Steel Property and Process Models for Quality Control and Optimization

2013 ◽  
Vol 762 ◽  
pp. 301-306
Author(s):  
Satu Tamminen ◽  
Henna Tiensuu ◽  
Ilmari Juutilainen ◽  
Juha Röning
Keyword(s):  
Steel Industry ◽  
Control Parameter ◽  
Industrial Applications ◽  
Process Models ◽  
Monitoring And Control ◽  
Event Prediction ◽  
Process Monitoring And Control ◽  
Steel Property ◽  
And Control

High quality and low variability in the properties of the products are the main goals in manufacturing. The quality of the product is verified by testing different properties. It can be improved with models developed for event prediction. This paper presents with application examples the modelling steps required for effective process modelling. First, the pre-processing and feature extraction phase are illustrated. The modelling phase concentrates especially on the heteroscedasticity problem that is commonly present in industrial applications. The process monitoring and control parameter optimization based on these models is presented, as well as the solution for the lack of observations for the dependent variable. Many of the developed models are in daily use in different process states in steel industry. They enable the design of new products and the analysis of the effects of different process parameters on variability reduction. The proposed methods are application independent.

scholarly journals Chatter model for enabling a digital twin in machining

2020 ◽  
Vol 110 (9-10) ◽  
pp. 2439-2444
Author(s):  
Shukri Afazov ◽  
Daniele Scrimieri
Keyword(s):  
Mathematical Model ◽  
Fourier Transform ◽  
Cutting Forces ◽  
Material Removal ◽  
Industrial Applications ◽  
Monitoring And Control ◽  
Machining Processes ◽  
Digital Twins ◽  
Process Monitoring And Control ◽  
And Control

Abstract This paper presents the development of a new chatter model using measured cutting forces instead of a mathematical model with empirical nature that describes them. The utilisation of measured cutting forces enables the prediction of real-time chatter conditions and stable machining. The chatter model is validated using fast Fourier transform (FFT) analyses for detection of chatter. The key contribution of the developed chatter model is that it can be incorporated in digital twins for process monitoring and control in order to achieve greater material removal rates and improved surface quality in future industrial applications involving machining processes.

scholarly journals A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6542
Author(s):  
Thiago C. Jesus ◽  
Paulo Portugal ◽  
Daniel G. Costa ◽  
Francisco Vasques
Keyword(s):  
Sensor Networks ◽  
Industrial Applications ◽  
Sensor Nodes ◽  
Monitoring And Control ◽  
Industrial Plants ◽  
Proposed Model ◽  
Dependability Evaluation ◽  
Industrial Monitoring ◽  
And Control

In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

The Isolator of Industrial Automatic Control System Selection and Application

2011 ◽  
Vol 301-303 ◽  
pp. 1714-1718
Author(s):  
Ji Meng Zhang ◽  
Hong Shuo Wang ◽  
Ben De Gan
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Automatic Control System ◽  
Signal Transmission ◽  
Control Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Isolation Device ◽  
System Selection ◽  
And Control

In the automatic control system of industrial field, the production process monitoring and control process is dependent on Mutual coordination of various automation instrument, computer and corresponding actuators. The coordination is accurate or not, the key is signal transmission quality among those agencies. The application and selection of isolation device directly affect signal transmission. This paper discusses the application and choose of industrial site isolator from isolation principle, the principle and choose for isolator, commissioning and parameter selection based on practical application.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

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