Improved Performance for Stability of Screw down System by implement Fuzzy Logic

Main problem in hydraulic cold rolling industries is the longitudinal strip thickness .In this random disturbance can increase the error of the system. For reduce this type of error ARM9 based gauge control system is design. In this paper screw down system by use of fuzzy logic. After apply fuzzy logic the screw down system is getting more stable. The output response is touching to the step response. Screw down System is getting stable in 1.8 second. DOI: 10.17762/ijritcc2321-8169.15069

Application of Artificial Neural Networks in Cold Rolling Process

Rolling is one of the most complicated processes in metal forming. Knowing the exact amount of basic parameters, especially inter-stand tensions can be effective in controlling other parameters in this process. Inter-stand tensions affect rolling pressure, rolling force, forward and backward slips and neutral angle. Calculating this effect is an important step in continuous rolling design and control. Since inter-stand tensions cannot be calculated analytically, attempt is made to describes an approach based on artificial neural network (ANN) in order to identify the applied parameters in a cold tandem rolling mill. Due to the limited experimental data, in this subject a five stand tandem cold rolling mill is simulated through finite element method. The outputs of the FE simulation are applied in training the network and then, the network is employed for prediction of tensions in a tandem cold rolling mill. Here, after changing and checking the different designs of the network, the 11-42-4 structure by one hidden layer is selected as the best network. The verification factor of ANN results according to experimental data are over R=0.9586 for training and testing the data sets. The experimental results obtained from the five stands tandem cold rolling mill. This paper proposed new ANN for prediction of inter-stand tensions. Also, this ANN method shows a fuzzy control algorithm for investigating the effect of front and back tensions on reducing the thickness deviations of hot rolled steel strips. The average of the training and testing data sets is mentioned 0.9586. It means they have variable values which are discussed in details in section 4. According to Table 7, this proposed ANN model has the correlation coefficients of 0.9586, 0.9798, 0.9762 and 0.9742, respectively for training data sets and 0.9905, 0.9798, 0.9762 and 0.9803, respectively for the testing data sets. These obtained numbers indicate the acceptable accuracy of the ANN method in predicting the inter-stand tensions of the rolling tandem mill. This method provides a highly accurate solution with reduced computational time and is suitable for on-line control or optimization in tandem cold rolling mills. Due to the limited experimental data, for data extraction for the ANN simulation, a 2D tandem cold rolling process is simulated using ABAQUS 6.9 software. For designing a network for this rolling problem, various structures of neural networks are studied in MATLAB 7.8 software

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Electromagnetic measurements of steel phase transformations

This thesis describes the development of electromagnetic sensors to measure the phase transformation in steel as it cools from the hot austenite phase to colder ferritic based phases. The work initially involved investigating a variety of sensing configurations including ac excited coils, C-core arrangements and the adaptation of commercial eddy current proximity sensors. Finally, two prototype designs were built and tested on a hot strip mill. The first of these, the T-meter was based on a C-shaped permanent magnet with a Gaussmeter measuring the magnetic field at the pole ends. Laboratory tests indicated that it could reliably detect the onset of transformation. However, the sensor was sensitive to both the steel properties and the position of the steel. To overcome this, an eddy current sensor was incorporated into the final measurement head. The instrument gave results which were consistent with material property variations, provided the lift off variations were below 3Hz. The results indicated that for a grade 1916 carbon- manganese steel, the signal variation was reduced from 37% to 2%, and the resulting output was related to the steel property variations. The second of these prototypes was based on a dc electromagnetic E-core, with Hall probes in each of the three poles. 'Cold' calibration tests were used to decouple the steel and the lift-off. The results indicated that there was an error of 3-4% ferrite/mm at high ferrite fractions. At lower fractions the error was higher due to the instrument’s insensitivity to lift-off. The resulting output again showed a relationship with varying steel strip properties. ft was also shown that a finite element model could be calibrated to experimental results for a simple C-core geometry such that the output was sensitive to 0.2% of the range. This is required to simulate the sensor to resolve to 10% ferrite

Energy Efficient Window Development

The paper investigates the development of energy efficient windows in the past 30 years. The focus is on the development and interlinkages among technology, actors´ interaction and market diffusion in a broader policy context. The paper shows that in singular development cycles, different factors and the interfaces among these factors influenced the improvement and penetration of energy efficient window technologies. Such factors includes a) surrounding factors, such as climate characteristics, oil crisis and international concerns and strategies, b) policy instruments, like building codes and technology procurement programs, as well as c) industry initiatives, including niche market strategies

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics