Production Engineering and Management

The annual International Conference on Production Engineering and Management takes place for the sixth time his year, and can therefore be considered a well - established event that is the result of the joint effort of the OWL University of Applied Sciences and the University of Trieste. The conference has been established as an annual meeting under the Double Degree Master Program ‘Production Engineering and Management’ by the two partner universities. The main goal of the conference is to provide an opportunity for students, researchers and professionals from Germany, Italy and abroad, to meet and exchange information, discuss experiences, specific practices and technical solutions used in planning, design and management of production and service systems. In addition, the conference is a platform aimed at presenting research projects, introducing young academics to the tradition of Symposiums and promoting the exchange of ideas between the industry and the academy. Especially the contributions of successful graduates of the Double Degree Master Program ‘Production Engineering and Management’ and those of other postgraduate researchers from several European countries have been enforced. This year’s special focus is on Direct Digital Manufacturing in the context of Industry 4.0, a topic of great interest for the global industry. The concept is spreading, but the actual solutions must be presented in order to highlight the practical benefits to industry and customers. Indeed, as Henning Banthien, Secretary General of the German ‘Plattform Industrie 4.0’ project office, has recently remarked, “Industry 4.0 requires a close alliance amongst the private sector, academia, politics and trade unions” in order to be “translated into practice and be implemented now”. PEM 2016 takes place between September 29 and 30, 2016 at the OWL University of Applied Sciences in Lemgo. The program is defined by the Organizing and Scientific Committees and clustered into scientific sessions covering topics of main interest and importance to the participants of the conference. The scientific sessions deal with technical and engineering issues, as well as management topics, and include contributions by researchers from academia and industry. The extended abstracts and full papers of the contributions underwent a double - blind review process. The 24 accepted presentations are assigned, according to their subject, to one of the following sessions: ‘Direct Digital Manufacturing in the Context of Industry 4.0’, ‘Industrial Engineering and Lean Management’, ‘Management Techniques and Methodologies’, ‘Wood Processing Technologies and Furniture Production’ and ‘Innovation Techniques and Methodologies

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control

Driving Manufacturing Systems for the Fourth Industrial Revolution

It has been a long way since the aroused of the Industry 4.0 and the companies' reality is not already align with this new concept. Industry 4.0 is ongoing slowly as it was expected that its maturity level should be higher. The companies´ managers should have a different approach to the adoption of the industry 4.0 enabling technologies on their manufacturing systems to create smart nets along all production process with the connection of elements on the manu-facturing system such as machines, employees, and systems. These smart nets can control and make autonomous decisions efficiently. Moreover, in the industry 4.0 environment, companies can predict problems and failures along all production process and react sooner regarding maintenance or production changes for instance. The industry 4.0 environment is a challenging area because changes the relation between humans and machines. In this way, the scope of this thesis is to contribute to companies adopting the industry 4.0 enabling technologies in their manufacturing systems to improve their competitiveness to face the incoming future. For this purpose, this thesis integrates a research line oriented to i) the understanding of the industry 4.0 concepts, and its enabling technologies to perform the vision of the smart factory, ii) the analysis of the industry 4.0 maturity level on a regional industrial sector and to understand how companies are facing the digital transformation challenges and its barriers, iii) to analyze in deep the industry 4.0 adoption in a company and understand how this company can reach higher maturity levels, and iv) the development of strategic scenarios to help companies on the digital transition, proposing risk mitigations plans and a methodology to develop stra-tegic scenarios. This thesis highlights several barriers to industry 4.0 adoption and also brings new ones to academic and practitioner discussion. The companies' perception related to these barriers Is also discussed in this thesis. The findings of this thesis are of significant interest to companies and managers as they can position themselves along this research line and take advantage of it using all phases of this thesis to perform a better knowledge of this industrial revolution, how to perform better industry 4.0 maturity levels and they can position themselves in the proposed strategic scenarios to take the necessary actions to better face this industrial revolution. In this way, it is proposed this research line for companies to accelerate their digital transformation.Já existe um longo percurso desde o aparecimento da indústria 4.0 e a realidade das empresas ainda não está alinhada com este novo conceito. A indústria 4.0 está em andamento lento, pois era esperado que o seu nível de maturidade fosse maior. Os gestores das empresas devem ter uma abordagem diferente na adoção das tecnologias facilitadoras da indústria 4.0 nos seus sistemas produtivos para criar redes inteligentes ao longo de todo o processo produtivo com a conexão de elementos do sistema produtivo como máquinas, operários e sistemas. Estas redes inteligentes podem controlar e tomar decisões autónomas com eficiência. Além disso, no ambiente da indústria 4.0, as empresas podem prever problemas e falhas ao longo de todo o processo produtivo e reagir mais cedo em relação a manutenções ou mudanças de produção, por exemplo. O ambiente da indústria 4.0 é uma área desafiadora devido às mudanças na relação entre humanos e máquinas. Desta forma, o objetivo desta tese é contribuir para que as empresas adotem as tecnologias facilitadoras das indústria 4.0 nos seus sistemas produtivos por forma a melhorar sua competitividade para enfrentar o futuro que se aproxima. Para isso, esta tese integra uma linha de investigação orientada para i) a compreensão dos conceitos da indústria 4.0, e suas tecnologias facilitadores para realizar a visão da fábrica inteligente, ii) a análise do nível de maturidade da indústria 4.0 num setor industrial regional e entender como as empresas estão enfrentando os desafios da transformação digital e suas barreiras, iii) analisar a fundo a adoção da indústria 4.0 numa empresa e entender como essa empresa pode atingir níveis mais elevados de maturidade, e iv) o desenvolvimento de cenários estratégicos para ajudar as empresas na transição digital, propondo planos de mitigação de riscos e uma metodologia para desenvolver cenários estratégicos. Esta tese destaca várias barreiras à adoção da indústria 4.0 e também traz novas barreiras para a discussão acadêmica e profissional. A perceção das empresas em relação a essas barreiras também é discutida nesta tese. As descobertas nesta tese são de grande interesse para empresas e gestores, pois podem-se posicionar ao longo desta linha de investigação e aproveitá-la utilizando todas as fases desta tese para obter um melhor conhecimento desta revolução industrial, como obter melhores níveis de maturidade da indústria 4.0 e possam posicionar-se nos cenários estratégicos propostos por forma a tomar as ações necessárias para melhorar o envolvimento nesta revolução industrial. Desta forma, propõe-se esta linha de investigação para que as empresas acelerem a sua transformação digital

A Model-Based Approach to Comprehensive Risk Management for Medical Devices

The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15].The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15]

Production planning process optimization

Produktionsautomationssysteme sind komplexe Systeme mit viele Entitäten (Roboter, Transportsysteme usw.) die mannigfaltig aufeinander einwirken und zusammenspielen um das Ziel einer Produktendfertigung zu ermöglichen. Multiagenten-Systeme basierend auf verteilter Kontrolle sind der praktikabelste Ansatz die ansteigende Kompliziertheit solcher Systeme in den Griff zu bekommen und gleichzeitig eine flexible Anpassung des Produktionsautomationssystems an variable Rahmenbedingungen zu gewährleisten (z.B. Änderung von Produktionsstrassen oder die Koordination von Transportelementen). Für solch kritische Produktionsautomationssysteme ist eine Überprüfung aller Schritte im Entwicklungsprozess erforderlich um ein sicher funktionierendes System zu gewährleisten. Qualitätsmessungen zur Sicherstellung der Korrektheit von Systemelemente stellen bei der Zielerreichung daher einen wichtigen Schritt dar. Die Softwaresimulation des Werkstatt-Systems erlaubt sowohl Leistungsmessung einer Systemkonfiguration als auch schnellere und preiswertere Reaktion auf sich ändernde Voraussetzungen. Hinzu kommt, dass die Softwaresimulation von Produktionsautomationssystemen immer mehr einen praktikable Möglichkeit darstellt, um Produktionsvorgänge zu planen und/oder zu optimieren.Production Automation Systems are complex systems. They typically have many entities like robots, transport systems, etc. that interact in complex ways to provide production automation functions like assembly of products. The increasing complexity of these systems makes central control more and more difficult. Therefore systems with distributed control are areas of intense research such as multi-agent systems. Moreover, changing requirements for production automation systems require better system and model flexibility for e.g. easy-to-change workshop layouts or coordination of transportation elements. Meeting all this tasks makes the design of a production automation system a challenge hard to solve for designers and system engineers. For safety-critical systems like production automation systems, verification is required for all steps in the development process. Testing aims at measuring the quality of executable system elements, especially the validity of a configuration and correctness of calculated results. A particular challenge is measurement of non-functional quality requirements such as system performance before the actual hardware system is built. Software simulation of the workshop system would allow both performance measurement of a configuration and faster, cheaper reaction to changing requirements, however the validity of the simulation has to be assured. On top of this, software simulation of production automation systems can get more and more a sufficient part during the production planning and optimization process