Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Automatic Generation of Controllers for Collision-Free Flexible Manufacturing Systems

A method for automatic generation of non-blocking controllers that generate collision-free flexible manufacturing cells is presented in this paper. Today, industry demands on flexible production sometimes require significant changes in location, orientation and configuration of industrial robots and other moving devices, when new products are introduced. All these changes pose a threat to the devices to collide while sharing workspace. To avoid this, a formal model of the operations in a manufacturing system is generated, and for each operation state a corresponding 3D simulation shape is created. A collision-free system is then achieved by considering pairs of colliding shapes as forbidden states. The automatic generation also includes a synthesis procedure, where a non-blocking and controllable supervisor is generated based on guard generation. The guards are computed by binary decision diagrams, which means that complex systems can be handled, still generating comprehensible restrictions that are easily included in PLC-code

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Methodologies for CIM systems integration in small batch manufacturing

This thesis is concerned with identifying the problems and constraints faced by small batch manufacturing companies during the implementation of Computer Integrated Manufacturing (CIM). The main aim of this work is to recommend generic solutions to these problems with particular regard to those constraints arising because of the need for ClM systems integration involving both new and existing systems and procedures. The work has involved the application of modern computer technologies, including suitable hardware and software tools, in an industrial environment. Since the research has been undertaken with particular emphasis on the industrial implementor's viewpoint, it is supported by the results of a two phased implementation of computer based control systems within the machine shop of a manufacturing company. This involved the specific implementation of a Distributed Numerical Control system on a single machine in a group technology cell of machines followed by the evolution of this system into Cell and Machine Management Systems to provide a comprehensive decision support and information distribution facility for the foremen and uperators within the cell. The work also required the integration of these systems with existing Factory level manufacturing control and CADCAM functions. Alternative approaches have been investigated which may have been applicable under differing conditions and the implications that this specific work has for CIM systems integration in small batch manufacturing companies evaluated with regard not only to the users within an industrial company but also the systems suppliers external to the company. The work has resulted in certain generic contributions to knowledge by complementing ClM systems integration research with regard to problems encountered; cost implications; the use of appropriate methodologies including the role of emerging international standard methods, tools and technologies and also the importance of 'human integration' when implementing CIM systems in a real industrial situation

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

Automated Verification and Generation of Flexible Automation Control

Consumer product life-cycles are constantly shortening; the automotive industry is an illustrative example. As a consequence, the introduction of new products into the manufacturing system necessarily becomes more frequent. Inherently, this brings a performance reduction for the manufacturing system. The reduced performance is caused by a down-time and a ramp-up-time. During the down-time the mechanical equipment is rebuilt and the new control programs are debugged. During ramp-up there are a large number of errors mainly caused by mechanical devices not being properly adjusted, bugs in the control programs and operators not used to new procedures. Thus, in order to maintain the productivity level and to achieve full cost-efficiency both the down-time and the ramp-up time must be reduced. One way to reduce these lead times is to verify the control programs in offline mode. However, efficient and reliable offline verification requires some major improvements of the current development process of manufacturing systems. Information handling and development of control programs based on information reuse are the two most important improvement areas.The work presented here addresses four industrial problems related to this, lack of tools for offline verification of control programs, lack of information reuse in the development process of a manufacturing system, lack of operator support in error situations, and lack of tools for analyzing the control of complex manufacturing cells.We propose a development method where information from different tools in the development process of a manufacturing system is reused and processed by tools for verification and optimization. Then the control programs are generated by combining the processed information with a library of standardized software components. The proposed method solves the above-mentioned industrial problems without adding work to the development process. On the contrary, the amount of work will be reduced since the control program development will be automated and the time for debugging the control programs on the shop floor will be drastically reduced, due to the new mathematically based verification process