Jakub Pizoń

Bio: Jakub Pizoń is an academic researcher from Lublin University of Technology. The author has contributed to research in topics: Cloud computing & Industry 4.0. The author has an hindex of 3, co-authored 7 publications receiving 22 citations.

Perspectives for Fog Computing in Manufacturing

Abstract: This article discusses ongoing efforts to enable the fog computing vision in manufacturing. As a new paradigm of computing implementation of fog computing faces many challenges that open perspective of new applications within a field of manufacturing. It is expected that fog computing will be one of factors that will accelerate development of in forth industrial revolution. In this article we discuss the perspectives of manufacturing companies surrounded by new solutions of CPS, CPPS and CM in relation

Matrix profile implementation perspective in Industrial Internet of Things production maintenance application

Abstract: The matrix profile processing is considered for the implementation of production maintenance tasks in the context of data acquisition by industrial Internet of Things solutions. The prospective implementation of the matrix profile data structure is verified through a dedicated case study, presenting the method of processing non-labelled data registered by sensors. The case study demonstrates the functionality of the profile and indicates the prospects of their applications in the field of production maintenance.

Key role and potential of Industrial Internet of Things (IIoT) in modern production monitoring applications

Abstract: The following paper presents a key role and potential of Industrial Internet of Things (IIoT) in industrial applications as a solution for monitoring and maintaining manufacturing assets. IIoT is particularly important due to progressing computerisation of hardware resources leading to development of a virtualised model of autonomous real-time production management. Adequately article presents case study of IIoT use in production environment – both methodical and analytic approach is presented.

Cobots Implementation in the Era of Industry 5.0 Using Modern Business and Management Solutions

Abstract: The paper describes the possibilities of implementing cobots for the execution of manual tasks in human-cobot collaborative teams to reduce waste within manufacturing systems from the perspective of Industry 5.0. Particular attention is paid to those manufacturing systems where, due to the high costs of possible reorganization, cobots are implemented in the existing system without significant modifications. The work is carried out in collaboration between humans and machines. To illustrate proposed implementation model, a conceptual use case (concept case) corresponding to an actual furniture manufacturing process was developed. The identification of the space for the use of cobots was verified using the value stream mapping method, and the implementation possibilities were analyzed using dedicated simulation software. The production process was mapped in both the value stream map and the simulation software. The potential for time savings in the implementation of the production process and a potential increase in the average production volume were demonstrated. Thus, the implementation possibilities of the presented concept were positively verified. The presented approach forms the basis for innovative solutions based on an interdisciplinary combination of organizational, management, and technical issues from the perspective of cobot use. This offers the opportunity to develop a cost-effective solution for implementing modern cobotic system technology to reduce waste in line with lean management. The concept opens up the perspective for many questions in terms of how and when to implement a cobotic systems solution in an organization. This is particularly relevant from the perspective of a company operating in a specific industry, using selected technologies and work organization methods.

Manufacturing Process Support Using Artificial Intelligence

Abstract: The aim of this paper is to presents an interdisciplinary approach to the problem of manufacturing process support using Artificial Intelligence (AI) by introducing a cross physical-information control system concept. The scope of the article covers description of logical architecture concept, data exchange process and autonomous steering mechanism using artificial intelligent method. The article indicates that in order to successfully support manufacturing process using Artificial Intelligence proper logical support system architecture need to be applied. It is important to emphasize that the use of AI method is not enough to cover multidimensional production issues of gathering and processing data. Therefore whole system need to be organized in this way to support AI with data to be processed. Thanks to that, it is possible to meet many different goals and achieve significant results in the field of manufacturing process. Because of that in this paper the main focus is put on system logical architecture with descriptions of each element involved. Moreover, the article describes AI controller mechanism applied running on real time raw data collected from machines and products. Findings presented within paper could be use in real case scenarios as a design pattern to develop, deploy or optimize production management systems in small-medium enterprises based on low cost solutions of Internet of Things (IoT) providing data to be analyzed with use of cloud computing technology running AI algorithms.

Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

Abstract: Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Abstract: This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing

Abstract: The state-of-the-art technologies in new generation information technologies (New IT) greatly stimulate the development of smart manufacturing. In a smart manufacturing environment, more and more devices would be connected to the Internet so that a large volume of data can be obtained during all phases of the product lifecycle. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. However, different factors, such as the network unavailability, overfull bandwidth, and latency time, restrict its availability for high-speed and low-latency real-time applications. Fog computing and edge computing extended the compute, storage, and networking capabilities of the cloud to the edge, which will respond to the above-mentioned issues. Based on cloud computing, fog computing, and edge computing, in this paper, a hierarchy reference architecture is introduced for smart manufacturing. The architecture is expected to be applied in the digital twin shop floor, which opens a bright perspective of new applications within the field of manufacturing.

A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges

Abstract: A new industrial revolution is undergoing, based on a number of technological paradigms. The will to foster and guide this phenomenon has been summarized in the expression “Industry 4.0” (I4.0). Initiatives under this term share the vision that many key technologies underlying Cyber-Physical Systems and Big Data Analytics are converging to a new distributed, highly automated, and highly dynamic production network , and that this process needs regulatory and cultural advancements to effectively and timely develop. In this work, we focus on the technological aspect only, highlighting the unprecedented complexity of I4.0 emerging from the scientific literature. While previous works have focused on one or up to four related enablers, we consider ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet. For each we explore the main characteristics in relation to I4.0 and its interdependencies with other enablers. Finally we provide a detailed analysis of challenges in leveraging each of the enablers in I4.0, evidencing possible roadblocks to be overcome and pointing at possible future directions of research. Our goal is to provide a reference for the experts in some of the technological fields involved, for a reconnaissance of integration and hybridization possibilities with other fields in the endeavor of I4.0, as well as for the laymen, for a high-level grasp of the variety (and often deep history) of the scientific research backing I4.0.