A System and Method for Monitoring Controlling and Troubleshooting of Abrasive Waterjet Cutting Apparatus

The invention consists of a system and method for monitoring, controlling and troubleshooting of Abrasive Waterjet Cutting machinery. More in detail, the method exploits a network of sensors for continuously monitor its vibroacoustic emission pattern. Relevant features are extracted from the data using an iterative processing method. Deviations from a benchmark are interpreted based on model training then used for automatic control and troubleshooting. The expected outcome is an improvement of waterjet automation and robustness to the excellence of Industry 4.0 with a consequent impact on both its cost effectiveness and the provided quality assurance

A Discrete-Continuous Method for Predicting Thermochemical Phenomena in a Cement Kiln and Supporting Indirect Monitoring

Thermochemical phenomena involved in cement kilns are still not well understood because of their complexity, besides technical difficulties in achieving direct measurements of critical process variables. This article addresses the problem of their comprehensive numerical prediction. The presented numerical model exploits Computational Fluid Dynamics and Finite Difference Method approaches for solving the gas domain and the rotating wall, respectively. The description of the thermochemical conversion and movement of the powder particles is addressed with a Lagrangian approach. Coupling between gas, particles and the rotating wall includes momentum, heat and mass transfer. Three-dimensional numerical predictions for a full-size cement kiln are presented and they show agreement with experimental data and benchmark literature. The quality and detail of the results are believed to provide a new insight into the functioning of a cement kiln. Attention is paid to the computational burden of the model and a methodology is presented for reducing the time-to-solution and paving the way for its exploitation in quasireal-time, indirect monitoring

A Discrete-Continuous Method for Predicting Thermochemical Phenomena in a Cement Kiln and Supporting Indirect Monitoring

Thermochemical phenomena involved in cement kilns are still not well understood because of their complexity, besides technical difficulties in achieving direct measurements of critical process variables. This article addresses the problem of their comprehensive numerical prediction. The presented numerical model exploits Computational Fluid Dynamics and Finite Difference Method approaches for solving the gas domain and the rotating wall, respectively. The description of the thermochemical conversion and movement of the powder particles is addressed with a Lagrangian approach. Coupling between gas, particles and the rotating wall includes momentum, heat and mass transfer. Three-dimensional numerical predictions for a full-size cement kiln are presented and they show agreement with experimental data and benchmark literature. The quality and detail of the results are believed to provide a new insight into the functioning of a cement kiln. Attention is paid to the computational burden of the model and a methodology is presented for reducing the time-to-solution and paving the way for its exploitation in quasi-real-time, indirect monitoring

Stationary Wavelet Transform denoising in Pulsed Thermography: influence of camera resolution on defect detection

Denoising filters are widely used in image enhancement. However, they might induce severe blurring effects the lower is the resolution of the original image. When applied to a thermal image in Non-Destructive Testing (NDT), blurring could entail wrong estimation of defect boundaries and an overall reduction in defect detection performances. This contribution discusses the application of a wavelet-based denoising technique to a thermographic sequence obtained from a Pulsed Thermography testing, when using a high- resolution 1024x768 FPA infrared camera. Influence of denoising approach on data post- processed by Principal Component Analysis is discussed. Results indicate marked enhancement in defect detection, especially when compared to those obtained with a standard-resolution 320x240 FPA infrared camera

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion

XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State

Processes involving materials in gaseous and powder states cannot be modelled without coupling interactions between the two states. XDEM (Extended Discrete Element Method) is a valid tool for tackling this issue, since it allows a coupled CFD- DEM simulation to be run. Such strength, however, mainly finds in long computational times its main drawback. This aspect is indeed critical in several applications, since a long computational time is in contrast with the increasing demand for predictive tools that can provide fast and accurate results in order to be used in new monitoring and control strategies. This paper focuses on the use of the XDEM framework as a tool for fine tuning a lumped representation of the non-isothermal decarbonation of a CaCO3 sample in powder state. The tuning of the lumped model is performed exploiting the multi-objective optimization capability of genetic algorithms. Results demonstrate that such approach makes it possible to estimate fast and accurate models to be used, for instance, in the fields of virtual sensing and predictive control

Soft sensing in the process industry

Il presente contributo discute l’applicazione di tecniche soft sensing nell’ambito di processi industriali, come metodi di monitoraggio indiretti per la stima di variabili di processo non accessibili. E’ presentato un caso applicativo riguardante la sinterizzazione di clinker, un processo caratterizzato da bassa efficienza, energeticamente intensivo e con rilevante impatto ambientale: l’approccio soft sensing è inizialmente testato nel sistema termico convenzionale, successivamente affronta un modulo di riscaldamento innovativo basato sull’applicazione di microonde ad alta potenza, mono-modali, al materiale processato. Il lavoro è focalizzato sullo sviluppo di modelli fisici per la stima indiretta di variabili critiche di processo. Vengono anche affrontati l’integrazione delle routine di calcolo con i dati forniti dai sensori delle architetture di monitoraggio, e l’implementazione di strategie per l’ottimizzazione dell’accuratezza dei nuovi strumenti. E’ implementato un metodo stocastico basato su una procedura Monte Carlo adattativa, per la stima della propagazione di incertezze attraverso il modello matematico del soft sensor. Un framework innovativo di modellazione fornisce una valutazione del limite minimo di incertezza introdotta dal modello stesso. L’incertezza totale del soft sensor è quindi calcolata attraverso la composizione dei diversi contributi. I soft sensor sono testati nel monitoraggio on-line delle variabili termiche e chimiche dei processi considerati. Le stime indirette delle variabili target sono comparate a misure dirette, e mostrano scostamenti nell’ordine dell’1%. Le routine di calcolo garantiscono tempi di risposta veloci, e migliorano l’adoperabilità degli strumenti. I risultati confermano le buone prestazioni dei soft sensor nel monitoraggio on-line di variabili non accessibili. La loro intrinseca robustezza li rende potenziali back-up di sensori hardware, pronti ad intervenire in caso di avarie, e prima che queste si ripercuotano sul processo

Soft sensing in the process industry

Il presente contributo discute l’applicazione di tecniche soft sensing nell’ambito di processi industriali, come metodi di monitoraggio indiretti per la stima di variabili di processo non accessibili. E’ presentato un caso applicativo riguardante la sinterizzazione di clinker, un processo caratterizzato da bassa efficienza, energeticamente intensivo e con rilevante impatto ambientale: l’approccio soft sensing è inizialmente testato nel sistema termico convenzionale, successivamente affronta un modulo di riscaldamento innovativo basato sull’applicazione di microonde ad alta potenza, mono-modali, al materiale processato. Il lavoro è focalizzato sullo sviluppo di modelli fisici per la stima indiretta di variabili critiche di processo. Vengono anche affrontati l’integrazione delle routine di calcolo con i dati forniti dai sensori delle architetture di monitoraggio, e l’implementazione di strategie per l’ottimizzazione dell’accuratezza dei nuovi strumenti. E’ implementato un metodo stocastico basato su una procedura Monte Carlo adattativa, per la stima della propagazione di incertezze attraverso il modello matematico del soft sensor. Un framework innovativo di modellazione fornisce una valutazione del limite minimo di incertezza introdotta dal modello stesso. L’incertezza totale del soft sensor è quindi calcolata attraverso la composizione dei diversi contributi. I soft sensor sono testati nel monitoraggio on-line delle variabili termiche e chimiche dei processi considerati. Le stime indirette delle variabili target sono comparate a misure dirette, e mostrano scostamenti nell’ordine dell’1%. Le routine di calcolo garantiscono tempi di risposta veloci, e migliorano l’adoperabilità degli strumenti. I risultati confermano le buone prestazioni dei soft sensor nel monitoraggio on-line di variabili non accessibili. La loro intrinseca robustezza li rende potenziali back-up di sensori hardware, pronti ad intervenire in caso di avarie, e prima che queste si ripercuotano sul processo.Present contribution discusses the application of soft sensing in the process industry, as an indirect monitoring technique for the on-line assessment of non-accessible process variables. A relevant application case is presented which involves clinker sintering, a low-efficient, high-energy intensive process with strong environmental impact: the soft-sensing approach is firstly tested in the conventional thermal system, then it is addressed an innovative heating module based on the application of high-power, mono-modal microwaves to the material under processing. The work is focused over the development of physical models for the indirect evaluation of the critical process variables. The integration of the computation routines with the data provided by the sensors of the monitoring architectures is also addressed, as well as possible optimization strategies for improving the reliability of the tools. A stochastic method based on an adaptive Monte Carlo procedure is implemented, for assessing the propagation of the input uncertainties through the mathematical model of the soft sensor. An innovative numerical framework provides a lower-bound estimation of the uncertainty introduced by the model itself. Successively, the overall uncertainty of the soft sensor is calculated as the composition of the different contributes. The soft sensors are tested in the real-time monitoring of thermal and chemical variables of the processes considered. The indirect estimations of the target variables are compared with direct measurements, showing deviations in the order of 1%. Computation routines ensure fast executions, thus improving the exploitability of the tools. Results confirm the good performances of the soft sensors in the on-line monitoring of non-accessible variables. The intrinsic robustness makes them a potential back-up of direct sensors, ready to intervene when a breakdown of the hardware counterpart occurs, and before this affects the process

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion

A comparison between discrete analysis and a multiphase approach for predicting heat conduction in packed beds

the Discrete Element Method (DEM) is a Lagrangian approach initially developed for predicting particles flow. The eXtended Discrete Element Method (XDEM) framework, developed at the LuXDEM Research Centre of the University of Luxembourg, extends DEM by including the thermochemical state of particles, as well as their interaction with a Computational Fluid Dynamics (CFD) domain. The level of detail of its predictions makes the XDEM suite a powerful tool for predicting complex industrial processes like steel making, powder metallurgy and additive manufacturing. Like in any other DEM software, the critical aspect of the simulations is the computation requirement that grows rapidly as the number of particles increases. Indeed, such burden currently represents the main bottleneck to its full exploitation in large-scale scenarios. Digital Twin, a research project founded by the European Regional Development Fund (ERDF), aims at drastically accelerate XDEM through different approaches and make it an effective tool for numerical predictions in industry as well as virtual prototyping. The Multiphase Particle- In-Cell (MP-PIC) method has been introduced for reducing the computation burden of DEM. It has been initially developed for predicting particles flow and uses a two-way transfer of information between the Lagrangian entities and a computation grid. The method avoids explicit contact detection and can potentially achieve a drastic reduction of the time-to-solution respect to DEM. The present contribution introduces a multiphase approach for predicting the conductive heat transfer within a static packed bed of particles. Results from a test case are qualitatively and quantitatively compared against reference XDEM predictions. The method can be effectively exploited in combination with MP- PIC for predicting the thermochemical state of particles