasound welding process. A PBN with interventions model was simulated and verified using model checking in PRISM, generating data required to conduct inferential statistical tests to compare the effects of probability of failures between the PBN and PBN with Interventions models. The obtained results demonstrate the validity of the proposed methodology.     

A knowledge intensive multi-agent framework for cooperative/collaborative design modeling and decision support of assemblies

Multi-agent modeling has emerged as a promising discipline for dealing with decision making process in distributed information system applications. One of such applications is the modeling of distributed design or manufacturing processes which can link up various designs or manufacturing processes to form a virtual consortium on a global basis. This paper proposes a novel knowledge intensive multi-agent cooperative/collaborative framework for concurrent intelligent design and assembly planning, which integrates product design, design for assembly, assembly planning, assembly system design, and assembly simulation subjected to econo-technical evaluations. An AI protocol based method is proposed to facilitate the integration of intelligent agents for assembly design, planning, evaluation and simulation process. A unified class of knowledge intensive Petri nets is defined using the O-O knowledge-based Petri net approach and used as an AI protocol for handling both the integration and the negotiation problems among multi-agents. The detailed cooperative/collaborative mechanism and algorithms are given based on the knowledge objects cooperation formalisms. As such, the assembly-oriented design system can easily be implemented under the multi-agent-based knowledge-intensive Petri net framework with concurrent integration of multiple cooperative knowledge sources and software. Thus, product design and assembly planning can be carried out simultaneously and intelligently in an entirely computer-aided concurrent design and assembly planning system.     

Digital twin-enabled machining process modeling

Considering the new generation of information technology, the digitalization and intellectualization of the machining process have become the major core in intelligent manufacturing. The complex and diverse requirements, as well as the processing sites force the machining sequence to move towards cyber-physical integration. This paper presents a multidimensional modeling approach for machining processes, by introducing Digital Twin (DT) technology. The method is oriented towards the design and execution phases of the machining process and is used to support intelligent machining. The working mechanism of modeling, simulation, prediction and control of machining process is described based on the interpretation of the modeling and application methods of machining process design, inspection process, fault diagnosis and quality prediction, as based on digital twin technology. Finally, key components of diesel engines are targeted as test objects, demonstrating increased material removal rate by 5.1%, reduced deformation by 22.98% and 30.13%, respectively, verifying the effectiveness of the applied framework and the proposed method.     

Evaluating the application of augmented reality devices in manufacturing from a process point of view: An AHP based model

Augmented Reality (AR) systems in last few years show great potentialities in the manufacturing context: recent pilot projects were developed for supporting quicker product and process design, as well as control and maintenance activities. The high technological complexity together with the wide variety of AR devices require a high technological skill; on the other hand, evaluating their actual impacts on the manufacturing process is still an open question. Few recent studies have analysed this topic by using qualitative approaches based on an “ex post” analysis – i.e. after the design and/or the adoption of the AR system - for evaluating the effectiveness of a developed AR application. The paper proposes an expert based tool for supporting production managers and researchers in effectively evaluating a preliminary ex-ante feasibility analysis for assessing quantitatively most efficient single AR devices (or combinations) to be applied in specific manufacturing processes. A multi-criteria model based on Analytic Hierarchy Process (AHP) method has been proposed to provide decision makers with quantitative knowledge for more efficiently designing AR applications in manufacturing. The model allows to integrate, in the same decision support tool, technical knowledge regarding AR devices with critical process features characterizing manufacturing processes, thus allowing to assess the contribution of the AR device in a wider prospective compared to current technological analyses. A test case study about the evaluation of AR system in on-site maintenance service is also discussed aiming to validate the model, and to outline its global applicability and potentialities. Obtained results highlighted the full efficacy of the proposed model in supporting ex-ante feasibility studies.     

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behavior which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model     

Intelligent process control using neural fuzzy techniques

In this paper, we combine the advantages of fuzzy logic and neural network techniques to develop an intelligent control system for processes having complex, unknown and uncertain dynamics. In the proposed scheme, a neural fuzzy controller (NFC), which is constructed by an equivalent four-layer connectionist network, is adopted as the process feedback controller. With a derived learning algorithm, the NFC is able to learn to control a process adaptively by updating the fuzzy rules and the membership functions. To identify the input–output dynamic behavior of an unknown plant and therefore give a reference signal to the NFC, a shape-tunable neural network with an error back-propagation algorithm is implemented. As a case study, we implemented the proposed algorithm to the direct adaptive control of an open-loop unstable nonlinear CSTR. Some important issues were studied extensively. Simulation comparison with a conventional static fuzzy controller was also performed. Extensive simulation results show that the proposed scheme appears to be a promising approach to the intelligent control of complex and unknown plants, which is directly operational and does not require any a priori system information.