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Jerzy Lipski

Bio: Jerzy Lipski is an academic researcher from Lublin University of Technology. The author has contributed to research in topics: Production engineering & Digital library. The author has an hindex of 4, co-authored 22 publications receiving 83 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the vibrations of a tool-workpiece system induced by random disturbances in a straight turning process are considered and their effect on a product surface is considered using a one-degree-of-freedom model.

35 citations

Journal Article
TL;DR: The perspectives of manufacturing companies surrounded by new solutions of CPS, CPPS and CM in relation to fog computing are discussed.
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

17 citations

Journal ArticleDOI
01 Jan 2017
TL;DR: An intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product and facilitating tool life optimisation and decreasing tool change costs is presented.
Abstract: The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3). The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3) and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone), connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

8 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: 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, presenting the method of processing non-labelled data registered by sensors.
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.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.

937 citations

Journal ArticleDOI
TL;DR: 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, which showed that the overall equipment effectiveness of the equipment is significantly improved.
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.

736 citations

Journal ArticleDOI
TL;DR: The definition and state-of-the-art development outcomes of Digital Twin are summarized, and outstanding research issues of developing Digital Twins for smart manufacturing are identified.
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.

649 citations

Journal ArticleDOI
Qinglin Qi1, Fei Tao1
TL;DR: Based on cloud computing, fog computing, and edge computing, a hierarchy reference architecture is introduced for smart manufacturing and is expected to be applied in the digital twin shop floor, which opens a bright perspective of new applications within the field of manufacturing.
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.

174 citations

Journal ArticleDOI
TL;DR: This work considers 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.
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.

149 citations