scholarly journals Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5031
Author(s):  
Javier Villalba-Diez ◽  
Miguel Gutierrez ◽  
Mercedes Grijalvo Martín ◽  
Tomas Sterkenburgh ◽  
Juan Carlos Losada ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Industry 4.0 ◽  
Numerical Control ◽  
Manufacturing Processes ◽  
Bayesian Decision ◽  
Cnc Machine ◽  
Digital Twin ◽  
Decision Networks ◽  
Computational Performance

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

Digital Twin-Driven Condition-Based Maintenance

2022 ◽  
pp. 109-136
Author(s):  
Adolfo Crespo del Castillo ◽  
Marco Macchi ◽  
Laura Cattaneo
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Simulation Models ◽  
Condition Based Maintenance ◽  
Industrial Management ◽  
Digital Twin ◽  
Operation And Maintenance ◽  
Twin Models ◽  
Technical Professionals ◽  
Case Development

The world is witnessing an all-level digitalization that guides the industry and business to a restructuration in order to adapt to the new requirements of the surrounding environment. That change also concerns the labour of the technical professionals and their formation. As a consequence of this deep consciousness-raising, this chapter will investigate and develop simulation models based on the current digitalization. The aim of this chapter is the exposition of a real case development of “digital twin” models framed as part of the condition-based maintenance paradigm to improve real-time assets operation and maintenance. This model contributes by providing real-time results that could turn into a basis for the industrial management decisions and place them in the Industry 4.0 paradigm environment.

scholarly journals Implementation of Digital Twin for Engine Block Manufacturing Processes

2020 ◽  
Vol 10 (18) ◽  
pp. 6578
Author(s):  
Roman Bambura ◽  
Marek Šolc ◽  
Miroslav Dado ◽  
Luboš Kotek
Keyword(s):  
Real Time ◽  
Production Line ◽  
Manufacturing Industry ◽  
Real Life ◽  
Engine Block ◽  
Manufacturing Processes ◽  
Production Plant ◽  
Implementation Framework ◽  
Digital Twin ◽  
Programmable Logic Control

The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Ahmet Soylemezoglu ◽  
S. Jagannathan ◽  
Can Saygin
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Numerical Control ◽  
Rolling Element Bearing ◽  
Time To Failure ◽  
Cnc Machine ◽  
Bearing Failures ◽  
Rolling Element

In this paper, a novel Mahalanobis–Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.

Development of a Smart Computer Numerical Control System

2013 ◽  
Author(s):  
Zhiqian Sang ◽  
Xun Xu
Keyword(s):  
Real Time ◽  
High Speed ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Machining Process ◽  
Cnc Machine ◽  
Field Bus ◽  
Nc System ◽  
Tool Paths ◽  
Cnc Controller

Traditional Computer Numerical Control (CNC) machines use ISO6983 (G/M code) for part programming. G/M code has a number of drawbacks and one of them is lack of interoperability. The Standard for the Exchange of Product for NC (STEP-NC) as a potential replacement for G/M code aims to provide a unified and interoperable data model for CNC. In a modern CNC machine tool, more and more motors, actuators and sensors are implemented and connected to the NC system, which leads to large quantity of data being transmitted. The real-time Ethernet field-bus is faster and more deterministic and can fulfill the requirement of data transmission in the high-speed and high-precision machining scenarios. It can provide more determinism on communication, openness, interoperability and reliability than a traditional field-bus. With a traditional CNC system using G/M code, when the machining is interrupted by incidents, restarting the machining process is time-consuming and highly experience-dependent. The proposed CNC controller can generate just-in-time tool paths for feature-based machining from a STEP-NC file. When machining stoppage occurs, the system can recover from stoppage incidents with minimum human intervention. This is done by generating new tool paths for the remaining machining process with or without the availability of the original cutting tool. The system uses a real-time Ethernet field-bus as the connection between the controller and the motors.

Using Virtual Reality in the Teaching of Manufacturing Processes with Material Removal in CNC Machine-Tools

2011 ◽  
Vol 692 ◽  
pp. 112-119 ◽  
Author(s):  
Alfredo Sanz ◽  
Ignaciof González ◽  
Agustin Javier Castejón ◽  
Jose Leopoldo Casado
Keyword(s):  
Virtual Reality ◽  
Virtual Machine ◽  
Machine Tools ◽  
Material Removal ◽  
Teaching Practice ◽  
Numerical Control ◽  
Cnc Machine Tools ◽  
Manufacturing Processes ◽  
Cnc Machine ◽  
Machining Processes

This paper presents a methodology for the incorporation of a Virtual Reality development applied to the teaching of manufacturing processes, namely the group of machining processes in numerical control of machine tools. The paper shows how it is possible to supplement the teaching practice through virtual machine-tools whose operation is similar to the 'real' machines while eliminating the risks of use for both users and the machines.

scholarly journals Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 151
Author(s):  
Samad M. E. Sepasgozar
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Industry 4.0 ◽  
Construction Projects ◽  
Data Exchange ◽  
Smart Cities ◽  
Information Modeling ◽  
Digital Data ◽  
Digital Twin ◽  
Industrial Sectors

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

scholarly journals An Architecture for Hybrid Manufacturing Combining 3D Printing and CNC Machining

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Marcel Müller ◽  
Elmar Wings
Keyword(s):  
Additive Manufacturing ◽  
Cnc Machining ◽  
Numerical Control ◽  
Manufacturing Processes ◽  
Hybrid Manufacturing ◽  
Cnc Machine ◽  
Subtractive Manufacturing ◽  
3D Printed ◽  
One Machine ◽  
Complex Parts

Additive manufacturing is one of the key technologies of the 21st century. Additive manufacturing processes are often combined with subtractive manufacturing processes to create hybrid manufacturing because it is useful for manufacturing complex parts, for example, 3D printed sensor systems. Currently, several CNC machines are required for hybrid manufacturing: one machine is required for additive manufacturing and one is required for subtractive manufacturing. Disadvantages of conventional hybrid manufacturing methods are presented. Hybrid manufacturing with one CNC machine offers many advantages. It enables manufacturing of parts with higher accuracy, less production time, and lower costs. Using the example of fused layer modeling (FLM), we present a general approach for the integration of additive manufacturing processes into a numerical control for machine tools. The resulting CNC architecture is presented and its functionality is demonstrated. Its application is beyond the scope of this paper.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

MANUFACTURING PROCESSES TRANSFORMATION IN AERONAUTICAL SECTOR IN AN INDUSTRY 4.0 CONTEXT

10.6036/9938 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 233-234
Author(s):  
LEANDRO RUIZ ◽  
MANUEL TORRES ◽  
ALEJANDRO GOMEZ VILANOVA ◽  
SEBASTIAN DIAZ DIAZ ◽  
FRANCISCO CAVAS MARTINEZ
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Industry 4.0 ◽  
Added Value ◽  
Manufacturing Processes ◽  
Production Chain ◽  
High Productivity ◽  
Robotic Cells ◽  
Major Transformation ◽  
The Creation

Adoption by the aeronautical sector of developments and technologies of the so-called Industry 4.0 is a major transformation, due to the added value that these new processes bring to the production chain. It is in this context, in which the relevance of the digitalization and automation of all manufacturing processes is observed, with the increasingly widespread implantation of robotic cells and other technologies such as systems of vision and artificial intelligence, will lead to a new digital scenario that will allow the creation in real time of reconfigurable and sustainable spaces with high productivity and reliability.

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