Högskolan i Skövde

The ability to adaptively control manufacturing equipment in cloud environments is becoming increasingly more important. Industry 4.0, supported by Cyber Physical Systems and the concept of on-demand, scalable and pay-for-usage resource-sharing in cloud environments offers many promises regarding effective and flexible manufacturing. For implementing the concept of manufacturing services in a cloud environment, a cloud control approach for the sharing and control of networked manufacturing resources is required. This paper presents a cloud service-based control approach which has a product perspective and builds on the combination of event-driven IEC 61499 Function Blocks and product manufacturing features. Distributed control is realised through the use of a networked control structure of such Function Blocks as decision modules, enabling an adaptive run-time behaviour. The control approach has been developed and implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. An application scenario is presented to demonstrate the applicability of the control approach. In this scenario, Assembly Feature-Function Blocks for adaptive control of robotic assembly tasks have been used.

In this paper, knowledge transfer is defined as a process of disseminating both technological and theoretical understanding as well as enhancing both industrial and academic knowledge through conducted research to project partners collaborating within a research project. To achieve this, a new methodology called 'user groups' is introduced. It facilitates knowledge transfer between project participants in collaborative research programs engaging both experienced and unexperienced partners regardless of level of input. The introduced methodology 'user groups' provides tools for collaborating with several research partners even though their levels of engagement in the project and prior research experience may vary without dividing them into separate groups. It enables all project partners to gain new knowledge and by so doing extending the knowledge society. The case study shows that the eight engaged companies are able to cooperate, achieve their own objectives and, both jointly and individually, contribute to the overall project goals.

This paper presents a novel system using Augmented Reality and Expert Systems to enhance the quality and efficiency of shop-floor operators. The novel system proposed provides an adaptive tool that facilitates and enhances support on the shop-floor, due to its ability to dynamically customize the instructions displayed, dependent upon the competence of the user. A comparative study has been made between an existing method of quality control instructions at a machining line in an automotive engine plant and this novel system. It has been shown that the new approach outcompetes the existing system, not only in terms of perceived usability but also with respect to two other important shop-floor variables: quality and productivity. Along with previous research, the outcomes of these test cases indicate the value of using Augmented Reality technology to enhance shop-floor operators’ ability to learn and master new tasks.

There is an on-going paradigm shift in manufacturing, in which modern manufacturing industry is changing towards global manufacturing networks and supply chains. This will lead to the flexible usage of different globally distributed, scalable and sustainable, service-oriented manufacturing systems and resources. Combining recently emerged technologies, such as Internet of Things, Cloud Computing, Semantic Web, service-oriented technologies, virtualisation and advanced high-performance computing technologies, with advanced manufacturing models and information technologies, Cloud Manufacturing is a new manufacturing paradigm built on resource sharing, supporting and driving this change.

It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities and equipment, will then be made accessible to presumptive consumers on a worldwide basis.

Cloud Manufacturing has been in focus for a great deal of research interest and suggested applications during recent years, by both industrial and academic communities. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with identified outstanding research issues, and future trends and directions within Cloud Manufacturing.

Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite.

In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.

The increasing globalization is a trend which forces manufacturing industry of today to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing (CM) 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. Providing a framework for collaboration within complex and critical tasks, such as manufacturing and design, it increases the companies’ ability to successfully compete on a global marketplace. One of the major, crucial objectives for CM is the coordinated planning, control and execution of discrete manufacturing operations in a collaborative and networked environment. This paper describes the overall concept of adaptive Function Block control of manufacturing equipment in Cloud environments, with the specific focus on robotic assembly operations, and presents Cloud Robotics as “Robot Control-as-a-Service” within CM.

Many manufacturing companies are facing an increasing amount of changes and uncertainty, caused by both internal and external factors. Frequently changing customer and market demands lead to variations in manufacturing quantities, product design and shorter product life-cycles, and variations in manufacturing capability and functionality 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. Such events are difficult for traditional planning and control systems to satisfactorily manage. For scenarios like these, with a dynamically changing manufacturing environment, adaptive decision making is crucial for successfully performing manufacturing operations. Relying on real-time information of manufacturing processes and operations, and their enabling resources, adaptive decision making can be realized with a control approach combining IEC 61499 event-driven Function Blocks (FBs) with manufacturing features. These FBs are small decision-making modules with embedded algorithms designed to generate the desired equipment control code. When dynamically triggered by event inputs, parameter values in their data inputs are forwarded to the appropriate algorithms, which generate new events and data output as control instructions. The data inputs also include monitored real-time information which allows the dynamic creation of equipment control code adapted to the actual run-time conditions on the shop-floor. Manufacturing features build on the concept that a manufacturing task can be broken down into a sequence of minor basic operations, in this research assembly features (AFs). These features define atomic assembly operations, and by combining and implementing these in the event-driven FB embedded algorithms, automatic code generation is possible. A test case with a virtual robot assembly cell is presented, demonstrating the functionality of the proposed control approach.

This paper is based on a study in which production and HR managers at six Swedish manufacturing industries have been interviewed about the role of the shop-floor operator, taking off in today’s situation in trying to identify the future one. As well as the production methods and the machines etc. in the production system continuously evolve, so does the environment of the shop-floor operator. Increasing complexity in the production systems raises demands on the operators’ ability to handle ICT-tools to gain decision support and knowledge needed in the future shop-floor environment. 

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.