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Life cycle assessment (LCA) is a well-established approach and benchmark for design for sustainability efforts, in which detailed reports are produced that can serve as decision-making guides for developing new products. However, LCA reports are typically dense and technically complex, making it difficult for many engineering design project stakeholders to appropriately leverage the information found within them. Our work seeks to understand and improve the transfer of knowledge from LCA reports during the early stages of the design process, specifically leveraging the natural language capabilities of large language models (LLMs). In this paper, we investigate how four LCA-and sustainability-centric prompting frameworks can extract relevant design knowledge from LCA reports, demonstrated through a case study where an LLM (ChatGPT) is prompted on a provided electric toothbrush LCA report. Key findings illustrate the prompting frameworks can establish high-level summaries and identify life-cycle specific information, but the development of specific and design-focused sub-prompts will allow for richer understanding. We envision designers can use these proposed frameworks to query an LLM to gain context and insights from relevant LCA reports. The proposed techniques serve as a basis for automatic knowledge extraction from life cycle documents, creating accessible information in a user-friendly manner for designers who look to develop life-cycle-informed products. [ABSTRACT FROM AUTHOR]

Digital data management and enterprise systems have become key to support the digitalisation of maintenance activities. With traditional maintenance activities still striving for efficiencies, platforms such as the natural language processing (NLP) are supporting industries to mine textural data, not just extracting degradation terminologies but providing the maintainer with holistic insights on the degradation process. Traditionally, the degradation analysis, the first step in maintenance, is a manual process for defect characterisation, followed by failure investigation and a remaining useful life estimation. To enable digitalisation, transfer of human cognitive decision making from the physical world to the digital world is key. This paper enables this cognitive knowledge transfer through the design of a common degradation taxonomy and extracting terminology relationships to produce degradation causality with an NLP knowledge extraction approach. Further, this paper proposes and demonstrates a framework to present the data in the form of a knowledge graph populated using an application-level ontology. Use cases in the aerospace context have been used to show the power of the NLP and conceptual journey into the digitalisation of maintenance. [ABSTRACT FROM AUTHOR]

The increasing diversity of variants and the high product complexity pose ever-greater challenges for manufacturing companies, especially in assembly. Therefore, assembly assistance systems are increasingly used to manage complexity in terms of guiding workers through assembly processes. Previous research work in this context has mainly focused on the assessment and improvement of typical assistance functions as well as their implementation. The integration of active knowledge management on both shopfloor and planning level illustrates a high potential for the extension and development of new assistance functions. However, the possibility of using informational assistance systems to implement active knowledge management in industrial assembly has not been sufficiently studied yet. Therefore, this paper firstly defines the main challenges of the integration, which are summarized in a negative cycle of knowledge management in assembly, and secondly describes the key requirements for knowledge management in conjunction with assembly assistance systems. Using these requirements, the paper finally presents a conceptual framework and the implementation in an informational assembly assistance system. [ABSTRACT FROM AUTHOR]

Demographic change and the digital transformation are megatrends of our time. This paper presents a concept of how to use elements of the digital transformation to transfer knowledge in order to cope with the demographic transitions especially in manufacturing SME. The concept combines visual and tactile sensors in order to determine hand movements. In order to recognize hand movement this paper will review different approaches of how to describe hand movement in manufacturing environments. In an outlook, the application of this concepts and challenges applying the concept in a prototype will be discussed. [ABSTRACT FROM AUTHOR]

Prototyping is an indispensable activity in product development that facilitates the generation of knowledge in the design process. It is crucial that this knowledge is the right knowledge (e.g., type, fidelity, and accessibility) to ensure stakeholders can evaluate and decision-make effectively. While this is well-recognised, prior work has focused on classifying prototyping/prototypes by their attributes rather than the knowledge they generate. As prototyping methods have a significant impact on the knowledge generated, both in the activity and resulting prototype, a means by which methods can be classified against knowledge generated would enable support of prototyping method selection, equivalence, and substitution. To this end, this paper presents the existing concept of fungibility – a context-specific value judgement as to whether two 'objects' are the same, and therefore capable of mutual substitution – and considers it in the context of prototyping and knowledge generation. In so doing, the paper provides a framework by which prototyping processes may be compared against their knowledge contributions to determine if they are fungible. This is then shown to support prototyping method comparison, selection and direction. After defining the fungibility framework, value is demonstrated via an illustrative example where it has been applied to the comparison of two prototyping instances. The framework is then appraised for its utility and limitations. [ABSTRACT FROM AUTHOR]

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information. However, integrating ChatGPT into the design process also presents new challenges. In this paper, we provide a concise review of the classification and representation of design knowledge, and past efforts to support designers in acquiring knowledge. We analyze the opportunities and challenges that ChatGPT presents for knowledge management in design and propose promising future research directions. A case study is conducted to validate the advantages and drawbacks of ChatGPT, showing that designers can acquire targeted knowledge from various domains, but the quality of the acquired knowledge is highly dependent on the prompt. [ABSTRACT FROM AUTHOR]

There had been research efforts in the knowledge management and related disciplines devoted to integrating data and knowledge generated from manufacturing activities into the design process. Such efforts focused on approaches for enhancing engineering specifications and supplier related decision making in order to improve manufacturing quality through reducing defects. However, rarely did previous researchers address the 'Integration' aspect as part of a centrally-driven systematic workflow that enables collaborative knowledge capture between the internal design teams and manufacturing engineering teams firstly, and also with dispersed supplier teams. The industrial context of this research is discussed in this paper reflecting on the nature of the aerospace industry, which involves heavy reliance on information exchange to optimise designs on a day-to-day basis. This aspect had already been identified by many researchers to be under-addressed and a very significant challenge of collaborative design. The main aim of describing the context is to address the complexity involved in integrating manufacturing data generated internally first, and from suppliers second, within workflow context in order to to design a collaborative framework using knowledge management principles. The complexity also features aspects of product design and manufacturing specifically related to the the aerospace industry which tend to have exceptional functional specifications than other products from other industries. The implications of the proposed approach in the light of high value, low volume and high product lifecycle management challenges is also discussed. This paper also reports findings of an empirical investigation carried out with a leading UK based manufacturer of avionic systems with regards to manufacturing knowledge integration challenges related to improving the design of complex avionic systems, in order to enhance the design process for the business and improve the adaptation of a generic product design knowledge base. The purpose of it is to enable more rich data towards information driven design to improve manufacturing quality through defect reduction strategies and techniques. [ABSTRACT FROM AUTHOR]

This paper aims at exploring cooperative work and knowledge implication into Systems Engineering approach in order to study organisational impact. Furthermore, the study aims at providing conceptual models that enhance a Model-Based Systems Engineering (MBSE) with the purpose of taking into account knowledge management (KM). The research formulates a decision model that complements the existing methods through Systems Modeling Language (SysML). The methodology is based on current SysML diagrams and proposes a KM solution focused on an organizational approach. The requirements satisfaction is built on this conceptual approach to help on the decisional process. Systems Engineering (SE) integrates all the disciplines and specialty groups into a team effort forming a structured development process from design to production. SE shows that cooperative work and management increase with the complexity of systems. Thus far, the current models do not present enough cooperative and knowledge management supports. The study proposes to add a new model to imply KM and organizational aspect through SysML in order to develop a cooperative work process with a first conceptual approach. This paper tackles several possibilities based on modeling language SysML extension and discusses the interest of implementing the model while providing a complementary decision-making tool. [ABSTRACT FROM AUTHOR]

Lean production. It is one of the most misunderstood business concepts of our time. Since the 1990s, it has been mystically reduced from an organization-wide concept to a set of principles and best practices for manufacturing in isolation, only to be subsequently re-constructed as an additional set of principles and practices for other specific functions (e.g., product development), often leading to sub-optimization. In this paper, we draw on practical insights from two Norwegian case studies and present an overview of emergent actionable knowledge to rethink lean production as a holistic approach to value creation and long-term profitability, with the product at its core. [ABSTRACT FROM AUTHOR]

The operation of manufacturing systems is increasingly accompanied by data-driven continuous improvement processes and product traceability is required. Commonly, radio-frequency identification (RFID) technology is applied to track the flow of a uniquely-identifiable workpiece along various stations or waypoints within a factory. Based on an automotive use case, this paper describes how to analyze the resulting traceability data in order to identify several properties of a manufacturing system. The acquired knowledge can support performance evaluation and facilitate model building for material flow simulation as a foundation for digital twins and cyber-physical production systems. [ABSTRACT FROM AUTHOR]

If Customized Product Development is perceived as developing products that fulfill the customers individual requirements and in parallel reflect production constraints, such as manufacturing capabilities, a direct demand can be derived for solutions to adapt a given design easy and fast to new requirements based upon the companies production knowledge - at best in an automated way. The latter is usually covered by Knowledge Based Engineering systems. KBE systems are capable to automate repetitive engineering tasks, such as the automated calculation of ship structural design. However, while the efficiency of implemented KBE projects is non controversial, the development or modification of an existing KBE solution usually requires substantial investments due to knowledge acquisition, codification and software implementation. In addition most solutions are still case based and not grounded in structural frameworks. Knowledge is often written in a proprietary language; rules and algorithms are not compatible with other KBE-frameworks and are usually not on a level that is comprehensible for the engineers or domain experts. While this may not be crucial for long development cycles, it may become a hurdle in terms of Customized Product Development with its short cycles. In other words, future KBE must support an incorporation of knowledge from different domains and business units. Thus the objective of the paper is to explain the need for a change in collaborative knowledge sharing and re-use in context of KBE. Based upon, the constraints for a KBE related interchange format are drafted. A three layered approach is proposed in order to adequately represent and exchange KBE knowledge. Each layer addresses different levels of abstraction: an upper layer describing just the core knowledge at a glance, a middle layer in order to codify the knowledge on abstract level, but with purpose of software development and a base layer covering the software code itself. Utilizing an independent format for management of KBE knowledge, the users of CAx systems are able to exchange codified knowledge and gain the rationale behind. Hence the full paper attempts to deliver a substantial contribution for the development of systems, which are capable to easily adapt a given design to upcoming user-requirements, while facing the production challenges. [ABSTRACT FROM AUTHOR]

The evolution of production systems encloses continuous adaptation of workplaces with changing levels of technologies and automation. This research paper examines scenarios of multi-skilled operators, cooperating to accomplish a common goal. The under-consideration environment consists of a distributed network of workstations, each one assigned with a set of pending jobs. Therefore, the production engineer faces the challenging task of job allocation to suitable operators and appropriate workstations. As such, the formulation of the problem is based on the evaluation of each individual operator's job-related skills and the provision of an intelligent decision-making algorithm for the human resources allocation. The framework addresses the optimization of the allocated operators-jobs correlation and the workforce cost as the model's decision criteria. The model was implemented in a cross-platform planning application and tested in a real-life industrial scenario. [ABSTRACT FROM AUTHOR]

In the recent years, many methodologies and tools to support the energy efficiency re-design of production systems have been developed, however, they do not investigate the real-time manufacturing process. In this paper, a methodology for energy efficiency re-design of production systems in a context of smart manufacturing is proposed. The continuous production-machine data collection with operator feedbacks enables the creation of a knowledge-based repository that provides useful support during the design of manufacturing systems. A case study in an automotive sector company has allowed to implement the methodology and to assess its effectiveness. [ABSTRACT FROM AUTHOR]

Today's mechanical engineering companies are confronted with challenges like new material technologies, shorter product life cycles, increasing interdisciplinarity of the various departments as well as product development across distributed company locations. In the context of lightweight design, Tailored Textiles offer promising potentials to reduce product weight and manufacturing costs. Tailored Textiles are textiles with integrated local reinforcements whose textile properties can be adapted locally during the production due to the textile structures, so that process steps and therefore time and costs can be reduced. However, the production-oriented design of Tailored Textiles is currently very complex. One reason for this is the large number of stakeholders that are involved in the process. In order to make this complexity manageable, it is necessary to develop a common understanding of the textile process chain and to improve communication between different stakeholders. Another major challenge is the identification and management of technical parameter dependencies over the entire product development process (PDP). The aim pursued by this work is to link the technical parameter level of different expert models with the process level of Tailored Textiles PDP, so that expert knowledge is externalized to other stakeholders In this context, Model-Based Systems Engineering (MBSE) is a promising approach. Through the formalized application of modeling, MBSE allows the process accompanying development of a system model, which enables the visualization of the parameter dependencies within the product development process. In this paper, a methodological approach is presented which initially involves modeling the PDP for Tailored Textiles in a product data management system (PDMS). The process is then linked to a MBSE modeling tool. In the modeling tool, the analyzed parameters and their dependencies are set up and visualized. Following this procedure, the feasibility of the design will be evaluated based on a use case. [ABSTRACT FROM AUTHOR]

The application of Life Cycle Engineering methodologies during the development of frontier and emerging technologies, such as batteries, is essential to anticipate unexpected problem shifting when mitigating current sustainability challenges. As development of such technologies is often framed in multidisciplinary research contexts, structured knowledge management of all relevant information is required. This paper introduces the concept of Knowledge Based Life Cycle Engineering to structure and link multidisciplinary knowledge, enabling a collaborative system modeling and life cycle analysis. We present the components that comprise the approach: knowledge engineering, repository and data platform; along with an implementation based on a reference ontology. [ABSTRACT FROM AUTHOR]

The role of workers in factories of the future is expected to change from labour-intensive to knowledge-intensive activities. Acquisition and formalisation of knowledge is a complex and time-consuming task limiting the performance of manual processes. This paper presents a framework to capture, manage and apply knowledge in a manual assembly process. The framework has been illustrated using an example of a riveting process. The proposed framework is intended to help in decision-making during a process, thereby minimising faults and improving productivity. In future, this framework needs to be validated, and if necessary updated, for other manufacturing processes. [ABSTRACT FROM AUTHOR]

The use of physical mock-ups offers the possibility to evaluate the efficiency of assembly processes in early design stages of product development. Therefore, the challenges of assembly procedures, which arise during the implementation of new large-dimension assembly groups, can be managed prematurely. Assembly training with a derivative product in an assembly workshop stimulates a knowledge exploitation of function and performance of assembly groups. The paper shows the acquisition of technical expertise from shop floor staff and knowledge from non-experts from assembly operations during the assembly workshop, by using the handling with a derivative product. These information were integrated into the implementation process of assembly planning. [ABSTRACT FROM AUTHOR]

Due to increasingly specific customer requirements, complex value-added processes and services characterize the single and small series production. A low degree of automation and insufficient knowledge management complicate the efficient accomplishment of projects, especially for the transparent calculation of new offers and change requests. The dissemination of product-service systems causes the additional challenge of calculating intangible services accurately. Innovative information and communication technologies (ICT) offer new potential for effective and efficient design of the calculation process. The availability of data along the entire product lifecycle, especially during the design phase, increases the transparency and allows the identification of interdependencies as well as prognosis possibilities. However, current calculation models only insufficiently consider the use of ICT. The solution presented in this paper considers the development of a design model for the holistic calculation of product-service systems in single and small series production that integrates and processes all cost-relevant data, both internally and externally. In order to filter important information, it includes the usage of suitable data analysis and enables a continuous learning process as well as sustainable knowledge management along the entire calculation process. [ABSTRACT FROM AUTHOR]

Nowadays, knowledge map as an efficient tool of knowledge management is widely used in various fields. In this paper, addressing the operational support of aerospace manufacturers, we explore the construction method of knowledge map and possible applications. We propose a five-step procedure for knowledge map construction, consisting of knowledge definition, knowledge extraction, knowledge description, knowledge links and knowledge map creation. Following this procedure, the knowledge map of operational support system for aerospace manufacturers is constructed in an example way. We also show the potential applications of knowledge map in aerospace manufactures. [ABSTRACT FROM AUTHOR]

Through semi-structured interviews in six companies, the current paper investigates the knowledge management practices in product-service systems from product lifecycle perspective. Knowledge requirements (types/sources), knowledge sharing, and knowledge reuse in both beginning-of-life and middle-of-life phases are our focus. Similarities and differences on knowledge management practices were found in the two phases. Our finding suggests that in the current digital era, in order to keep competitive, the knowledge requirements in different PLC phases should be clearly identified, the importance of people should be re-emphasized, external collaboration should be strengthened, and standardization should be advocated in the company. [ABSTRACT FROM AUTHOR]

The current paper aims to investigate the knowledge management practice in PSS based on literature, and presents propositions for both academia and practitioners from the perspective of product lifecycle. In particular, we look at knowledge requirement, knowledge reuse, and knowledge sharing throughout the entire product lifecycle. Our findings suggest that more appropriate knowledge representation manners and standard knowledge representation form, the identification and classification of the most important knowledge for different stakeholders, and balanced application of personalization and codification strategy will be very important for companies in PSS domain to manage knowledge. Trigging by the increasing concern of sustainability in PSS context, we propose a product lifecycle model integrated raw materials extraction and material production, therefore making a more integral close information loop in PSS. [ABSTRACT FROM AUTHOR]

Service technicians spend a considerable amount of their working hours in order to search for information regarding a current work order. In case of an IPS² malfunction for example, they can either search for potential failure causes within previously documented repair reports that describe a similar malfunction or manually inspect the IPS². On the one hand the former IT-related information procurement is caused by a large amount of irrelevant search results rendered by current state of the art information retrieval approaches implemented in maintenance-related IT systems. On the other hand many repair reports suffer from missing or ambiguous information and a holistically low data quality, which makes it difficult for the service technicians to derive task-related information from a particular repair report, although this report basically describes the same malfunction. These current difficulties will be amplified once service partners get access to maintenance-related information documented by other service partners (companies) within the IPS² network as the amount of available data will increase drastically and new problems will raise concerning the heterogeneity of the data. The paper on hand presents a semantic quality assurance concept for heterogeneous unstructured repair reports that addresses the low data quality problem by utilizing natural language processing and machine learning methods to automatically analyze the service technician’s inputs during the repair report creation process and by notifying him of potential losses in data quality. The concept’s feasibility has been shown by performing a case study with a prototype that utilizes the developed methods. [ABSTRACT FROM AUTHOR]

Availability-oriented business models in IPS 2 networks obligate providers to cooperate with several service partners that provide maintenance, repair and overhaul (MRO) services in case of a machine malfunction. Currently, these partners, as well as customers, who operate the IPS 2 , barely have access to technical documents from the product development phase or to the knowledge of other parties within the IPS 2 network. Additionally, traditional information retrieval approaches, which are currently state-of-the-art within MRO-related IT systems, render too many irrelevant results and are not suitable for efficient usage in heterogeneous data that differ in wording and structure because of the diverse nature of IPS 2 partners. This causes long machine downtimes, as the service technician needs to inspect the machine locally by himself to find the cause(s) for the malfunction. The paper on hand presents a provider-driven knowledge allocation approach to overcome the aforementioned issues by a semantic repair assistant as the gateway to knowledge from all parties within the IPS 2 network. [ABSTRACT FROM AUTHOR]

A new approach to knowledge management in engineering domains was presented in the CIRP General Assembly 2001 with the title “An Autopoietic Approach for Building Knowledge Management Systems in Manufacturing Enterprises” [1] . Based on this a new process management system was developed and deployed. Today the system supports day to day engineering work of more than 300 engineering related staff on three continents. It drives organizational behavior by mimicking intelligence and the acquisition of knowledge, using both to derive suitable processes. This paper reports on lessons learned and may shed some light on future developments of knowledge-based manufacturing systems. [ABSTRACT FROM AUTHOR]

Due to the megatrend globalization, special machinery is gaining significance for the capital goods sector. Characterized by the fulfillment of individual customer requirements, companies in special machinery have to deal with very specific and technologically complex tasks. Hence, managing information and knowledge becomes vital for a company's competitive ability, notably when it comes to expert knowledge. The characteristics of special machines leads to iterative processes for problem solving and thereby, increase lead times significantly. The more technologically complex a machine is, the more scattered the expert knowledge, meaning that many different experts need to be consulted before solving a problem. Up to now, in scientific literature, there has been little discussion about the challenges of special machinery and practical solutions regarding an implementation of technical intelligence in a special machinery environment. Therefore, the goal of this paper is to give an example of how an expert system can be applied to special machinery surroundings and thus, increases productivity. A Bayesian network forms the basis of the system as it allows efficient inference algorithms and reasoning under uncertainty, despite its ability to describe complex dependencies. The expert systems capability has been proven in industrial laser manufacturing. [ABSTRACT FROM AUTHOR]

Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance. [ABSTRACT FROM AUTHOR]

Knowledge management has drawn great interest in manufacturing industries and related business practices. With the requirement for better managing the massive data and knowledge generated during different lifecycle stages of products, manufacturing industries are looking for effective way to acquire, store, process and share knowledge from and between different stakeholders, so as to make appropriate decisions and continuously improve business operations. Current (conventional) engineering information systems in manufacturing applications, such as Enterprise Resource Planning, Computerized Maintenance Management and Product Lifecycle Management Systems are difficult to inter-operate and integrate with each other when dealing with growing amount of data and knowledge as a product goes through its lifecycle stages. As informational and communication technologies (ICT) are being developed much faster in other sectors such as financial, business and social media, it is important to explore the potential of latest ICT tools predominantly used in those sectors for engineering applications and identify any advantages and benefits over the conventional engineering information systems. This paper presents an experiment in using an Open Source Content Management System, for the implementation of a collaborative product-service system for the planning and execution of maintenance and service operations of high-value complex numerical control machine tools in advanced manufacturing systems. [ABSTRACT FROM AUTHOR]

Process patents contain substantial knowledge of the principles behind manufacturing process problems-solving; however, this knowledge is implicit in lengthy texts and cannot be directly reused in innovation design. To effectively support systematic manufacturing process innovation, this paper presents an approach to extracting principle innovation knowledge from process patents. The proposed approach consists of (1) classifying process patents by taking process method, manufacturing object and manufacturing feature as the references; (2) extracting generalized process contradiction parameters and the principles behind solving such process contradictions based on patent mining and technology abstraction of TRIZ (the theory of inventive problem solving); and (3) constructing a domain process contradiction matrix and mapping the relationship between the matrix and the corresponding process patents. Finally, a case study is presented to illustrate the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

State of the art Engineering Education is crucial for Research and Innovation according to the European Union's growth strategy. This paper contributes with more knowledge of how Universities and Industry can facilitate collaborative learning environments and create efficient working methods. A group of students accepted the challenge from an industrial company to examine robotic assembly of their products. The students built a robot cell prototype in the company's design department, to promote knowledge about design for robot manufacturing. The setting of this learning environment enabled knowledge creation with significant learning outcomes for the university, the Industrial Company and other partners. The project motivated the teachers to collaborate across Faculties at the University, and gave new insight in formulation of objectives for this type of projects. [ABSTRACT FROM AUTHOR]

Manufacturing industry is highly affected by trends of globalization and increasing dynamics of product life-cycles which results in global supply chain networks. For inbound logistics, a high variance of parts from different suppliers and locations needs to be delivered to the assembly line. Planning these inbound logistics processes depends on frequently changing information of product development, assembly line planning and purchasing. Currently, a high amount of time is spent for gathering information during planning and existing knowledge from previous planning processes is scarcely used for future planning. Therefore, this paper presents an approach for predictive inbound logistics planning. Using machine learning, generic knowledge of logistics processes can be extracted and used to predict future scenarios. [ABSTRACT FROM AUTHOR]

Along the product life-cycle, industrial processes generate massive digital assets containing precious information. Besides structured databases, written reports hold unstructured information hardly exploitable due to the lack of vocabulary and syntax standardization. In this paper we present a methodology and natural language processing approach to exploit these documents. Our method consists in providing connections based on supervised retrieval of domain-specific expressions. No prior document analysis are required to drive the algorithm. It underlines a scale of specificity in pattern visualization. This allows relevant and specific information extraction for feedback (e.g. design stage, after-sales service). [ABSTRACT FROM AUTHOR]

In the case of Product-Service Systems (PSS), the design phase is characterized by a demand for intensive exchange of knowledge between stakeholders from different domains. Thus, a comprehensive approach for knowledge sharing would support the integrated development of PSS. Existing attempts are however mainly focusing on using explicit service knowledge for product design and service operations only. Knowledge exchange between domains, including tacit knowledge and sentiment, for the integrated design of products and services have received less attention. The objective of this paper is to present the initial results on the requirements for cross-domain knowledge sharing when designing innovative PSS. [ABSTRACT FROM AUTHOR]

Adaptability of inspection and testing activities in manufacturing systems is of high importance for sustaining quality of products and production processes. However, process models identifying the demand for changing quality testing activities as well as methodologies for systematic adaption of ongoing quality inspection processes are deficient. This is related to the fact that existing inspection planning procedures are primarily related to product and process development stages and not to ongoing series production. Furthermore, knowledge acquired by modifying ongoing quality inspection activities is seldom used systematically to improve inspection planning for future product and process development. This paper presents a holistic approach for inspection planning covering the systematic change and adaption process of inspection activities during manufacturing stage as well as knowledge transfer to future product and process development. By the evaluation of inspection-related change demand, suitable design options for inspection process modification can systematically be derived. Based on the paradigm of engineering change management (ECM), the adaption of quality inspection processes can be planned, assessed and implemented. Finally, knowledge acquired by implementing inspection process modifications is transferred to inspection planning for future product and process development by using concepts of knowledge management. [ABSTRACT FROM AUTHOR]

Recent visions for the factory of the future highlight the importance of integrating the pillars of Knowledge Management (KM) in the whole manufacturing divisions involved in the product lifecycle development. The increasing usage of ICT tools in the factory coming with the digital (PLM, ERP, MES, etc.) and smart (IoT, RFID, Wi-Fi, etc.) manufacturing paradigms contributes to this vision with the aim of supporting factory workers in conducting better production outcomes. However, this expansion results a data and information overflow making the handling of the access to the exact needed information more and more complex. This fact explains the need for a contextual access to the information and more generally to the knowledge set in the factory. Based on some fundamentals derived from ubiquitous computing paradigm, this paper proposes an approach to handle contextual access to the manufacturing knowledge. [ABSTRACT FROM AUTHOR]

Based on the customer's product recognition, environment protection becomes key sales arguments within the consumer goods industry. Customers expect longer usage phase of products and reduced use of resources. Product reliability may help to save resources in many ways such as replacement of parts and minimizing logistic efforts throughout the product's life span. To achieve the generic goal of reliability and sustainability, it is important to learn from product's failures during design, manufacturing and use phase. Therefore, structured knowledge regarding a long life cycle (e.g. development: 2 years; production: 6 years; usage: 15 years) should be saved. This paper outlines a conceptual Lessons Learned approach on how to analyse, save and prioritise Lessons Learned issues – based on structured weak point data and information - out of the product life cycle to improve reliability and sustainability of the subsequently product generation. [ABSTRACT FROM AUTHOR]

The expanding smart industries approaches (called Industrie 4.0) going together with increasing ICT penetration and data collection in production areas also promote advanced maintenance, repair and overhaul strategies (MRO) targeting to resource, cost, time, and quality optimizations. However, up to now only few use cases are reported; additionally, concepts for sophisticated economic and ecological control and evaluation as a prerequisite of implementation are missed. Motivated by these deficits, the paper suggests an integrated control, evaluation and knowledge management concept. The evaluation methodology comprises a decision theory-based procedure model allowing for systematic assessment and analysis of alternatives (e.g., as-is vs. altered MRO-processes, design of ICT) under consideration of the relevant economic, ecological and/or social objectives as well as influencing factors. Additionally, a knowledge management approach intended to improve the usefulness of measured data for MRO-related decision-making is presented. For demonstrating the procedure model as well as the knowledge management approach, the fictive use case of a mobile network operator is regarded. [ABSTRACT FROM AUTHOR]

Providing maintenance and services for high value complex products would extend manufacturers’ responsibilities and benefits to the products’ whole usable life, and provide the opportunities to re-use or re-manufacture some failed parts. Sophisticated Computer Numerical Control (CNC) machine tools in modern manufacturing systems are special products in that they are also used to manufacture other products, and their operation performance directly affects the quality of the manufactured parts as well as the performance of the entire manufacturing system. To ensure CNC machine tools’ consistent performance, appropriate and efficient maintenance and services are essential and this is more challenging as technologies become more sophisticated and the environment is more dynamic. Previous research was mainly focused on maintenance strategy and maintenance scheduling. Very little effort was devoted to providing operational guidance for maintenance process execution, i.e., providing service suppliers with detailed information about resources needed for maintenance such as tooling, consumables, materials and spare parts, as well as service steps including disassembly and assembly of the serviced products. In this project, planning maintenance operation sequences, schedules and resource allocation are the three main tasks for generating final maintenance plans. This paper will present a Collaborative Maintenance Planning System (CoMPS) which will manage information and knowledge to support decision making in maintenance process planning. [ABSTRACT FROM AUTHOR]

Increasingly complex products and business models require support of increasingly complex information. In this paper we propose a new approach BIC, Business Information Context, to define contexts for accessing, viewing, and managing this complex information. BIC structures information based on key domains: business drivers, business processes, information entities, product characteristics, and information systems. We compare BIC with other and simpler approaches, like views and contexts used in the ISO 10303 (STEP) standards, design methodologies, and PLM systems. We illustrate the definition and use of BIC with an implementation of an application for protecting a company's intellectual property. [ABSTRACT FROM AUTHOR]

With the proliferation of systems generating huge amounts of product information and the increasing number of companies involved in the production processes, the efficient management of product lifecycle is becoming a challenging issue. The problem of maintaining a coherent structure to represent and link different pieces of information is crucial for companies aiming at improving their performances by reducing the time to search for particular information. The need of developing models for such a complex information structure can be found in many works, which define semantic models and ontologies to represent and integrate information along the product lifecycle. However, some works address only specific cases, thus resulting in very detailed ontologies that model few portions of the product lifecycle and are not easily generalizable or usable, while other works define more general models, but did not provide concrete examples of applications in industrial domains. The aim of our work is to propose a reference ontology to support PLM, general enough to represent the main concepts and relationships needed to manage the whole product lifecycle. Furthermore, the PLM reference ontology can be extended and specialized to manage specific product lifecycles by adding new entities, sub-entities and relationships to its structure. An example of a real application of the development and extension of the PLM reference ontology in an industrial case is reported. The work presented in this paper is supported by the EU-FP7 project amePLM. [ABSTRACT FROM AUTHOR]

Research on technical complaint management has shown that it can be a driver of product quality improvements. Systematic handling and analysis of customer complaints, throughout the whole product lifecycle, has a significant positive impact on future products’ quality. Nevertheless, the results of an empirical study within the machine tool industry provide evidence, that long-term effects for future product generations are rarely achieved. Based on this study, the paper examines reasons for this lack of sustainability in quality improvements. An approach to data-oriented technical complaint management is presented, which focuses on the transformation of complaint information to failure knowledge and its use for long-term quality improvements. [ABSTRACT FROM AUTHOR]

Product Lifecycle Management (PLM) focuses on management of product data and processes across their lifecycle stages. One of the key features of PLM is the management of knowledge collected during product development. Reduced time to market, a better collaboration and savings are expected benefits of PLM amongst others. The implementation of a PLM System inside a company can be a hurdle because of the heavy change of the company structure during introduction. Most PLM solutions are based on one integrated product model that stores product data and shares these data with all contributors. However, the access of product data by different expert domains can be challenging when domain expert knowledge is necessary to understand it. This leads to a communication overhead that increases cost, product development time and thus time to market due to the need for contact to experts. To deal with comprehensible knowledge throughout the product lifecycle phases and thereby eliminate communication overhead, this paper presents a process-oriented and integrated semantic solution that supports interoperability of knowledge during all phases of the product lifecycle. Based on shared ontologies and product models, collaborators of product chains have the ability to define their own extensions to the underlying models and ontologies. Collaborators are thus able to use their own modeling methodology, which reduces inhibitions to use the solution. The ability to automatically infer information between partial product models of different process chains enables a better collaboration during product development. Since the access of inferred information for specific process chains can be permitted or restricted, collaboration between multiple departments inside and outside the enterprise is supported. For a better understanding, the solution will be exemplarily applied to the aviation industry. This use case will also be used for evaluation and further improvement. To give a brief outlook on future activities besides PLM, the OSMOSE Project is introduced. [ABSTRACT FROM AUTHOR]

Nowadays, reverse engineering is widely spread in the manufacturing industry. The need of shorter development cycles has led to the identification of social and economic issues related to reverse engineering. The integration of a reverse engineering solution in a PLM context represents a good solution in order to shorten the development cycles, especially when it is automated. In this paper we present the issues identified in the context of METIS, a French national project aiming to provide a software solution for reconstructing large and complex mechanical assemblies and systems, through a global reverse engineering methodology combined with a knowledge management approach, and using heterogeneous data as inputs. [ABSTRACT FROM AUTHOR]