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Manufacturing companies operate in increasingly turbulent and discontinuous environments. Uncertain projections regarding future sales, large volume fluctuations, the steady advance of new technologies, and ever-shorter product life cycles, combined at the same time with increasing product variety, demand the constant adjustment of production structures to meet the current market requirements. Uncertainty in the production area requires a continuous transformation of production structures to meet the current requirements on the market. This poses a challenge for manufacturers in the various branches of industry and creates an ever-increasing need for flexibility to find an economic balance, in times of tight budgets, between the prevailing market uncertainties and the correct arrangement of the production systems. In the following document, the applied experience is presented, extracted from the flexibility assessment tool economical flexibility measurement (ecoFLEX). It will be shown along with the practical experience gained in its application at a car-engines plant. [ABSTRACT FROM PUBLISHER]

The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities.

The paper highlights events of manufacturing’s evolution and metamorphosis over a period of 60 years (as noted by the first author) and offers some observations on key developments and activities. It includes the reflections of professional activities of the first author and discussions of how manufacturing is evolving to accommodate the current and expected trends in manufacturing technology. Three specific software environments relative to developments in the design-to-manufacturing cycle are described. These lead into a perspective of what may now lie ahead, relative to enterprise issues, human system interactions, and the fully integrated “digital factory.”

Current requirements which are connected with designing new, or reali-zing the existing production systems, cause continuous and rapid changes in products and processes. This means that classical approaches of pro-duction systems design have to be extended by advanced technologies and methods, such as digital factory, virtual and augmented reality, computer simulation, reverse engineering, etc. The article describes new computer technologies, which were applied in production practice, with a particular focus on the augmented reality technology.

The study of the Digital Factory consists of the design of an automated manufacturing system with elements of autonomy within the concept of Industry 4.0. This study origin was inspired with the production of drumsticks. For the intended purposes, manufacturing system designs for Factories of Future are created. The system consists of elements that are part of the digital production with automated functions that serve to communicate and interconnect the equipment located inside the factory as well as its external parts - buildings. Together, they should create a comprehensive set of automated elements and autonomous production in Smart Future Factories. [ABSTRACT FROM AUTHOR]

In the current trend of smart manufacturing, the architecture of smart manufacturing is gradually proposed by many countries in the world. Main components, elements, functions, and relationships of these smart manufacturing architectures have been clarified. However, the specific details of cross matching of different dimensions in these smart manufacturing architectures have not been thoroughly developed. Therefore, a certain part of a plane intersected of system hierarchy dimension and smart characteristics dimension in the model for smart manufacturing (SMS) from a perspective of implementation was selected for detailed study, which is defined as smart manufacturing implementation system (SMIS). Firstly, a top-level scenario planning of SMIS was proposed from perspectives of lean factory, interconnection factory, information factory, transparent factory, virtual factory, and smart factory. Then, an overall framework of SMIS was deduced through a top-level scenario planning of SMIS. Finally, through a case study, the specific implementation process of SMIS is obtained. Through the proposed overall framework and implementation path of SMIS, enterprises can implement intelligent manufacturing step by step. In addition, the top-level scenario planning and overall framework of SMIS are complementary to the SMS proposed by China. [ABSTRACT FROM AUTHOR]

The subject of this work is layout planning of machinery, especially for Flexible Manufacturing Systems (FMS), using Augmented Reality tools. The goal is for the user to evaluate suggested layouts by taking into account non-measurable factors, such as operator experience, empirical or non-tacit knowledge and on-site impression. By reference to existing machinery (in the case study: CNC lathe and an industrial robot) the missing elements of a complete FMS cell (conveyor belts, automatic storage and retrieval system, other robots and CNC machines etc.) are super-imposed. The functional connection is enabled between the collaborating real-physical and the virtual machinery of the layout in the most convincing way, i.e., by simulation of the full production process involving movement, manipulation and processing of parts by real and virtual equipment in parallel and serial co-existence. This involves predefined scenarios that can be experienced exploiting alternative equipment in the same application and alternative layouts assessed in analogous applications. Static and dynamic simulation of the manufacturing cell offers the possibility of extensive analysis of the selected layout by walk-through navigation. The application is implemented in ARKit™ API tool and Unity3D™. [ABSTRACT FROM AUTHOR]

Replying to requests for quotation in a fast and effective way is a key need for technology providers, in particular machine tool builders and system integrators. Digital factory technologies provide the opportunity of speeding up the generation of technical offers through the development of a digital twin of the system under study, thus enabling the assessment of different candidate configurations and the associated performance. In this paper we present a set of integrated digital tools to support the design of roll shop plants, i.e. plants dedicated to grinding cylinders for rolling mills. These digital tools are aimed to engineers and provide a configuration workflow, a 3D environment and performance evaluation tools. The interoperability among the software modules and the reuse of knowledge is enhanced by semantic web technologies and the definition of a common data model as an ontology relying on technical standards. [ABSTRACT FROM AUTHOR]

In the tide of smart manufacturing in the world, many countries have put forward their own reference frameworks for smart manufacturing system. Based on the framework of smart manufacturing system (SMS) proposed by China, a reference smart manufacturing integration system (SMIS) from multi-level and multi-perspective was proposed. The reference model of SMIS was analyzed by means of system engineering. Based on this model, the overall architecture of SMIS was proposed. Then, the physics subsystem, information integration subsystem, network integration subsystem, data integration subsystem, and visualization integration subsystem for SMIS were proposed through the overall architecture of SMIS. Finally, the above integrated subsystems were collected together to deduce the implementation path of SMIS. Four reference subsystems for SMIS proposed in this paper have achieved good effects in the projects we participated in, which were organized by the state. The research results of this paper can be used as a reference for industry and government to design, set, and carry out SMIS. At the same time, it has a certain reference value for the improvement and supplement of national smart manufacturing system architecture. [ABSTRACT FROM AUTHOR]

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing. [ABSTRACT FROM AUTHOR]

Digitalization of manufacturing is once again on the industry application research agenda and Digital Manufacturing plays a fundamental role in this process. However, there is a lack of commonality in the literature about the purpose of Digital Manufacturing. The purpose of this paper is to analyze the concept and application domain of Digital Manufacturing considering the increasingly established Industry 4.0 paradigm. Based on a content analysis concepts are framed, and new technological characteristics identified. The paper contributes to a better understanding of the future challenges that companies face by positioning Digital Manufacturing conceptually and delimiting its application domain. [ABSTRACT FROM AUTHOR]

Taking smart manufacturing as a strategy for industrial development, China has put forward a people-oriented policy and launched a series of plans for smart manufacturing talent education (SMTE). The demand for smart manufacturing talents in ten priority areas and the industrial applications in China is very huge. Therefore, in this paper, a reference system of SMTE in China is presented, which includes discipline system, training system, practice system, and assessment system. In order to further refine the architecture of smart manufacturing system, a reference course system was proposed; the system contains seven layers, which are basic layer, technique layer, implementation layer, management layer, platform layer, application layer, and industrialization layer. Finally, nine stakeholders of the common operation body were investigated, and a reference implementation of SMTE in China was put forward. In this paper, the smart manufacturing talent education reference system, reference model, and related reference subsystems can be a very useful guideline for Chinese industry and education to design, set, and carry out the smart manufacturing talent education system. At the same time, the system has its reference value for the improvement of China's smart manufacturing system architecture. [ABSTRACT FROM AUTHOR]

Abstract Within product development process data and information management is an essential task. Data and information are generated at every stage of this process and stored in software tools used to generate that data. For large scale manufacturing systems this leads to manual work, which is error-prone. This issue intensifies due to increasing influence of computer science on those systems (e.g. Industrie 4.0), leading to a rise of complexity. New approaches are needed for a better information flow. This contribution introduces an approach of an evolving Digital Factory containing and visualizing all generated information based on an open standard meta model. [ABSTRACT FROM AUTHOR]

One of the new challenges of the 21st century is the Industry 4.0. Manufacturing companies moving away from mass production and getting closer to customized production and manufacturing of customized products through digitization. The expectations are high, meeting the requirements is a real challenge to industrial partners. In order to help meet the challenges the University of Pannonia Nagykanizsa Campus started to establish a fully automatized industrial laboratory. In this paper the architecture of the Industry 4.0 laboratory and the purpose of the Factory Subsystem is presented. [ABSTRACT FROM AUTHOR]

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.

The fourth industrial revolution is characterised by the increased use of digital tools, allowing for the virtual representation of a real production environment at different levels, from the entire production plant to a single machine or a specific process or operation. In this framework, Digital Factory technologies, based on the employment of digital modelling and simulation tools, can be used for short-term analysis and validation of production control strategies or for medium term production planning or production system design/redesign. In this research work, a Digital Factory methodology is proposed to support the enhancement of an existing manufacturing cell for the fabrication of aircraft engine turbine vanes via robotic automation of its deburring station. To configure and verify the correct layout of the upgraded manufacturing cell with the aim to increase its performance in terms of resource utilization and throughput time, 3D Motion Simulation and Discrete Event Simulation are jointly employed for the modeling and simulation of different cell settings for proper layout configuration, safe motion planning and resource utilization improvement. Validation of the simulation model is carried out by collecting actual data from the physical reconfigured manufacturing cell and comparing these data to the model forecast with the aim to adapt the digital model accordingly to closely represent the physical manufacturing system.