- Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030
- Industry 4.0 and the current status as well as future prospects on logistics
- A big data enabled load-balancing control for smart manufacturing of Industry 4.0
- Distributed maintenance planning in manufacturing industries
- Additive manufacturing scenarios for distributed production of spare parts
- Towards a lean automation interface for workstations
- Using autonomous intelligence to build a smart shop floor
- Sustainability aspects of a digitalized industry – A comparative study from China and Germany
- Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems
- Understanding data heterogeneity in the context of cyber-physical systems integration
1. Chen T, Tsai H R. Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. [J]. Robotics and Computer-Integrated Manufacturing, 2017, 45: 126-132.
ABSTRACT: Despite extensive research on future manufacturing and the forthcoming fourth industrial revolution (implying extensive digitalisation), there is a lack of understanding regarding the specific changes that can be expected for maintenance organisations. Therefore, developing scenarios for future maintenance is needed to define long-term strategies for the realisation of digitalised manufacturing. This empirical Delphi-based scenario planning study is the first within the maintenance realm, examining a total of 34 projections about potential changes to the internal and external environment of maintenance organisations, considering both hard (technological) and soft (social) dimensions. The paper describes a probable future of maintenance organisations in digitalised manufacturing in the year 2030, based on an extensive three-round Delphi survey with 25 maintenance experts at strategic level from the largest companies within the Swedish manufacturing industry. In particular, the study contributes with development of probable as well as wildcard scenarios for future maintenance. This includes e.g. advancement of data analytics, increased emphasis on education and training, novel principles for maintenance planning with a systems perspective, and stronger environmental legislation and standards. The scenarios may serve as direct input to strategic development in industrial maintenance organisations and are expected to substantially improve preparedness to the changes brought by digitalised manufacturing.
2. Hofmann, E. and Rüsch, M., 2017. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, pp.23-34.
ABSTRACT: Industry 4.0, referred to as the “Fourth Industrial Revolution”, also known as “smart manufacturing”, “industrial internet” or “integrated industry”, is currently a much-discussed topic that supposedly has the potential to affect entire industries by transforming the way goods are designed, manufactured, delivered and payed. This paper seeks to discuss the opportunities of Industry 4.0 in the context of logistics management, since implications are expected in this field. The authors pursue the goal of shedding light on the young and mostly undiscovered topic of Industry 4.0 in the context of logistics management, thus following a conceptual research approach. At first, a logistics-oriented Industry 4.0 application model as well as the core components of Industry 4.0 are presented. Different logistics scenarios illustrate potential implications in a practice-oriented manner and are discussed with industrial experts. The studies reveal opportunities in terms of decentralisation, self-regulation and efficiency. Moreover, it becomes apparent that the concept of Industry 4.0 still lacks a clear understanding and is not fully established in practice yet. The investigations demonstrate potential Industry 4.0 implications in the context of Just-in-Time/Just-in-Sequence and cross-company Kanban systems in a precise manner. Practitioners could use the described scenarios as a reference to foster their own Industry 4.0 initiatives, with respect to logistics management.
3. Li, D., Tang, H., Wang, S. and Liu, C., 2017. A big data enabled load-balancing control for smart manufacturing of Industry 4.0. Cluster Computing, pp.1-10.
ABSTRACT: The concept of “Industry 4.0” that covers the topics of Internet of Things, cyber-physical system, and smart manufacturing, is a result of increasing demand of mass customized manufacturing. In this paper, a smart manufacturing framework of Industry 4.0 is presented. In the proposed framework, the shop-floor entities (machines, conveyers, etc.), the smart products and the cloud can communicate and negotiate interactively through networks. The shop-floor entities can be considered as agents based on the theory of multi-agent system. These agents implement dynamic reconfiguration in a collaborative manner to achieve agility and flexibility. However, without global coordination, problems such as load-unbalance and inefficiency may occur due to different abilities and performances of agents. Therefore, the intelligent evaluation and control algorithms are proposed to reduce the load-unbalance with the assistance of big data feedback. The experimental results indicate that the presented algorithms can easily be deployed in smart manufacturing system and can improve both load-balance and efficiency.
4. Upasani, K., Bakshi, M., Pandhare, V. and Lad, B.K., 2017. Distributed maintenance planning in manufacturing industries. Computers & Industrial Engineering, 108, pp.1-14.
ABSTRACT: The combination of sensors and computing infrastructure is becoming increasingly pervasive on the industry shop-floor. Such developments are enabling the automation of more and more industrial practices, and are driving the need to replace conventional planning techniques with schemes that can utilize the capabilities of Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoT). The future is a place where intelligence is endowed to every entity on the shop floor, and to realize this vision, it is necessary to develop new schemes that can unlock the potential of decentralized data observation and decision-making. Maintenance planning is one such decision-making activity that has evolved over the years to make production more efficient by reducing unplanned downtime and improving product quality. In this work, a distributed algorithm is developed that performs intelligent maintenance planning for identical parallel multi-component machines in a job-shop manufacturing scenario. The algorithm design fits intuitively into the CPS-IIoT paradigm without exacting any additional infrastructure, and is a demonstration of how the paradigm can be effectively deployed. Due to the decentralized nature of the algorithm, its runtime scales with complexity of the problem in terms of number of machines; and the runtime for complex cases is of only a few minutes. The supremacy of the devised algorithm is demonstrated over conventional centralized heuristics such as Memetic Algorithm and Particle Swarm Optimization.
5. Durão, L.F.C., Christ, A., Zancul, E., Anderl, R. and Schützer, K., 2017. Additive manufacturing scenarios for distributed production of spare parts. The International Journal of Advanced Manufacturing Technology, pp.1-12.
ABSTRACT: Spare parts manufacturing and in-time provision are complex activities for several industries. One of the decisions that need to be made on a spare parts production is related to the location of the production. The distributed manufacturing of spare parts in locations closer to the final user may have several advantages, such as reduced delivery lead times and reduced logistics costs. However, distributed manufacturing by the adoption of advanced manufacturing technologies raises challenges in terms of information exchange, communication, and control between the production sites. The connected industrial environment, brought by what has been called the 4th Industrial Revolution, might be the answer for this challenge. Therefore, the aim of this paper is to characterize centralization and independence levels between a central factory and a distributed production site for the manufacturing of spare parts leveraging additive manufacturing as main production process. Use cases have been developed with design and engineering—providing the product model—in Germany and the additive manufacturing (AM) site—providing the manufacturing structure and machines—in Brazil, together forming a distributed development and manufacturing network. Four implemented use cases demonstrate the evolution of the independence level between the central factory and the distributed site. The analyses focus on implications for work organization, network performance, and intellectual property protection. Results show that the connection, communication, and control brought by advanced manufacturing technologies and connected industrial environment to distributed manufacturing change the organizational structure of both sites creating a flexible focused factory with the production closer to the final client and the specialization centered at the central factory.
6. Kolberg, D., Knobloch, J. and Zühlke, D., 2017. Towards a lean automation interface for workstations. International Journal of Production Research, 55(10), pp.2845-2856.
ABSTRACT: Methods and principles of Lean Production have become the major concepts to create highly efficient processes since the early 1990s. Due to its high effectiveness by reducing complexity and focusing on value-adding tasks, the Lean concept is still successful. Nevertheless, its changeability to produce highly customised products is limited. Industry 4.0 describes the vision of a smart production which can meet these future market requirements. Enablers are innovative information and communication technologies and the integration of all production entities into a common digital network. Lean Automation is the application of Industry 4.0 technologies to Lean Production methods in order to combine benefits from both domains. First proprietary Lean Automation solutions exist, but to enhance changeability in production, a common, unified communication interface is required. This paper presents the ongoing work towards an interface for digitising Lean Production methods using Cyber Physical Systems.
7. Tang, D., Zheng, K., Zhang, H., Sang, Z., Zhang, Z., Xu, C., Espinosa-Oviedo, J.A., Vargas-Solar, G. and Zechinelli-Martini, J.L., 2016. Using autonomous intelligence to build a smart shop floor. Procedia CIRP, 56, pp.354-359.
ABSTRACT: The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method.
8. Beier, G., Niehoff, S., Ziems, T. and Xue, B., 2017. Sustainability aspects of a digitalized industry–A comparative study from China and Germany. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(2), pp.227-234.
ABSTRACT: Industrial production is currently undergoing a fundamental transformation, leading towards a digitalized and interconnected industrial production, which is subsumed under the term Industrial Internet (of Things) or Industrie 4.0. This paper discusses the changes that digitalization is expected to bring about in the industrial sector by comparing a highly industrialized (Germany) with a major emerging industrial economy (China). We conducted two empirical surveys asking manufacturing companies from different sectors in Germany and China respectively, how they expect the digitalization of their processes will affect them. Both questionnaires addressed the future of work in production and the future of production itself. The main contribution of this paper is its empirical investigation of how the digitalization of industry is likely to affect sustainability aspects of manufacturing companies in two countries with very different industrial structures. Our findings suggest that this transformation will not only impact the ecological dimension (resource efficiency, renewable energy), but that the technical transformation is likely to be accompanied by social transformations. The findings of this paper will help decision-makers in the political sphere to anticipate and shape pathways towards a more sustainable future in the industrial sector.
9. Adamson, G., Wang, L. and Moore, P., 2017. Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems. Journal of manufacturing systems, 43, pp.305-315.
ABSTRACT: Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite. In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.
10. Jirkovský, V., Obitko, M. and Mařík, V., 2017. Understanding Data Heterogeneity in the Context of Cyber-Physical Systems Integration. IEEE Transactions on Industrial Informatics, 13(2), pp.660-667.
ABSTRACT: The current gradual adoption of the Industry 4.0 is the research trend that includes more intensive utilization of cyber-physical systems (CPSs). The computerization of manufacturing will bring many advantages but it is needed to face the heterogeneity problem during an integration of various CPSs for enabling this progress. In this paper, we describe various types of heterogeneity with emphasis to a semantic heterogeneity. The CPSs integration problem is classified into two different challenges. Next, we introduce the approach and the implementation of the semantic heterogeneity reduction with the focus on using Semantic Web technologies for a data integration. Then, the Big Data approach is described for facilitating the implementation. Finally, the possible solution is demonstrated on our proposed semantic Big Data historian.