Engineering Methods and Tools for Collaborative ...

Engineering Methods and Tools for Collaborative Development of Industrial Cyber-Physical Based Products and Services

Dragan Stokic, Sebastian Scholze

Christian Decker, Karsten Stöbener

Institute for Applied System Technology, ATB-Bremen Bremen, Germany dragan@atb-bremen,de

KLÖCKNER DESMA Schuhmaschinen GmbH Achim, Germany

Abstract— The objective of the research is to provide a novel methodology and a set of ICT solutions for collaborative design of cyber-physical based product-services. The products which include cyber-physical features, such as Ambience Intelligence (AmI) features, have to be extended with new services. Development of such products and services requires collaborative work of various stakeholders. A Cloud Manufacturing approach is applied for effective collaborative design of product-services, and the effective implementation of innovative services. It involves all the actors of a value chain, within a product ecosystem, allowing manufacturers to strengthen their competitiveness at the global market. The development platform is to be provided, including a set of new engineering tools, such as a tool for selection /use of AmI solutions for building services around the products, tools for embedding context sensitivity into products and services, simulation, configuration of product-services etc. The research is driven by industrial application scenarios addressing different aspects of service and business building as well as products development in different sectors (automotive, home appliances, automation equipment etc.).In this paper the focus is on an application scenario in automation equipment industry. Keywords—Collaborative design; Product-services; Engineering tools; Cyber Physical Products; Ambience Intelligence

I. INTRODUCTION Manufacturing industry needs to move towards a new concept of products [1] which include cyber-physical features, such as Ambience Intelligence (AmI) features (see e.g. [2]), which allows for extending of their products with different novel, personalized and context sensitive product and customer support services. Such Product Extension Services (PES) are needed in wide range of applications: e.g. services to support adaptation of the shoe design to personal needs, services to support users of machines in the maintenance activities, condition based maintenance of household appliances etc., and to be able to cost effectively provide these services to customers distributed world-wide. The research presented in this paper intends to enable the manufacturing companies to enter in a continuous process of upgrading their products along their life cycle under the frame of adding cyber-physical features and establishing/further enhancing Product Service System (PSS) model. A PSS is a function-oriented business model aimed at the provision of sustainability (i.e. minimization of environmental impact) of both consumption and production patterns. Many (if not all) services to extend products can be effectively facilitated by ICT solutions (SW services) what is a key approach applied

within the research presented. However, a new strategy is required in order to stimulate the change in current production and consumption patterns. This can be achieved by changing technology and applying best up to date technologies. The enhancing of PSS approach requires collaborative development of the products with cyber-physical features, where different actors along the value chain have to be involved. On the other hand, services made based on such cyber-physical features have to be adaptable to specific users’ needs and context under which they are using the product/service, i.e. they need to be context sensitive. Therefore, the assumption is that cyber – physical features, such as AmI technology, as well as ICT supported collaborative environments and ICT based context sensitive services are such technologies to allow for effective PSS concept. The objective of the research is to provide a set of tools for effective engineering of product-services based on their cyber-physical features. The research presented is a part of a wider initiative where different tools such as eco – driven design, simulation, configuration of the product-services etc. are addressed. In this paper the focus is upon the tools to support application of AmI technology and context sensitive approach for PES within collaborative product-service design based on Cloud Manufacturing (CMfg) technology. The research is driven by four industrial applications in different sectors. In this paper the focus is upon the application in automation equipment for shoe manufacturing industry. II. RELATED WORK PSS within the Product Ecosystem: There are various definitions of PSS. For example, [1] defined PSS as a marketable set of product and services capable of jointly fulfilling a user’s needs, whereas Mont (in [2]) defines PSS as a system of products, services, networks of players and supporting infrastructure that continuously strives to be competitive, satisfy customer needs and have a lower environmental impact than traditional business models. Moreover, it is concluded that PSS generally includes characteristics such as a set of combination of tangible product(s) and (in)tangible service(s); the fulfilment of user needs and user-satisfaction; competitiveness; reduced environmental impact; an innovation strategy; an integrated approach; stakeholder involvement; radical shift of behavior from consumption (selling product) to use (selling function) [3]. For the purpose of this research, PSS is defined as a new business strategy based on condition monitoring, diagnosis and

prognosis of complex equipment in order to support best-best practice asset management. The PSS could be defined as increased offering by the company to the customer to include combinations of goods, services, support, self-service and knowledge in order to add value to core product offerings [4]. Context Awareness and Context modelling for PES: Context Awareness is a concept propagated in the domains of AmI and ubiquitous computing. Existing research on context can be classified in two categories: context-based, proactive delivery of knowledge, and the capture & utilization of contextual knowledge. In the case of embedded services the notion of context refers to process preferences of products and process skills of devices, physical capabilities of the equipment and environment conditions. The modelling of context in this case presents an additional challenge, as the mentioned services are highly dynamic and reside in distributed environments [5]. Application of context awareness PES has not yet been sufficiently researched. AmI based monitoring: Ambient Intelligence refers to electronic environments that are aware of and responsive to the presence of people. With a focus on manufacturing industry, a definition of AmI and reference architecture for control systems of devices/processes in manufacturing industry, aiming at a unified representation of essential control features of these devices is provided in [6]. The full application of AmI in industry and various products is still to be achieved within the next years. From the industrial perspective, a less humanand more system-centered definition of AmI is considered. However, modern manufacturing concepts turn to humancentered approaches. Many RTD issues still have to be solved in order to bring the AmI technology to industrial sectors [7], such as robust, reliable (wireless) sensors and contextsensitivity, intelligent user interfaces, safety, security etc. Collaborative PSS design: One key area of research comes from generic software frameworks which allow the developers to integrate services for creating specific industry solutions. Some previous and current approaches in this area include the Common Object Request Broker Architecture (CORBA), Grid computing infrastructure (e.g. Globus, Legion and SNIPE) and the Common Component Architecture (CCA). In the research presented in this paper, computing platform operates in a distributed fashion over an Internet-based infrastructure comprising a large number of systems and services, although it could be set up on just a single server as well. It facilitates adoption of the underlying Distributed Software Architecture services by creation of a service platform, based on several reference implementations of Service Frameworks. Over the last decade, we have seen various forms of tools for supporting collaboration across distance. In EU and USA there has been much research work to develop distributed virtual environments such as Bamboo, Avango, CAVERNSoft, VRJuggler, DIVE, Lightning and COVISE. While these initiatives and other international projects have made advances in the area of collaborative virtual environment, much work needs to be done to develop and deploy collaborative workspaces in real engineering settings that can take a whole view of the product life cycle and intensive collaborative

engagement of all the stakeholders that could lead to products and services and related production processes [8]. Collaborative Systems in the Engineering Domain: Many leading CAD industries are now offering collaboration functionality into their CAD / PLM products/services in some form. The basic motivation for this development is to enable remote teams who are using CAD / PLM products and services for their own product to come together to work collaboratively to reduce time and cost in product design and development while increasing quality. This shows that all the major CAD/PLM vendors are now providing some form of collaboration capabilities of their products. However, one of the key limitations of these systems is that these collaborations are supported around their products mainly for detailed CAD design [8]. Cloud Manufacturing: Cloud Computing [9] emerges as the latest computing paradigm that promises flexible IT architectures, configurable software services, and QoS guaranteed service environments. The value of implementing Cloud computing in manufacturing environment was analyzed in [10]. A Cloud-based networked manufacturing model, named Cloud Manufacturing (CMfg), was presented in [11]. The trust and security issue plays a critical role in the Cloud environment. Although it is hard to establish a trust serviceoriented grid architecture because of the lack of supporting user single sign-on and dynamic transient service, there are many approaches attempting to solve this problem [12] [13]. Cloud computing enables a new business model that supports ondemand, pay-for-use, and economies-of-scale IT services over the Internet [14]. However, a lack of trust between Cloud users and providers has hindered the universal acceptance of Clouds as outsourced computing services. What is fundamental to understand is that CMfg is much more than just store and retrieving data using services. Service Oriented Architectures (SOA): Service-orientation is still one of the most promising architectural designs for rapid integration of data and business processes. Backed by a matured and universally accepted set of interoperability standards (e.g. HTTP, JSON, XML, SOAP, UDDI, WSDL, WS-* standards) for building, describing, cataloguing and managing reusable services, service orientation is the foundational architecture for today’s mash-ups, software as a service and cloud computing. These standards give service oriented architectures a distinct advantage over other architectural styles, since it makes interoperability one of its intrinsic characteristics, which eases the integrations of heterogeneous systems and provides a major enhancement in business agility. Although a significant share of the research in SOA focused on modelling and supporting inter enterprise relationships, there is a favorable convergence of factors that are rendering it attractive in the establishment of automated networks of devices. The objective is to obtain a universal, comprehensive information layer. In fact, there is a strategic demand for SOA systems capable of realizing the vision of 'any content, anytime, anywhere, any platform' [15, 16].

Fig. 1. Collaborative Environment for Product-services design and deployment of PES involving various actors

III. PROPOSED ARCHITECTURE AND TOOLS The research aims to provide a means for collaborative product-service design which includes [17]:  A new Collaborative Eco-Innovating Design Methodology to effectively take into account ecorelevant issues using eco-design principles through Life Cycle Assessment (LCA) techniques and AmI technology to be integrated already in the conceptual system design phase of products and services where lean product development principles will be applied [18]

 Tools for definition/design of AmI solutions integrated in products and of their use for PES and environmental impact monitoring, as well as tools for definition of ontology based context models needed to achieve context awareness of PES  Eco-design rules using LCA techniques to be taken into account in the design of products and services, utilizing feedback on user behaviors and environmental impact for product/services improvement, as well as Lean principles to support eco-driven concurrent design of products-services (and their production systems).

 SOA based product-service development platform, applying cloud computation approach and including a set of new engineering tools to support collaborative work and enhancing the existing tools for product design

 Configuration tools for supporting the dynamical adaptation of the products - services to the customer needs, allowing for defining variants taking into account design or process restrictions and optimizing the different vectors (economical, time response, environmental impact though all the cycle of the manufacturing process along the whole chain) and facilitating involvement of the different actors in the value chain.

 Knowledge management tools supporting collaborative work within product ecosystem, specifically supporting concurrent (re-)engineering needed to extend products with new services

 A set of so-called core generic services, which can be easily embedded in the products and combined to create/update application specific services, and can be easily adapted to the individual user needs:

 Collaborative environment for cross-sectorial design of product -services (Figure 1) including

 Simulation of products and services and their environmental impact of their use, facilitating virtualization of the product services for CMfg

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AmI based monitoring services based on information obtained from AmI solutions integrated in the Product and processes proving inputs to both context awareness services and environmental monitoring

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Context monitoring services allowing for context awareness within PES and “personalization” of services

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Knowledge provision services (supporting users of the product)

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Environmental (impact) monitoring and optimization services i.e. services for avoiding eco-constraints taking into account a life cycle perspective by covering all phases and integrated environmental impacts along life cycle, including raw material, energy/ water consumption, air pollution, end-of life, etc.

 CMfG based configurable Service Broker serving as mediator for connecting services to be developed, new engineering tools and existing (design) tools for product-service design, as well as CMfg based platform for PES deployment, for composing and deploying new PES (application specific services), where additional features and customizations are implemented. The objective is to allow manufacturers to effectively build/maintain/configure products and services embedding more and more knowledge in product by cost effective creation/management of services and production processes in order to meet dynamically changing needs of customers as well as optimize environmental impact of their products. The focus is on the product extension by generation of new services and cyber physical features to allow for such product extension (e.g. integration of new sensors and AmI features in products). IV.

ENGINEERING TOOLS FOR AMI FEATURES AND CONTEXT SENSITIVITY

As explained above, the proposed approach is to apply AmI solutions as powerful source of information to identify context of product-service use, as well as to identify context under which the product is produced – needed, on one hand to “personalize” product-services and, on the other, to identify environmental impact patterns. Defining the mechanisms and (AmI-based) sensors to monitor context and process changes – in relation to the quantitative relationships influencing the meaning of context from various perspectives (process, products, user, services) is a next challenging task. This includes a definition of the entities relevant for monitoring context and equipment parameter changes and investigation of the extent to which these mechanisms depend on the viewpoint (equipment or product) for which changes are being monitored, according to the defined context models. Tools for definition/design of AmI solutions: The engineering tool allows for effective selection of the AmI solutions to be integrated in the product, allowing for provision of new PES as well as extraction of context on product-service use following the above explained methodology approach. AmI technology and associated services are considered throughout the process starting from the design of the product and the manufacturing process to the use and operation of the product. Special attention is paid to monitoring the resources (energy, water) consumption during both use stages. The method and

tool have to support the designer in selecting/designing of AmI and other measurement systems and appropriate extended monitoring and decision support services, ensure eco efficiency during product manufacturing and life cycle operation, and to provide knowledge needed for enhanced design of products from ecological point of view. The critical issue is how to select the AmI and other measurement systems which may provide information on eco impact which are relevant for (future) product/process design and which may support optimal eco-impact in the product manufacturing and life cycle operation. The component will use structured approach to AmI solutions for manufacturing industry based on a reference model for AmI solutions [19]. Context Modelling: The key task is the definition of a ‘holistic’ and dynamic context model to enable context sensitivity, as a mean for effective “personalization” of products-services, taking into account the context of processes, equipment, products. The concepts and relations of the context model are derived from reference models as well as smart device spaces. These models are used to extract meta-data as upgrades of existing ontologies to support context awareness. Therefore, a Context Model, e.g. OWL-based, to represent the extracted information as explicit machine interpretable knowledge is defined [20]. These existing ontologies serve as a base for context extraction, refining and reusing. The context model, as an intuitive knowledge representation model, explicitly describes processes, resources, user interactions, etc. and their mutual relationships. The context model includes generic and sector/application-specific concepts, so it is extensible for different industrial sectors. It allows modelling various contexts (various abstraction levels, various business models) depending on specific requirements within various applications. This facilitates efficient adaptation/customization of the new components to the needs of different industrial companies. The key research issues to be solved are: how to refine a context model to better describe process/ product in domain-specific activities; how to integrate the context model into existing enterprise service infrastructure and tools, in order to enhance context-awareness of these services, targeting as well the facilitation of context extraction from these services; how to provide a generic solution adaptable to different scenarios. Core service – AmI based monitoring services: The main objective of the AmI monitoring services is to collect data from AmI systems integrated in the product and process in which the product is used (e.g. shop-floor in the case of machines – see use cases) and transform it into knowledge, which can be stored in the common repository and reused for context extraction and ecological impact pattern prediction (in combination to various measurements, e.g. energy use). AmI data are collected and will be transformed into knowledge, in the several steps. Core Service – Context Monitoring & Extraction: The developed services solve the problem of how context can be extracted from multiple sources, such as processes, user interactions, surrounding circumstances, devices, etc. The extracted context is used to personalize the services (e.g. knowledge provision services or other application specific services) as well as for eco impact monitoring and

optimization. Especially mechanisms of extracting meanings from observed content are to be studied in depth. By using an appropriate context model and unstructured information provided by user or devices, the context monitoring & extraction services are able to process this unstructured information, to automatically annotate it and to store it in the context repository. Automated, example-based self- learning of the semantic categories representing the extraction patterns are facilitated [21]. Special attention is dedicated to dealing with context uncertainty. There is always uncertainty in context due to the complexity of reality and limitation of sensor technologies. To provide a more sophisticated solution, several mechanisms for checking reliability of the monitored and extracted context (applying statistical and reasoning approaches) are developed. V. USE CASE The company delivers world-wide technology for industrial shoe producers, in particular machinery and molds for both direct soling and unit sole processes. The company designing and delivering complex automated machines/systems for the shoe production worldwide intends to radically improve services around their automation systems, aiming at building “cyber-physical based machines” and services. These automation systems, implying rotary table and injection machines, robots etc., represent system solutions ready for production. The company wants to allow consumer to design customized shoes and to produce such customized shoes on customers demand. To achieve such a vision, the idea is to install PES to allow for a dynamic on-demand design and production of shoes customized directly by the consumer. The company currently provides state-of-the-art machines and services to their customers, but the intention is within the next 5 years, specifically taking into account new markets opportunities, to go beyond the state-of-the-art in industry. To support such approach (by provision of data needed for collaborative work with geographically distributed teams) and in order to allow customer driven shoe production and so called “urban manufacturing”, the company intends to develop intelligent PES and will make use of information from AmI integrated in their machines. The company intends to extend the machines with new advanced AmI solutions. Focus is on AmI solutions for effective interaction between the operator and machines, as well as between the customer and the shoe design system, where advanced technology such as Google Glass augmented reality, multitouch HMI for main control, force feedback sensors (haptic control), contactless gesture control etc. The design of the machines and PES will be driven as well by a strong consideration of environmental impacts which is of special relevance for urban manufacturing. The scenarios will include an intelligently designed media cloud for service technicians and maintenance staff of customers aiming to build services community as social platform. Interacting social and machine networks, where machine control systems are interacting with the social network and machines are interacting with each other are the basis for building various PES around machines, such as predictive maintenance and remote diagnostics etc. These services require integration of production data collecting systems, existing

product documentation such as operating manuals, spare part catalogue, 3D design data from engineering systems etc. To establish such services a unique service robot will be applied capable to monitor the manufacturing processes and provide information to the media cloud needed for services. The robot will also provide on-site support (video and acoustic communication) and will be directly interfaced to the media cloud. It will serve as multi-sensorial assistant for technical analysis of production and equipment. A wide scope of services is planned which have to be adapted/configured for various customers distributed all over the world, i.e. the machines and services have to be adapted to the specific customer cultural environment, and experiences of operators and service staff. The proposed platform and engineering tools should allow for efficient building/reconfiguring of services for various customers. The platform should allow for collaborative building/reconfiguring of services where design experts have to cooperate with geographically distributed service teams and customers (shoe manufacturers). For example, the above described tool for definition/design of AmI solutions will support the designer of machines and services to select AmI solutions (e.g. UI) appropriate for a specific machine, which in turn will allow for integration of the machine in the machine and social network and building of various services. The core AmI based monitoring services will be integrated in the services to provide information from AmI systems need for e.g. remote diagnostic purposes or to support operators in performing specific activities. In order to be effective the services will be context sensitive, i.e. they will include above described core services for context monitoring and extraction. In this way these services will automatically adapt to a specific situation under which the machines/equipment are operating. The context modelling tool will allow for efficient defining of various context models depending on the customer groups, geographical and cultural circumstances in which the services cyber-physical machines will be applied. VI. CONCLUSIONS/FUTURE WORK The main originality lies in solving the above listed crucial problems in providing new PSS concept in manufacturing industry by combining Collaborative Design of productsservices, AmI Based monitoring, Context sensitivity, Cloud Manufacturing. The proposed approach provides several innovative solutions:  A new methodology on how to combine eco- and leandesign principles for eco-driven product and services and production design, addressing both technological and organizational aspects related to context sensitivity, AmI based monitoring and collaborative building of PES within product ecosystems in global market.  A new platform for collaborative design of Products and services within product ecosystem, set of new core services and service engineering tools, providing means to easily configure new (ICT-driven) PES and

customize them to specific customer groups (and their dynamically changing requirements).  Context sensitivity approach to allow for efficient selfadaptation of the PES to the individual customers’ needs in specific situation (during product/service use). This involves innovative context modelling, based upon ontological approach, and an ontology for ‘process and device context’ to meet dynamically changing context of users of PES, as well as new real-time, dynamic context monitoring services providing data needed to extract current user context based on AmI systems.  New product use monitoring services based on AmI supporting building new products/services based on data gathered throughout a product lifecycle (feedback to design) providing new insights into product use patterns and product environmental impact patterns

[3]

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Aiming to provide such innovative solutions for the several industrial applications the research presented provides several innovations relevant for the overall RTD and industrial community:

[7]

 A pioneering application of CMfg for industrial community, aiming to allow for effective collaborative product-services design within product ecosystem and effective deployment of PES

[9]

 This is one of the first attempts to apply advanced AmI technology and context awareness solutions and paradigms in classical manufacturing allowing for product extensions.

[11]

The proposed solution is currently under development. The platform and engineering tools will be applied to develop the new services in four application cases. It is expected, based on the experience of the industrial partners involved, that the platform and tools will allow for reduction of time and efforts needed for development/update of Cyber-Physical Based Products by 30%, and allow for an increase in number of innovative personalized PES by more than 40%. It is expected that the proposed solution will contribute to reduction of time to market of Cyber-Physical Based Products by at least 10%. ACKNOWLEDGMENT This work is partly supported by the ProSEco project of EU’s 7th FP, under the grant agreement no. NMP-2013 609143. This document does not represent the opinion of the European Community, and the Community is not responsible for any use that might be made of its content. REFERENCES [1]

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