Minna Lanz

Process planning based on feature recognition method

Managing and controlling very complex manufacturing systems and vast volumes of accumulate knowledge, holonic manufacturing system is developed. This paper introduces a method, which utilizes feature-based modeling for defining a pre-process plan. The pre-process plan developed can be linked in a holonic system. This paper, the generic steps for making a product are called “pre-process plan”. The pre-process plan defines the required capabilities on a high level. All the resources have some sort of capability that represents the possible candidates for the product manufacturing. The feature recognition method offers geometric and non-geometric (such as shape, type, tolerance and material) information. Using feature information a pre-process plan can be defined. The fact that the pre-process plan does not strictly define the used processing methods allows the product to be manufactured on different machines based on their availability or other criteria. This is important in dynamic, adaptive production environment.

Presenting capabilities of resources and resource combinations to support production system adaptation

Todays turbulent production environment calls for adaptive and rapidly responding production systems that can adjust to required changes both in production capacity and processing functions. This kind of adaptivity can not be reached without intelligent methods and tools supporting the adaptation planning and deployment of the systems. This paper introduces novel method to present and manage capabilities of production resources and combined capabilities of multiple co-operating resources. This modeling approach enables matching of products and resources based on their required and provided capabilities and this way supports rapid allocation of resources.

Formal resource and capability descriptions supporting rapid reconfiguration of assembly systems

Todays production environment is characterised by frequent changes in terms of high product variation, small batch sizes, high demand fluctuation as well as random unexpected disturbances on the factory floor. Production systems need to be rapidly reconfigurable and adaptable to these changing requirements. ReCaM project targets to develop a set of integrated tools for rapid and autonomous reconfiguration of production systems. Such tools need to be supported by formal information models describing the product requirements, as well as resource characteristics and functionalities. This paper concentrates on introducing the formal resource and capability models, which are used and further enriched to support ReCaM targets. Also examples of how these models can be applied to support rapid reconfiguration will be given.

Product-Process Ontology for Managing Assembly Specific Knowledge Between Product Design and Assembly System Simulation

The aim of the present paper is to introduce a feature-based Product-Process-System model and ontology in order to retrieve and share knowledge for simulation of manufacturing systems. Product knowledge is the combination of product specific information, such as functionality, colour and product variants, and the corresponding product model. The model provides understanding of the product structure, rules, constraints and assembly-specific information in relation to the product model. In the design and modeling of assembly processes, features form the foundation for analysis and knowledge acquisition of the product. This knowledge includes geometric and non-geometric information. In the present paper, an approach is proposed to share platform independent product-process knowledge between the assembly process and system design and even with the simulation environment.

Neutral Interface for Assembly and Manufacturing Related Knowledge Exchange in Heterogeneous Design Environment

The goals of this research are to provide overview of the recent activities in the field of design of manufacturing and assembly processes from the knowledge share point of view and propose a solution how to connect products to the manufacturing or assembly processes and suitable systems. The research conducted here starts with the assumption of connectivity of three design domains: product, process and system design domains. The characteristics of the product are pre-describing the set of the processes needed to manufacture and/or assemble the product. The description of the product defines constraints for the suitable processes. The processes are pre-defining the system requirements and constraining the set of systems capable of carrying out the needed processes. From the technical point of view the goal is also to provide an information architecture, where the relations exist between these three domains.

Dynamic operation environment — Towards intelligent adaptive production systems

Todays turbulent production environment calls for adaptive and rapidly responding production systems that can adjust to required changes in products, production volumes and unexpected failure situations. Holonic manufacturing systems aim to offer a solution for changeability requirements by providing self-organizing capabilities. This paper presents a concept of a holonic manufacturing framework and its implementation into a laboratory environment. The adaptivity of the presented holonic system rests on SOA-based communication and negotiation between entities through open interfaces, and matching of resource capabilities against product requirements.

Formal Information Model for Representing Production Resources

This paper introduces a concept and associated descriptions to formally describe physical production resources for modular and reconfigurable production systems. These descriptions are source of formal information for (automatic) production system design and (re-)configuration. They can be further utilized during the system deployment and execution. The proposed concept and the underlying formal resource description model is composed of three different description levels, namely Abstract Resource Description (ARD), Resource Description (RD) and Resource Instance Description (RID), each having different scope and objectives. This paper discusses in details the content and differences between these description levels.

Information Flows in Future Advanced Manufacturing Ecosystems

Manufacturing is the backbone of each and every society, and in order for society to sustain in long run the manufacturing has to be sustainable as well. The sustainability in the field of manufacturing has traditionally been discussed in a sense of operational efficiency and environmental metrics. Rarely the link between individuals working in the company and the efficiency of operations has been established and discussed deeply. This link is information flow that combines both tacit and formal information in a dynamically changing socio-technical environment. In this paper the information flow between individuals in different levels of company hierarchy is utilized as the observation baseline. This paper discusses the information flow within a company and outlines socio-technical challenges needed to solve in order to realize future Advanced Manufacturing Ecosystems.

Collaborative Systems and Environments for Future Working Life: Towards the Integration of Workers, Systems and Manufacturing Environments

While the industrial sector in Europe was previously strongly based on mass production technology, it is now moving towards highly customised products and thus to lot-size-one production. The change in production paradigm is strengthened by the emerging technologies. In small- and medium-sized enterprises (SMEs), this means, for example, the increased use of modern digital manufacturing tools, new additive manufacturing processes and novel engineering intelligence solutions. As a direct result, workers need to develop new skills and competences to effective work. From an educational perspective, it is especially critical that people with few prior successful experiences with fully applying the key information-processing skills need to obtain adequate comprehension to guide them in structural changes in their future working lives. In this chapter, we will discuss the critical points of adults’ skills based on the PIAAC large-scale assessment results and illustrate novel educational approaches to meet the emerging needs on the digitalisation of work. Based on these critical points, we will illuminate two learning approaches that can guide educational efforts in designing ‘future’ learning at manufacturing sector. Our first approach is a pedagogical evidence-based physical and virtual learning environment that is based on learning in a reality-conform production environment. In our second approach, we will illustrate a simulation-based learning environment that is designed to increase the understanding of complex machine systems.

Engineering intelligence - Product-service concepts and requirements in industry

European manufacturing and construction sector is wrestling with the fundamental and rapid changes in their business environment. Business environment is becoming more dynamic and distributed, thus old standardization and mass-customization methods can no longer support the companies. In order to survive the companies must be able to offer fully customized product-service concepts instead of technical solutions. This causes challenges relating to efficiency of collaboration, utilization of information flows, agility and interoperability of technical solutions and operation culture. The products, processes and services need to be designed “for humans by humans”. The competence development methods and enhanced learning have to be comprehensively taken into account. This paper summarizes the challenges among industry and provides a new approach Engineering Intelligence Ecosystem (EIE) that addresses aforementioned challenges of complex engineering systems.