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Product-Service System (PSS) models offer an integrated service solution to create value for businesses. In the high-value manufacturing sector, value creation for maintaining market competitiveness and improving customer satisfaction is a challenging task. Designing an effective PSS solution depends on integrated service, and product requirements and constraints. Thereby, PSS contract decisions can be significantly influenced by customers’ requirements, and also product and service features. However, existing literature primarily focuses on the impact of service requirements on the PSS contract decisions. Moreover, the existing insights for PSS customization mainly consider hysteretic customer requirements rather than forecasting the requirements under product and service uncertainties. In this paper, an agent-based cost-benefit analysis simulation model is implemented for the PSS contract decisions context. Moreover, a sensitivity analysis is conducted on service costs. Additionally, the effect of product remaining life on service contract decisions is analyzed. The simulation model considers stochastic uncertainty to study PSS contracts customization. The presented model supports PSS customization process by providing a quantitative tool that measures contracts’ profitability as early as the requirement elicitation phase. Furthermore, the bottom-up nature of the model, and the integration of probabilistic uncertainties enhance the flexibility of PSS customization. A case study of PSS contract decision in the machine tool industry is considered for assessing the validity of the presented model. Studies on different forms of service uncertainty highlight that the product failure rate has the most influence on the profitability of a service contract. Moreover, the impact of product age on profitability in an availability-based contract is more significant compared to a spare-parts contract.

There are chances for companies to participate in the German energy markets and generate additional income by powering up or shutting down their facilities and supporting the stabilization efforts from the grid operator. However, the income is highly based on the flexibility markets, the velocity of the flexibility, the amount of energy flexibility and the right timing. The load profile plays a particular role here. Companies are currently not in a position to assess the cost saving potential of hybrid or bivalent systems, when participating in the control energy market. The Institute of Energy Efficiency in Production EEP of the University of Stuttgart and the Fraunhofer Institute for Manufacturing Engineering and Automation IPA have jointly developed an evaluation model, that enables companies to determine the level of potential earnings based on historical values quickly. The developed model was verified based on a modeled bivalent crucible furnace – able to switch from electricity to gas – by assessing the energy reduction potential, the level of energy flexibility, the CO2 reduction potential and the frequency of demand.

Industry 4 0 concept is one of the growing concepts worldwide for the last five years It combines the physical and digital world, thus creating new capabilities which affect not only the manufacturing industry but to all other sectors and organizations Furthermore, many experts worldwide declare that Industry 4 0 will have a substantially positive impact, especially in developing countries, where low-paid jobs are concentrated After facing the social and economic effects of Coronavirus (Covid-19) global pandemic, the introduction of digitalization and moving toward Industry 4 0 has become not only emerging but essential for every industry, including SMEs It is expected that it will help to improve the flexibility, productivity, and sustainability of the industries, to develop a knowledge-based economy and society, and, thus, enhance the quality of life Therefore, this paper aims to analyze the emerging concept of Industry 4 0, impacts and applicability on SMEs, and, in particular, the readiness of SMEs in Kazakhstan to digitization and Industry 4 0 The gap in research, which exists currently about drivers, challenges, barriers, and opportunities specific for SMEs in Kazakhstan for moving towards Industry 4 0, will also be investigated © 2020 Elsevier B V All rights reserved

Assembly systems must provide maximum flexibility qualified by organization and technology to offer cost-compliant performance features to differentiate themselves from competitors in buyers’ markets. By mobilization of multipurpose resources and dynamic planning, Line-less Mobile Assembly Systems (LMASs) offer organizational reconfigurability. By proposing a holarchy to combine LMASs with the concept of an Internet of Production (IoP), we enable LMASs to source valuable information from cross-level production networks, physical resources, software nodes, and data stores that are interconnected in an IoP. The presented holarchy provides a concept of how to address future challenges, meet the requirements of shorter lead times, and unique lifecycle support. The paper suggests an application of decision making, distributed sensor services, recommender-based data reduction, and in-network computing while considering safety and human usability alike.

Recent trends, such as Supply Chain agility, just in time delivery, and mass customization of products, are pushing automated production processes in Industry 4.0 towards increasing flexibility. Although the entire set of devices is already on the market and can be selected according to the needs, the element that regularly has to be redesigned is the robotic end effector, mainly a gripper. Therefore, we established a Fast Development Cycle to accelerate the design and test process of new industrial grasping devices. The cycle consists of the three main steps: Build, Test, and Learn. The most fundamental aspect of the methodology is to decompose a gripper idea into its essential uncoupled constituents and convert it into a gripper pretotype that is solely oriented to validate its basic principles. This procedure ensures a quick initial building phase and allows to enter testing early. Test results are used to evaluate the initial idea, to enhance knowledge and to create the input for the next turn of the cycle. During each turn the design evolves further while reducing uncertainties. Contrary to traditional product development, this method enables to test the feasibility of a device in the earliest possible stage with the least possible amount of time and money. The single steps of the methodology are illustrated in this paper in detail based on real cases from industry. The successful development of industrial grippers from these real cases through the Fast Development Cycle demonstrates its applicability. Instead of optimizing complex systems, the methodology generates simple solutions with a high potential for cost savings in the design and production process of the devices themselves and during their operation in automated production processes in several industries. The methodology applies regardless of the underlying physical principles and encounters the use of TRLs as KPIs to measure technology maturity.

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order to be robust to changes in the production scenario. Therefore, manufacturing companies face a relevant risk when taking strategic decisions about which system resources should be acquired. This risk can be mitigated by exploiting performance evaluation models, such as analytical models and Discrete Event Simulation, that are effectively adopted to estimate the performance of possible system configurations. However, current decision-support tools for optimizing system configurations can be only loosely coupled with performance evaluation models, hence undermining the actual optimization of the system itself, even more if production requirements may evolve in the future. This work presents an analytical methodology to support the optimization of manufacturing systems configuration and reconfiguration subject to evolving production requirements. The methodology integrates a stochastic analytical model for performance evaluation of manufacturing lines into a mixed integer programming problem, by means of performance linearization. The advantage of using the proposed methodology is shown on a line configuration problem, where buffer capacities and machine capabilities have to be jointly optimized, in order to minimize costs and satisfy the target performance.

The focus on the fourth industrial revolution and advancements in 3D printing has reignited the need for energy efficient manufacturing. In particular, Selective Laser Melting (SLM), an additive manufacturing process, has garnered wide attention owing to its adaptability in producing lightweight components for metal industries. Reasonable material demand along with environmental and methodical capabilities of SLM machines has opened up an intriguing possibility to examine its power consumption as well as to determine its suitability for energy efficient manufacturing. In addition, the energy demand of SLM machines along with its occupancy time in a factory floor poses challenges to energy supply grid and subsequent effects on energy flexibility. Hence, it is necessary to determine energy demand of SLM process chain. This paper provides an empirical power consumption analysis of an additive process chain and interprets the power utilized by various process steps of an SLM machine.

The production share of laser powder bed fusion increases in industrial application and single piece and small series production are core markets. Still, many process steps in the field of additive manufacturing are manually operated, especially in post-processing. Additionally, it is not possible to achieve tight dimensional tolerances or low surface roughness which are necessary for typical mechanical parts. Hence, a process chain has to be set up to combine the design flexibility and function integration capabilities of AM with further machining technologies to achieve workpiece requirements. As L-PBF has some particular properties and process parameters influencing post-processing, an individual approach for process chain generation is needed. These properties and parameters are, in particular, support structures and free positioning options within the build chamber of the AM machine. Both properties have a major influence to post-processing and have to be addressed in machine selection, clamping options, processing steps and workpiece description. The approach shows how an additive manufactured workpiece can be described taking into account the complex part consisting of workpiece, support structures and substrate plate. Further, kinematic limitations for further processing are added as meta-information and workpiece requirements and machine performances are compared for a manufacturing process selection for further processing. Based on technological and economical parameters a manufacturing process selection is performed. Finally, improved parameters are suggested to optimize the L-PBF process with regard to further processing. So, the L-PBF process design can combine AM parameters with further processing parameters to improve the whole process chain. The methodology shall be implemented in a software tool to simplify the practical application.

As industry heads to an era of high productivity and energy efficiency, the manufacturing of robotic manipulators moves far from the traditionally heavy and stiff structures. Therefore, rigid body dynamic models become obsolete and the need for new dynamic models accounting for the flexibility of industrial manipulators emerges. To this end, a physics-based approach considering the flexibility of robots’ links and gearboxes is presented. In particular, this paper intends to provide a framework for modelling flexible robotic structures’ towards increasing their accuracy. The robot components’ elasticity is introduced in a lumped parameters model by pairs of springs and dampers, the number of which is affected by the target of keeping the computational effort and run time of the simulation at levels allowing for the integration of the model in industrial environments. Finally, the proposed model considers the activation of the robots’ Digital Twins aiming to enable the use of the model in a series of applications e.g. predictive maintenance, energy efficiency optimization etc.

Hybrid designs with the combination of metal and long fiber reinforced thermoplastics (LFT) offer a great opportunity to reduce component weight for automotive applications. Production costs are typically high due to complex processes and challenging end machining. A complete manufacturing process chain for simultaneous hybrid design production is presented. Hybrid forming, a manufacturing process combining metal sheet forming, LFT compression molding and bonding, was developed to manufacture an automotive lightweight component, finally end machined by innovative waterjet techniques. Besides optimizing waterjet end machining strategies, waterjet controlled-depth machining was used to remove LFT material selectively, resulting in increased hybrid production flexibility.

Additive manufacturing (AM) processes are characterized by a high degree of flexibility regarding the manufacture of complex geometries due to the tool-free layerwise build-up principle. A well-established AM method is Fused Deposition Modeling (FDM), in which the part is produced by melting thermoplastic material in an extrusion process. However, fully automated operation of FDM is not possible due to the usually manual removal of finished parts, which reduces the overall productivity of the process. To compensate for this disadvantage, the development and implementation of a system for automated part removal for FDM is presented in this paper. The system enables the removal of finished parts from the building platform and thus the start of new build-up processes without the need for human interaction or the removal of the entire building platform as described in existing approaches. By applying this system, the productivity of FDM could be increased significantly while also reducing the need for human presence in the context of COVID-19. © 2021 The Authors. Published by Elsevier B.V.

31st CIRP Design Conference 2021, CIRP Design 2021, online, 19 May 2021 - 21 May 2021; Procedia CIRP 100, 439-444 (2021). doi:10.1016/j.procir.2021.05.101 special issue: "31st CIRP Design Conference 2021 (CIRP Design 2021) / Edited by Eric Lutters", Published by Elsevier, Amsterdam [u.a.]

Flexibility and versatility in production put high demands on the planning, simulation and ramp-up of assembly systems and processes. For this purpose, a digital twin of a laser-based assembly assistance system for manual mounting tasks was developed, implemented and analyzed. A capability model was elaborated using quantitative and qualitative studies. The capability model was realized as a Unity 3D model. In the augmented reality implementation using the Microsoft HoloLens users can control the digital twin through hand gestures and voice control. The implementation shows that the digital twin of the laser-based assembly assistance supports the planning, simulation and training of employees at the shop floor.

The current market trends have focused on personalization, where products based on the same platform can have different variations so they can satisfy multiple market segments. Trying to keep up with these trends, industries have introduced hybrid Human Robot Collaborative stations that offer short throughput times, along with the flexibility to process different tasks. For this kind of flexibility to be achieved in production, a concept is examined for remotely reprograming industrial robots. As a result, a fully automated assembly line or a production station, could be repurposed to handle different products or processes, using the currently installed equipment with minor adjustments. The concept that is described in this paper presents a teleoperation – based method for process design and control of industrial robots utilizing Virtual Reality. The main aim is to reduce the time and effort required for repurposing the robot operation without the physical presence of a robot operator at the shop floor. A case study is presented to demonstrate the above described concept.

The development of sensitive robots is also accompanied by the implementation of open cell concepts. Contrary to the classic safety of robotic cells, separating protective barriers can be avoided with these concepts. For design engineers, this results in an increased design flexibility for the layout of robot cells. This paper addresses the disadvantages of open cell concepts and presents a method for real-time monitoring of end effectors of industrial robots. The method uses a sensor module at the robot’s flange to produce a reference record of distance values within the cell of the process. The recorded sensor data is compared with incoming sensor signals. This enables very short calculation times to evaluate the safety and thus minimizes the required safety distances. The validation of the concept is done by means of a simulation on the detection rate of the sensor module. Within the experiments an accuracy of about 87 % with a single shot data point could be achieved.

The concept of Lean Production System is applied in manufacturing systems around the globe. With the help of avoidance of waste on the shop floor and a holistic view of the manufacturing system, efficient processes are created. The concept of a Lean Production System with its principles and tools is influenced by a newer concept that is increasingly introduced in manufacturing systems as well, named Industry 4.0. Here, the connection of every resource, object, and human in real-time is aspired to handle future production problems. Such problems are, for example, the high demand for customized products that require a high degree of flexibility. Numerous researches address the combination of both concepts to create an efficient and also a flexible manufacturing system. However, an investigation of the relationships of these combinations is still missing. In this paper, the relationships of a method library, which links the concept of Lean Production System and Industry 4.0, are analyzed. Especially the causal and temporal relationships are examined. These relationships establish the interdependencies between the methods of Lean Production Systems and the technologies of Industry 4.0 and their implementation order.

Industry 4.0 is associated with numerous technologies, which offer great potential for optimizing linear winding processes as used in electric motor manufacturing. To further increase flexibility and quality in the production of mass products like coils, data-driven techniques such as machine learning (ML) are increasingly moving into focus. To generate the required data, however, linear winding machines must be equipped with suitable sensors. Accordingly, this paper will first present all known influencing and quality parameters in linear winding based on earlier research work. Based on this, suitable data sources and sensor systems are identified and a concept for applying ML techniques is derived.

Hybrid assembly systems combine the advantages of automated assembly solutions with the cognitive abilities of humans to make assembly more flexible while efficient. Adaptive systems in this context enlarge the scope in which the division of tasks between human and machine can be varied. These systems are an enabling factor for agile assembly since highly situation-aware technologies allow quick reaction in volatile assembly situations. This research focuses on the main opportunities and implementation obstacles of adaptive assembly systems in industrial practice. The results of an empirical study, focusing on cognitive worker assistance, with 132 participants from manufacturing companies, are presented in this paper. Main findings show that the introduction of adaptive worker assistance solutions requires extensive modifications of IT infrastructure. Besides an increase in flexibility, an enlarged potential for the improvement of worker training processes is expected.

In human-centered assembly systems, workers adapt to new and changing tasks based on learning processes. Human factors are crucial for the entire system capability in such kind of systems. Current methodologies of assembly scheduling often take only limited account of human factors and the interaction between assembly scheduling and workforce planning. Although extensive research indicates that a careful introduction of human factors implies many benefits, examples are reduction of errors, increasing flexibility and performance of the system as well as increasing human wellbeing. The developed methodology addresses the research gap in modeling the impact of human factors on assembly scheduling. In the paper at hand, human factors are systematically integrated into assembly scheduling. This is done by adapting the learn-forget curve model of Jaber and Bonney. In addition to experience, learning and forgetting, the age and the accordance of assembly operations are considered. Within the scheduling model of worker assignment, two coordination approaches are followed. On the one hand, skill-based assembly planning with a long-term perspective and on the other hand, time-based assembly planning with a short-term perspective is proposed. The developed mathematical model assists planners in the assignment of workers to workplaces in a manual assembly line by predicting performance based on human factors.

Manufacturing has evolved over the past few centuries, both in technological and managerial terms; for the latter, Lean Manufacturing (LM) has been widely used to maximize value for customers and minimize waste. The broad use of Information and Communication Technologies (ICT) in manufacturing forms the concept of Industry 4.0 (I4.0), connecting physical and digital systems and integrating value chains. Although with different approaches, LM and I4.0 share the same goals of increasing flexibility and productivity. In fact, two views on their interaction can be extracted from literature: LM serves as a basis for I4.0 and I4.0 improves LM effectiveness. The novelties presented by I4.0 comprise not only the technical scope, but also the behavioral one. It has been unclear, however, how I4.0 affects LM soft practices, especially when it comes to the skills required for Lean professionals to face this new environment. In this regard, this paper addresses the impact of I4.0 on the skills of Lean professionals. One could observe that in addition to technical skills linked to programming and data science, analytical and interpersonal skills make up the sociocultural skills necessary for the professional of the future in the context of I4.0.

The global competition requires from industries New Product Developments (NPD) with innovation, speed and flexibility, which has to be tightly connected with new manufacturing technologies, in order to achieve success and constant growth. Additive Manufacturing (AM) is an emerging technology from Industry 4.0 capable to address such challenges. It provides better and functional product development, promptly allowing running tests and reducing time to market, promoting agility, as a whole. Parallel evolution is in course regarding the theory of Agile Project Management (APM) applied to NPD, especially when conducting development of innovative products and technologies. However, to achieve the whole benefits with such new approaches towards the NPD process, it is necessary to study and understand how to combine AM and APM for improve NPD. The present research proposes an innovative framework to manage NPD with AM, incorporating APM envisioning innovative products, customer involvement and successfully obtaining a final product faster than conventional routes. The proposal is based on a rigorous Systematic Literature Review (SLR) method, which identified the most appropriate practices to be combined to promote a NPD with agility and flexibility when using AM. All concepts and practices were analyzed and systematically organized, generating the proposed framework. The result can be a reference model to companies conjointly adopt AM and APM.

The reduction of CO2 emissions has high priority in many parts of the world. It is achieved primarily through the expansion of renewable energies such as PV and wind power. However, they have the disadvantage of fluctuating over time. Energy flexibility measures (EFM) can provide the necessary compensation. One measure is the installation of bivalent and hybrid facilities. They can change their energy source to reduce energy costs, stabilize the electrical grid, and increase efficiency, e.g. for economic and ecological considerations, the waste heat potential and its utilization must be taken into account. EEP and Fraunhofer IPA have developed a method to evaluate various waste heat recovery technologies, such as thermal storage, ORC plants, and other technologies depending on their energy flexibility and their potential to reduce the facility’s energy costs. In addition, the change in the electricity to gas price ratio on the level of energy cost savings was examined in a parameter study. The developed method was applied to a bivalent furnace.

The era we live in today is full of uncertainty and varying customer demand. Firms are managing various difficulties including consistent change, shorter item life cycles, assorted client necessities and expanded vulnerability of interest. The purpose of this paper is to put forth the dimensions along which a firm can improve its performance and enhance its sustainability through supply chain agility. Supply chain agility is a key component of all successful firms today, be it in the services or in the manufacturing sector. It is believed that an agile company would tend to outshine its competitors, who have not adopted the agile principles. Seven dimensions of supply chain agility have been identified in this paper; and their effect on each other and on supply chain agility is assessed using Fuzzy DEMATEL. Four major factors- accessibility, alertness, flexibility and swiftness are identified as the key determinants of agility. It is found that the operational flexibility and analytical capabilities of a firm play the most crucial role in determining its agility as they had the maximum effect on the overall system. This study provides the management of companies with a clear guide map as to how they can improve their agility in today’s highly competitive market and how one dimension of supply chain agility has an impact on another dimension. Identification of dimensions of agility and evaluating their impact on each other as well as on supply chain agility using Fuzzy DEMATEL is a new and unique contribution to the concept of agility.

Battery cell manufacturing consists of several energy intensive processes. With the global demand for batteries rising, manufacturing companies continuously seek for new strategies to improve their competitive advantage, reduce costs and boost their environmental performance. This paper assesses high-volume battery cell manufacturing for potentials to increase energy flexibility. The flexible change of energy carriers in the drying of electrodes and the use of advanced production planning and scheduling as well as storing energy in the formation process are identified to achieve highest potentials. Results of this paper contribute to the design and operation of battery cell manufacturing plants.

The railway industry is moving towards a complex socio-technological system that relies on computer control and human-machine interfaces. Digitization and convergence of information technology (IT) with operational technology (OT) becomes increasingly important. Therefore, enabling innovations, lowering costs, increasing safety systems, enhancing performance, and increasing flexibility gains. In this scenario, opportunities arise for Augmented Reality (AR) capabilities to enhance maintenance operations of digitized trains. Initial research and tests have enabled training technicians and assistance of train drivers during specific activities by facilitating interactions with machines that go beyond traditional training manuals. Despite the positive results AR has shown, there are no specific indications available regarding the 'deployability' of the technology related to maintenance tasks. This position paper presents arguable boundaries and challenges for a structural appraisal of AR on maintenance operations of IT/OT converged trains. Potential AR solutions and specifications were based on current maintenance operations. Semi-structured interviews and surveys were conducted with maintenance providers to determine existing challenges. Moreover, the paper comprises an analysis to reveal the potential application AR has in maintenance procedures of digitized trains. Finally, this paper provides future research directions of AR technologies related to maintenance procedures of trains.

Modular system architectures in both product and manufacturing domains are of high interest in both academia and industry, as having modular product platforms and reconfigurable manufacturing systems are a key enabler for companies to obtain the strategic flexibility needed to respond to rapidly shifting market and customer requirements. Previous research has focused extensively on modularizing products and manufacturing equipment. However, to fully reap the benefits of modularization, this cannot be treated solely as engineering efforts taking place in one domain or cross domains with co-platforming, but rather as a top management initiative. The purpose of this research is to review state-of-the-art literature on modularization/platforming looking across managerial levels and business domains, market, product, and manufacturing.

Industry 4.0 has enabled technological integration of cyber physical systems and internet based communication in manufacturing value creation processes. As of now, many people use it as a collective term for advanced technologies, i.e. advanced robotics, artificial intelligence, machine learning, big data analytics, cloud computing, smart sensors, internet of things, augmented reality, etc. This substantially improves flexibility, quality, productivity, cost, and customer satisfaction by transforming existing centralized manufacturing systems towards digital and decentralized one. Despite having potential benefits of industry 4.0, the organizations are facing typical obstacles and challenges in adopting new technologies and successful implementation in their business models. This paper aims to identify potential barriers which may hinder the implementation of industry 4.0 in manufacturing organizations. The identified barriers, through comprehensive literature review and on the basis of opinions collected from industry experts, are: poor value-chain integration, cyber-security challenges, uncertainty about economic benefits, lack of adequate skills in workforce, high investment requirements, lack of infrastructure, jobs disruptions, challenges in data management and data quality, lack of secure standards and norms, and resistance to change. Interpretive Structural Modeling (ISM) is used to establish relationships among these barriers to develop a hierarchical model and MICMAC analysis for further classification of identified barriers for better understanding. An analysis of driving and dependence of the barriers may help in clear understanding of these for successful implementation of Industry 4.0 practices in the organizations.

Digital transformation potentially improves business processes, leading to increased flexibility in manufacturing and logistics networks. Therefore, decision support systems for responsive manufacturing are gaining importance. In this paper, a discrete event simulation model is applied. In order to assess the potential of digital transformation in the context of Industry 4.0 towards production and logistics network robustness, different simulation scenarios are observed. The results contribute to identify potential flexibility measures and to quantify their impact in order to mitigate control uncertainties within production and logistics networks.

In production environments, it is essential to anticipate the opportunities and challenges of new technology implementation trajectories. Often, companies face an inability to oversee the consequences on different aggregation levels, fields of expertise, perspectives and time-frames. This research presents an architecture to support simulation-based prevision of potential realities that comprehend results in a meaningful and perspective-dependent manner. The architecture specifically aims to ensure that the simulation benefits surpass the efforts and cost involved. The instant-controlled flexibility of reality-infused simulations leads to more insight in the effects of changes, while structuring the development trajectory and making it more predictable and deterministic.

Complex assembly tasks require increased flexibility and adaptability, as well as higher effort on the conventional (re)programing of robots. To solve such challenges, this paper presents a function block-enabled multimodal control scheme for symbiotic human-robot collaborative assembly. Data/event-driven function blocks with smart decision algorithms are used for human-centred robot control with multimodal fusion. Then, multimodal control commands in the form of haptics, gesture and voice are defined as the inputs of the function blocks to trigger task execution. This novel scheme facilitates the implementation of the multimodal symbiotic human-robot collaborative assembly with enhanced stability and flexibility.

The increasing complexity of production processes, shortening product lifecycles and rising energy costs lead to an emerging need for an efficiency optimization approach including process simulations in factory adaption planning as well as in process planning. This paper presents a method for the design of milling processes by analyzing process times, energy consumption and process stability based on process simulations. By evaluating simplified virtually conducted processes, reasonable parameter value combinations can be selected efficiently and provide flexibility by generating multiple solutions. Using detailed process simulations and conducting iterative adjustments to ensure a sufficient workpiece quality, the selected parameter sets are transferred to actual NC processes.

Due to the high demand placed on today’s production systems, such as high flexibility or low cost and time requirements, simulations tools continue to gain more importance. With the help of simulation tools, complex production systems can be designed as well as examined in terms of feasibility before their actual physical existence in order to make them controllable and to avoid possible cost-intensive errors. However, creating and implementing a simulation model manually takes a considerable amount of time and expert knowledge regarding the used simulation tool. A promising approach for this is to automate the implementation of simulation models using existing data about product, processes and resources (PPR-Triple). Therefore, the simulation model should represent the layout as well as the executed processes and workflows. For this purpose, the presented paper focuses on the automatic workflow and process transfer to the 3D simulation environment of Visual Components. For the representation of the imported layout and process data AutomationML (AML) is extended and utilized as a data exchange format.

The next generation of manufacturing system calls for feasible solutions with high efficiency and flexibility. Thus in recent years, Human-Robot Collaboration (HRC) research attracts many attentions worldwide since it unites the repeatability and accuracy of robots and the adaptivity and intelligence of human operators. In this paper, the system architecture of an HRC solution is presented, together with the novel method of vision sensing and controlling towards a smart solution with high accuracy and fast speed. The proposed method is demonstrated through implementations in the real robot cell, and evaluated by quantifiable measurements. Future research outlooks are addressed at the end of the paper.

Value stream design (VSD) is the leading method in lean manufacturing for planning production systems. Preliminary research showed that promising logistical alternatives are not adequately addressed in VSD. Therefore, a framework for planning these alternatives was developed. Possible VSD alternatives occur for shipping principles, process linkages, pacemaker positioning as well as different transport and process designs. Promising design alternatives are simulated and evaluated to find the best VSD. The paper presents findings based on applications of the approach in industry. It is shown when and how logistic decisions can affect key performance indicators such as costs, throughput times, degree of flow and flexibility parameters in practical application.

Looking back on the past decade, a steadily growing demand for integrity and shorter development cycles as well as higher flexibility and effectiveness can be observed in both product development and production. This trend continues to this day. Products, as well as their manufacturing and development processes, are becoming more and more complex due to the digital transformation and the new technical possibilities of Industry 4.0, which customers want to exploit to the full. Recently, the trend has become apparent that in the future, customers will be more integrated into product development processes with the help of Industry 4.0. In order to achieve this and to keep their economic strength, companies are striving for increased product and production flexibility to be able to offer products with batch size 1 for the price of series products on the market. In recent years, there have been countless scientific research efforts around Industry 4.0, the integration of the development of production processes into product development processes and the development of technical and methodological approaches to increase flexibility. This publication presents a theoretical approach of a method that can be used to evaluate product and production flexibility of a company on a superior level by independent application. The aim is to provide the user with a tool for determining the maturity of the company with regard to the existing and future increasing demands on flexibility, which he can apply without great technical, monetary and time expenditure.

The great flexibility provided by generative manufacturing offers immense potential for the structural component optimization. However, this freedom needs to be utilized by appropriate CAx tools. In this paper, we present a novel slicing method, enabling the automatic generation of topology-optimized Laminated Layer Manufacturing (LLM) parts. With this method a part of arbitrary shape can be automatically sliced and equipped with a uniform virtual grid that is suitable for manufacturing. The optimal density distribution is then mapped onto the individual slices and locally approximated through the introduction of variably shaped cavities. By using 3D modeling techniques all LLM sheets are modeled parametrically and assembled automatically. To verify the functionality of the presented method, it is applied to two practical parts and compared with a conventional topology optimization method.

Increased product customization based on customer requirements results in a large number of variants and thus smaller batch sizes in production. In order to maintain a flexible and dynamic interlinkage within a production system in terms of industry 4.0, it is necessary to implement flexible and adaptive handling and gripping solutions into the production process. A suitable way of integrating more flexibility into production is the use of compliant soft-robotic gripping systems, which are characterized by a high adaptability in terms of workpieces’ shape, size and structure compared to state-of-the-art grippers. Current focus in research refers on design and simulation of the repeatability of soft-robotic grippers with low payload capability, such as silicone-based Soft-Pneumatic-Actuators (SPA). Design and process parameters of soft-robotic grippers with higher payload capability, such as Fin Ray®, are usually revealed through time-consuming empirical test series. This paper demonstrates the potential of a new simulation-based approach in designing soft-robotic grippers for heavy-duty applications to reveal crucial design and process parameters with low effort and without empirical test series.

In the automotive industry, the ”completely knocked down” (CKD) business requires the identification and verification of a large number of unmarked parts. This is often a manual activity with a high risk of failure. Deep learning methods offer flexibility and a useful distinction between object classes for the automatic classification. However, these methods require large amounts of labeled training data and are sometimes inaccurate. In comparison, classical algorithms can achieve higher accuracy, but are inflexible and therefore unsuitable. This paper presents an approach to increase process reliability while alleviating the data labeling effort by using synthetic training data from spatially scanned and virtualized parts and combining deep learning and classical algorithms. The result is a flexible, sensor-independent, and autonomously trained classification system.

The individual consumer mobility behavior is rapidly changing with “shared mobility” concepts set to shift the market towards on-demand fit-for-purpose mobility solutions. The increasing connectivity of automobiles combined with the shorter innovation cycles especially in software-based systems requires cars to be upgradable in order to fulfill the consumers’ expectations for the newest technology. This leads to shorter product life cycles, increased variety and volatile sales figures, which the automotive manufacturer must react to quickly and flexibly in order to maintain a competitive edge. With the rise of reconfigurable and rescaling production methods like the matrix production, corresponding material supply concepts fulfilling the required flexibility need to be developed. This paper develops a scheme, which comprehensibly derives design concepts for the intralogistics material supply in matrix productions. It concludes with an automotive use case of the presented design concept at the ARENA2036 research campus where new type flexible production methods are developed and validated in cooperation with partners from industry and research.

More understanding about ways to consider flexibility during design processes is needed. This also demands reconsideration of research approaches for studying design processes of flexible assembly systems to develop knowledge. To assist efforts to meet these needs, a participatory research approach that facilitates elucidation of design processes of assembly systems and their relations to flexibility is presented. The approach has been applied in a heavy-duty vehicle manufacturing firm, resulting in both theoretical and practical contributions of participatory research.

Due to increasing global competition, the demand for more individualization and constantly changing requirements, the importance of time to market and a high degree of flexibility in product development is increasing. New manufacturing technologies as well as improved methodical approaches in product development can be used for this purpose. The additive manufacturing industry has developed strongly in recent years and the possibilities of using this technology for final components are constantly increasing. At the same time, there are many efforts that contribute to identifying and implementing the potential of this manufacturing technology during product development. Other approaches, such as agile product development from software industry, are applied at the organizational level and also try to tackle the aforementioned challenges. This contribution therefore presents an approach using agile product development methods to develop components for additive manufacturing. The potentials of additive manufacturing are specifically used (e.g. tool-free manufacturing) to enable the principles of agile development also for physical products. The Design Pattern Matrix as an opportunistic approach is applied to systematically link the potentials of additive manufacturing with the functions of the product. Therefore, design elements are conducted from additive manufacturing case studies and provided to design engineers during the development. Furthermore, already known methods of design for additive manufacturing are classified into the procedure of the agile framework scrum.

In today’s production environment, change is an integral part of all aspects of production planning and control. Adaptable cell-oriented assembly systems can be equally efficient as conventional assembly lines. While, the former provides significantly more flexibility, contributing to the inevitable transition to more productive and variant-oriented assembly. This paper presents a novel concept for planning and controlling of modular and adaptable assembly systems. The approach utilizes an agent-based simulation model designed by embedding and orchestrating intelligence into assembly modules such as products, workstations, materials and tools. As a result, this leads to higher autonomy in communication among all system entities and allows more configuration possibilities and greater flexibility in the control of assembly flows and system performance, respectively.

Industry 4.0 was introduced with the hope of transitioning to the next industrial revolution. I4.0 is a vision of increasing flexibility and efficiency within a system of low complexity. Despite this intention, Industry 4.0 has required users to understand complex information and communication systems, limiting usability and therefore not necessarily satisfying the original vision. However, application of principles from the Lean Production Systems originally developed in the 1930’s in combination with Industry 4.0, may help to reach its desired goals. Through a literature review, this paper discusses the current state of art of Industry 4.0 and some Lean Production Systems principles. The paper focuses on a model combining Lean Production Systems with Industry 4.0, that was created in response to the shortcomings of the anticipated industrial revolution. With a Lean Production System as a foundation, the six design principles of I4.0 of interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity can be better implemented to reach the vision of highly flexible and low complexity systems. In return, horizontal and vertical integration within Industry 4.0 can support and accelerate Lean principles by reducing human error and increasing intercommunications between the systems, resulting in a productive integration. The specific principles of Lean that could boost Industry 4.0 will be discussed, demonstrating why a company should be Lean, meaning value adding processes and stakeholder orientation are at its core.

The interaction of human and machine in assembly, as applied in collaborative robotic systems, offers a great potential for a higher flexibility of production systems and the individualization of assembly operations. In order to make collaborative assembly systems save for workers that are not trained in programming and operating robotic systems, an applicable solution for the commissioning has to be implemented. This paper shows a novel concept for the virtual commissioning of human-robot assembly systems, using a human in the loop simulation that is connected to a physical robot simulation.

In the past few decades, competition has increased between organizations as a result of globalization and fast development in IT. Companies these days are continuously looking to expand their market geographically to attract new customers. With that in mind, the main concern of companies is to achieve their customers’ requirements, which makes the supply chain (SC) longer and more complex. This leads to increased difficulty in managing the SC along with controlling risks and disruptions in SC. Such as loss of a critical supplier, a fire accident in the production facility, or an act of terrorism. To deal with these risks, SC must be designed to provide an efficient and effective response, keep its process working and be capable of recovering to their original state after disruptive events, this is considered as the core of supply chain resilience (SCRes). Furthermore, companies that design their SC with capabilities to react quickly to any disruptions in its process have the opportunity to become more stabilized and acquire an improved position in the market. Researchers have been exploring ways to acquire supply chain resilience, such as encouraging and improving collaboration between SC partners or by increasing the organization’s visibility through monitoring SC events and patterns. Studies have shown that IT has a role in improving SCRes, such as information-sharing systems to promote collaboration and visibility tactics. However, recent research trends are exploring new and emerging technologies like the Internet of Things (IoT). Despite the growing interest in IoT, minimal research has been carried out for its application in SCRes. Research in this direction is necessary to explore opportunities provided by IoT to redesign SC which in turn reinforces flexibility of supply chains and aspects of analysis of the product quality and how it could enable companies to improve their SCRes. This study is directly focused to identify and highlight this gap.

53rd CIRP Conference on Manufacturing Systems 2020, CIRP CMS 2020, online, 1 Jul 2020 - 3 Jul 2020; Procedia CIRP 93, 1073-1078 (2020). doi:10.1016/j.procir.2020.04.072 special issue: "53rd CIRP Conference on Manufacturing Systems 2020 : 53rd CIRP Conference on Manufacturing Systems / Edited by Robert X. Gao, Kornel Ehmann", Published by Elsevier, Amsterdam [u.a.]

The manufacturing industry faces immense challenges for maintaining and increasing their productivity and flexibility. In this context, it is important for companies to ensure that their employees have the relevant data, information and knowledge necessary to make well-informed decisions. Due to recent development with Industry 4.0 enabling technologies that create new possibilities, the amount of available data, information and knowledge increase rapidly, but the insights into how to utilize it to its full potential are still lacking. In this paper, a human-centred perspective has been applied, aiming at improving how to cognitively support humans at work with new Industry 4.0 enabling technologies. Heavy emphasis is placed on people’s requirements and preferences of data, information and knowledge for enhancing their performance and satisfaction at work. This paper examines the relationship between existing literature on dissemination of data, information and knowledge within the manufacturing industry with state-of-the-art research on Industry 4.0. The outcome of the research recognizes the increased importance of utilizing data, information and knowledge for people at work, facilitated by exploiting the new possibilities from Industry 4.0. To accomplish this, it is concluded that there exists an urgency to design: both a holistic framework for identifying and accommodating individuals’ needs and expectations of relevant data, information and knowledge; and demonstrators and concepts to simplify the implementation of Industry 4.0 enabling technologies that support the aforementioned dissemination of data, information and knowledge.

In the context of product service systems and smart services, the huge number of possible variants and dynamic market requirements present a challenge for service providers who want to stay profitable. The combination of variety and dynamic is called the complexity challenge. Among the main difficulty is to respond to the market’s external variety and dynamic with the right flexibility without losing efficiency or effectiveness. This paper, motivated by a case study, focuses on selected difficulties posed by complex markets on the portfolio, product, process and resource level. It introduces a qualitative and quantitative model to manage a company’s internal complexity according to the external complexity on all mentioned levels. In the introduction, the most relevant challenges posed by complex markets will be explained in detail. The second section covers the understanding of complexity management as a control loop. The third section then introduces a set of key figures that can be used to evaluate a company’s internal complexity on a quantitative level. In the fourth section, a curve model that aims at representing the qualitative interconnections between these key figures to gain control of the complexity in product service systems and smart services will be presented.

This paper discusses the performance measurement of an envisaged flexible production station, where automation devices, updateable tools and robotized resources have been introduced, that has been implemented in laboratory environment. The production station has been inspired by an existing one from the consumer goods industry, where hard automation solutions have been selected to achieve high productivity times. The aim of this paper is to prove how the combination of the aforementioned technologies can improve the reconfigurability and adaptability levels of an assembly station. In order to achieve this, a number of time values has been extracted from the real demonstrator, such as manipulation time, recognition time and transportation time of parts. Additionally, production information such as the number of failures after a successful number of assemblies has been measured as well. The design of the proposed system as well as the above information have been added in simulation environment and more specifically in the Witness Horizon Simulation tool in order to simulate its function using discrete event simulation approach so that the response of the suggested system is clear. After the simulation process, conclusions have been reached for the long-term behavior of the suggested flexible station, highlighting its advantages in terms of flexibility and adaptability, compared with the existing, hard-automated station that has already been installed in a consumer goods industrial production line.

Modelling of cutting forces in milling is a key aspect for an accurate simulation of the process. For fast and reliable simulation with 3 axis milling machines, macroscopic approach is often used. The tool is divided in slices around its axis and a mechanistic model is used to predict the elementary cutting forces. In robotic machining, the tool can experience more complex toolpath and its axis may be tilted during machining due to the flexibility of the robot. The paper presents an improvement of the cutting forces model allowing the simulation for cases where the degrees of freedom in rotation of the axis are freed.