Generic Design Methodology for Smart Manufacturing Systems From a Practical Perspective. Part II—Systematic Designs of Smart Manufacturing Systems

In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part

Measuring the Overall Complexity of Graphical and Textual IEC 61131-3 Control Software

Software implements a significant proportion of functionality in factory automation. Thus, efficient development and the reuse of software parts, so-called units, enhance competitiveness. Thereby, complex control software units are more difficult to understand, leading to increased development, testing and maintenance costs. However, measuring complexity is challenging due to many different, subjective views on the topic. This paper compares different complexity definitions from literature and considers with a qualitative questionnaire study the complexity perception of domain experts, who confirm the importance of objective measures to compare complexity. The paper proposes a set of metrics that measure various classes of software complexity to identify the most complex software units as a prerequisite for refactoring. The metrics include complexity caused by size, data structure, control flow, information flow and lexical structure. Unlike most literature approaches, the metrics are compliant with graphical and textual languages from the IEC 61131-3 standard. Further, a concept for interpreting the metric results is presented. A comprehensive evaluation with industrial software from two German plant manufacturers validates the metrics' suitability to measure complexity.Comment: 8 pages, https://ieeexplore.ieee.org/abstract/document/9444196

EFFICIENCY OF FLEXIBLE FIXTURES: DESIGN AND CONTROL

The manufacturing industries have been using flexible production technologies to meet the demand for customisation. As a part of production, fixtures have remained limited to dedicated technologies, even though numerous flexible fixtures have been studied and proposed by both academia and industry. The integration of flexible fixtures has shown that such efforts did not yield the anticipated performance and resulted in inefficiencies of cost and time. The fundamental formulation of this thesis addresses this issue and aims to increase the efficiency of flexible fixtures.To realise this aim, the research in this thesis poses three research questions. The first research question investigates the efficiency description of flexible fixtures in terms of the criteria. Relative to this, the second research question investigates the use of efficiency metrics to integrate efficiency criteria into a design procedure. Once the efficiency and design aspects have been established, the third research question investigates the active control of flexible fixtures to increase their efficiency. The results of this thesis derive from the outcome of seven studies investigating the automotive and aerospace industries. The results that answer the first research question use five criteria to establish the efficiency of flexible fixtures. These are: fundamental, flexibility, cost, time and quality. By incorporating design characteristics in respect of production system paradigms, each criterion is elaborated upon using relevant sub-criteria and metrics. Moreover, a comparative design procedure is presented for the second research question and comprising four stages (including mechanical, control and software aspects). Initially, the design procedure proposes conceptual design and verification stages to determine the most promising flexible fixture for a target production system. By executing detailed design and verification, the design procedure enables a fixture designer to finalise the flexible fixture and determine its efficiency. Furthermore, a novel parallel kinematics machine is presented to demonstrate the applicability of the design procedure’s analytical steps and illustrate how appropriate kinematic structures can facilitate the efficiency-orientated design of flexible fixtures.Based on the correlation established by the controller software’s design procedure, the active control of flexible fixtures directly affects the quality criterion of flexible fixture efficiency. This provides the answer to the third research question, on general control strategies for active control of flexible fixtures. The introduction of a system model and manipulator dynamics proposes force and position control strategies. It is shown that any flexible fixture using a kinematic class can be controlled, to regulate the force and position of a workpiece and ensure that process nominals are preserved. Moreover, using both direct and indirect force control strategies, a flexible fixture’s role in active control can be expanded into a system of actively controlled fixtures that are useful in various processes. Finally, a position controller is presented which has the capacity to regulate both periodic and non-periodic signals. This controller uses an additional feedforward scheme (based on the Hilbert transform) in parallel with a feedback mechanism. Thus, the position controller enables flexible fixtures to regulate the position of a workpiece in respect of any kind of disturbance

Development of a reconfigurable assembly system with an integrated information management system

Thesis (M. Tech. (Engineering Electrical)) -- Central University of Technology, Free State, [2014]This dissertation evaluates the software and hardware components used to develop a Reconfigurable Assembly System with an Integrated Information Management System. The assembly system consists of a modular Cartesian robot and vision system. The research focuses on the reconfigurability, modularity, scalability and flexibility that can be achieved in terms of the software and hardware components used within the system. The assembly system can be divided into high-level control and low-level control components. All information related to the product, Cartesian positioning and processes to follow resides in the Information Management System. The Information Management System is the high-level component and consists of a database, web services and low-levelcontrol drivers. The high-level system responds to the data received from the low-level systems and determines the next process to take place. The low-level systems consist of the PLC (Programmable Logic Controller) and the vision system. The PLC controls the Cartesian robot’s motor controllers and handles all events raised by field devices (e g. sensors or push buttons). The vision system contains a number of pre-loaded inspections used to identify barcodes and parts, obtain positioning data and verify the products’ build quality. The Cartesian robot’s positioning data and the vision system’s inspections are controlled by the Information Management System. The results showed that the high-level control software components are able to add more modularity and reconfigurability to the system, as it can easily adapt to changes in the product. The high-level control components also have the ability to be reconfigured while the assembly system is online without affecting the assembly system. The low-level control system is better suited to handling the control of motor controllers, field devices and vision inspections over an industrial network

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool

Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design

In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model cause-effect relationship between faults and sensors. Analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties between sensors and faults into single edge values in fuzzy graph, thus quantitatively determining the fault detectability. An appropriate multiple objective model was set up to minimize fault unobservability and cost while achieving required detectability performance. Lexicographical mixed integer linear programming and greedy search were respectively used to optimize the model, thus assigning the sensors to faults. Second, a diagnoser based on real time fuzzy Petri net (RTFPN) was proposed to detect faults in discrete manufacturing systems. It used the real time PN to model the manufacturing plant while using fuzzy PN to isolate the faults. It has the capability of handling uncertainties and including industry knowledge to diagnose faults. The proposed approach was implemented using Visual Basic, and tested as well as validated on a dual robot arm. Finally, the proposed sensor deployment approach and diagnoser were comprehensively evaluated based on design of experiment techniques. Two-stage statistical analysis including analysis of variance (ANOVA) and least significance difference (LSD) were conducted to evaluate the diagnosis performance including positive detection rate, false alarm, accuracy and detect delay. It illustrated the proposed approaches have better performance on those evaluation metrics. The major contributions of this research include the following aspects: (1) a novel fuzzy quantitative graph based sensor deployment approach handling sensor heterogeneity, and optimizing multiple objectives based on lexicographical integer linear programming and greedy algorithm, respectively. A case study on a five tank system showed that system detectability was improved from the approach of signed directed graph's 0.62 to the proposed approach's 0.70. The other case study on a dual robot arm also show improvement on system's detectability improved from the approach of signed directed graph's 0.61 to the proposed approach's 0.65. (2) A novel real time fuzzy Petri net diagnoser was used to remedy nonsynchronization and integrate useful but incomplete knowledge for diagnosis purpose. The third case study on a dual robot arm shows that the diagnoser can achieve a high detection accuracy of 93% and maximum detection delay of eight steps. (3) The comprehensive evaluation approach can be referenced by other diagnosis systems' design, optimization and evaluation

A component-based virtual engineering approach to PLC code generation for automation systems

In recent years, the automotive industry has been significantly affected by a number of challenges driven by globalisation, economic fluctuations, environmental awareness and rapid technological developments. As a consequence, product lifecycles are shortening and customer demands are becoming more diverse. To survive in such a business environment, manufacturers are striving to find a costeffective solution for fast and efficient development and reconfiguration of manufacturing systems to satisfy the needs of changing markets without losses in production. Production systems within automotive industry are vastly automated and heavily rely on PLC-based control systems. It has been established that one of the major obstacles in realising reconfigurable manufacturing systems is the fragmented engineering approach to implement control systems. Control engineering starts at a very late stage in the overall system engineering process and remains highly isolated from the mechanical design and build of the system. During this stage, control code is typically written manually in vendor-specific tools in a combination of IEC 61131-3 languages. Writing control code is a complex, time consuming and error-prone process. [Continues.

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Development of a reconfigurable assembly system with enhanced control capabilities and virtual commissioning

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013The South African (SA) manufacturing industry requires developing similar levels of sophistication and expertise in automation as its international rivals to compete for global markets. To achieve this, manufacturing plants need to be managed extremely efficiently to ensure the quality of manufactured products and these plants must also have the relevant infrastructure. Furthermore, this industry must also compensate for rapid product introduction, product changes and short product lifespan. To support this need, this industry must engage in the current trend in automation known as reconfigurable manufacturing. The aim of the study is to develop a reconfigurable assembly system with enhanced control capabilities by utilizing virtual commissioning. In addition, this system must be capable of assembling multiple different products of a product range; reconfigure to accommodate the requirements of these products; autonomously reroute the product flow and distribute workload among assembly cells; handle erroneous products; and implement enhanced control methods. To achieve this, a literature study was done to confirm the type of components to be used, reveal design issues and what characteristics such a system must adhere to. Software named DELMIA was used to create a virtual simulation environment to verify the system and simultaneously scrutinize the methods of verification. On completion, simulations were conducted to verify software functions, device movements and operations, and the control software of the system. Based on simulation results, the physical system was built, and then verified with a multi agent system as overhead control to validate the entire system. The final results showed that the project objectives are achievable and it was also found that DELMIA is an excellent tool for system verification and will expedite the design of a system. By obtaining these results it is indicated that companies can design and verify their systems earlier through virtual commissioning. In addition, their systems will be more flexible, new products or product changes can be introduced more frequently, with minimum cost and downtime. This will enable SA manufacturing companies to be more competitive, ensure increased productivity, save time and so ensure them an advantage over their international competition