scholarly journals Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

2021 ◽  
Vol 5 (4) ◽  
pp. 58
Author(s):  
Saman Fattahi ◽  
Takuya Okamoto ◽  
Sharifu Ura
Keyword(s):  
Surface Roughness ◽  
Big Data ◽  
Model Simulation ◽  
Semantic Annotation ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Simulation Algorithm ◽  
Planning Systems ◽  
Digital Twins ◽  
Roughness Model

In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

Theoretical Surface Roughness Model in High Speed Face Milling

2014 ◽  
Vol 989-994 ◽  
pp. 3331-3334
Author(s):  
Tao Zhang ◽  
Guo He Li ◽  
L. Han
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
High Speed ◽  
Cutting Parameters ◽  
Angular Speed ◽  
Face Milling ◽  
Advanced Manufacturing ◽  
Roughness Model ◽  
Machined Parts ◽  
Important Object

High speed milling is a newly developed advanced manufacturing technology. Surface integrity is an important object of machined parts. Surface roughness is mostly used to evaluate to the surface integrity. A theoretical surface roughness model for high face milling was established. The influence of cutting parameters on the surface roughness is analyzed. The surface roughness decreases when the cutter radius increases, total number of tooth and rotation angular speed, while it increases with the feeding velocity. The high speed face milling can get a smooth surface and it can replace the grinding with higher efficiency.

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen
Keyword(s):  
Surface Roughness ◽  
Real Time ◽  
Back Propagation ◽  
Cutting Parameters ◽  
Wavelet Network ◽  
Back Propagation Algorithm ◽  
The Real ◽  
Roughness Model ◽  
On Line ◽  
Artificial Network

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.

Architecture and use cases of digital twins towards smart manufacturing

2021 ◽  
Vol 8 (3) ◽  
pp. 264
Author(s):  
Nibhrita Tiwari ◽  
Maninder Jeet Kaur ◽  
Ved Prakash Mishra
Keyword(s):  
Use Cases ◽  
Smart Manufacturing ◽  
Digital Twins

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Investigation of Loss Impact From Production-Like Features in a Compressor Duct Under Engine Realistic Conditions

2017 ◽  
Author(s):  
Fredrik Wallin ◽  
Mark H. Ross ◽  
Max Rusche ◽  
Scott Morris ◽  
Steven Ray
Keyword(s):  
Surface Roughness ◽  
Total Pressure ◽  
Experimental Testing ◽  
Computational Grid ◽  
Test Facility ◽  
Pressure Probe ◽  
Cfd Analysis ◽  
Roughness Model ◽  
Geometrical Features ◽  
Real Geometry

An experimental and numerical investigation of the flow in a compressor duct with engine-realistic in-production features is presented in this paper. The experimental testing was conducted in the ND-FSCC test facility at University of Notre Dame, Indiana, USA. A baseline duct was also tested for back-to-back comparison. The ducts were heavily instrumented; duct inlet and exit flowfields were scanned using a five-hole pressure probe that provided total pressure, velocities and flow angles. Based on the five-hole probe total pressures, duct losses could be assessed. Furthermore the duct inlet boundary layers were traversed and turbulence intensity levels were assessed. For the CFD analysis of the production-like duct, a highly complex computational grid, resolving all the geometrical features present, was used. A previously validated surface roughness model was used to account for the cast aero-surfaces. Both experimental and numerical results show that there is a significant increase in loss for the production-like duct when compared to the baseline duct loss. The CFD results agree very well with experimental results for the baseline duct, which makes it possible to use the experimental data recorded for the production-like duct to validate CFD tools for real geometry effects, such as interface steps and surface roughness for example.

scholarly journals Using AutomationML and Graph-Based Design Languages for Automatic Generation of Digital Twins of Cyber-Physical Systems

2020 ◽  
Author(s):  
Nicolai Beisheim ◽  
Markus Kiesel ◽  
Markus Linde ◽  
Tobias Ott
Keyword(s):  
Development Process ◽  
Cyber Physical Systems ◽  
Production Equipment ◽  
Manufacturing Processes ◽  
Reference Architecture ◽  
Physical Systems ◽  
Cross Domain ◽  
Digital Twins ◽  
Process Simulations ◽  
Design Languages

The interdisciplinary development of smart factories and cyber-physical systems CPS shows the weaknesses of classical development methods. For example, the communication of the interdisciplinary participants in the development process of CPS is difficult due to a lack of cross-domain language comprehension. At the same time, the functional complexity of the systems to be developed increases and they act operationally as independent CPSs. And it is not only the product that needs to be developed, but also the manufacturing processes are complex. The use of graph-based design languages offers a technical solution to these challenges. The UML-based structures offer a cross-domain language understanding for all those involved in the interdisciplinary development process. Simulations are required for the rapid and successful development of new products. Depending on the functional scope, graphical simulations of the production equipment are used to simulate the manufacturing processes as a digital factory or a virtual commissioning simulation. Due to the high number of functional changes during the development process, it makes sense to automatically generate the simulation modelling as digital twins of the products or means of production from the graph-based design languages. The paper describes how digital twins are automatically generated using AutomationML according to the Reference Architecture Model Industry 4.0 (RAMI 4.0) or the Industrial Internet Reference Architecture (IIRA).

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