Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches

Pengukuran Koefisien Atenuasi dan Waktu Tempuh Gelombang Ultrasonik Suspensi Zirconia Untuk Mencari Korelasinya Dengan Ukuran Partikel

The measurement of particle size distribution in a solid-liquid suspension system is important in the purposes of material characterization. This work reports on the measurement results of attenuation and time-of-flight (tof) parameters of ultrasonic wave propagated through a zirconia-water suspension system to find their correlation with the particle sizes. An 2.1 MHz ultrasonic wave was generated continuously by a transmitter transducer and propagated through the suspension contained in a test-cell with dimension of 1.15×4.25×6 cm. A receiver transducer converted the propagated wave into an electrical signal which was displayed on an oscilloscope. The zirconia-water suspension system used three particle sizes of 45, 18 and 7 μm, respectively with concentration variation of 050 wt%. Attenuation was calculated as a ratio of received- and transmitted- amplitudes. The phase difference between initial and received amplitudes is define as the time-of-flight of the propagated wave. The suspension test showed the attenuation value increased with larger particle sizes. As concentration increased, the attenuation decreased and tof increased. Based on the analysis results, it is concluded that attenuation and tof values of propagated ultrasonic waves through a zirconia-water suspension system indicated strong correlation to represent the suspended zirconia particle sizes.Pengukuran partikel padatan dalam suatu sistem suspensi banyak diperlukan untuk keperluan karakterisasi bahan. Makalah ini melaporkan hasil korelasi pengukuran parameter atenuasi dan waktu-tempuh gelombang ultrasonik yang dirambatkan dalam suatu medium sistem suspensi partikel zirconia di dalam air. Gelombang ultrasonik kontinyu frekuensi 2,1 MHz yang dibangkitkan dari suatu transduser pemancar dirambatkan ke medium sistem suspensi zirconia-air dalam sel uji dengan dimensi 1.15×4.25×6 cm dan diterima oleh suatu transduser penerima yang ditempatkan saling berhadapan. Larutan suspensi menggunakan tiga ukuran partikel zirconia masing-masing 45, 18 dan 7 μm dengan variasi konsentrasi 0–50 wt%. Atenuasi dihitung dengan membandingkan amplitudo gelombang ultrasonik penerima dengan pemancar. Waktu tempuh diukur dengan perbedaan fasa gelombang ultrasonik sinusoid antara penerima dan pemancar. Hasil pengujian suspensi menunjukkan nilai atenuasi semakin besar untuk suspensi dengan ukuran partikel yang besar. Seiring peningkatan konsentrasi suspensi zirconia-air, nilai atenuasi menurun dan nilai waktu tempuh meningkat. Berdasarkan hasil analisis dapat disimpulkan bahwa nilai parameter atenuasi dan waktu tempuh gelombang ultrasonik yang dirambatkan ke medium suspensi zirconia-air menunjukkan korelasi yang kuat untuk mewakili ukuran partikel zirconia yang tersuspensi di dalam air

Pengukuran Koefisien Atenuasi dan Waktu Tempuh Gelombang Ultrasonik Suspensi Zirconia Untuk Mencari Korelasinya Dengan Ukuran Partikel

The measurement of particle size distribution in a solid-liquid suspension system is important in the purposes of material characterization. This work reports on the measurement results of attenuation and time-of-flight (tof) parameters of ultrasonic wave propagated through a zirconia-water suspension system to find their correlation with the particle sizes. An 2.1 MHz ultrasonic wave was generated continuously by a transmitter transducer and propagated through the suspension contained in a test-cell with dimension of 1.15×4.25×6 cm. A receiver transducer converted the propagated wave into an electrical signal which was displayed on an oscilloscope. The zirconia-water suspension system used three particle sizes of 45, 18 and 7 μm, respectively with concentration variation of 050 wt%. Attenuation was calculated as a ratio of received- and transmitted- amplitudes. The phase difference between initial and received amplitudes is define as the time-of-flight of the propagated wave. The suspension test showed the attenuation value increased with larger particle sizes. As concentration increased, the attenuation decreased and tof increased. Based on the analysis results, it is concluded that attenuation and tof values of propagated ultrasonic waves through a zirconia-water suspension system indicated strong correlation to represent the suspended zirconia particle sizes.Pengukuran partikel padatan dalam suatu sistem suspensi banyak diperlukan untuk keperluan karakterisasi bahan. Makalah ini melaporkan hasil korelasi pengukuran parameter atenuasi dan waktu-tempuh gelombang ultrasonik yang dirambatkan dalam suatu medium sistem suspensi partikel zirconia di dalam air. Gelombang ultrasonik kontinyu frekuensi 2,1 MHz yang dibangkitkan dari suatu transduser pemancar dirambatkan ke medium sistem suspensi zirconia-air dalam sel uji dengan dimensi 1.15×4.25×6 cm dan diterima oleh suatu transduser penerima yang ditempatkan saling berhadapan. Larutan suspensi menggunakan tiga ukuran partikel zirconia masing-masing 45, 18 dan 7 μm dengan variasi konsentrasi 0–50 wt%. Atenuasi dihitung dengan membandingkan amplitudo gelombang ultrasonik penerima dengan pemancar. Waktu tempuh diukur dengan perbedaan fasa gelombang ultrasonik sinusoid antara penerima dan pemancar. Hasil pengujian suspensi menunjukkan nilai atenuasi semakin besar untuk suspensi dengan ukuran partikel yang besar. Seiring peningkatan konsentrasi suspensi zirconia-air, nilai atenuasi menurun dan nilai waktu tempuh meningkat. Berdasarkan hasil analisis dapat disimpulkan bahwa nilai parameter atenuasi dan waktu tempuh gelombang ultrasonik yang dirambatkan ke medium suspensi zirconia-air menunjukkan korelasi yang kuat untuk mewakili ukuran partikel zirconia yang tersuspensi di dalam air

Transfer learning for process monitoring using reflection-mode ultrasonic sensing

The fourth industrial revolution is set to integrate entire manufacturing processes using industrial digital technologies such as the Internet of Things, Cloud Computing, and machine learning to improve process productivity, efficiency, and sustainability. Sensors collect the real-time data required to optimise manufacturing processes and are therefore a key technology in this transformation. Ultrasonic sensors have benefits of being low-cost, in-line, non-invasive, and able to operate in opaque systems. Supervised machine learning models can correlate ultrasonic sensor data to useful information about the manufacturing materials and processes. However, this requires a reference measurement of the process material to label each data point for model training. Labelled data is often difficult to obtain in factory environments, and so a method of training models without this is desirable. This work compares two domain adaptation methods to transfer models across processes, so that no labelled data is required to accurately monitor a target process. The two method compared are a Single Feature transfer learning approach and Transfer Component Analysis using three features. Ultrasonic waveforms are unique to the sensor used, attachment procedure, and contact pressure. Therefore, only a small number of transferable features are investigated. Two industrially relevant processes were used as case studies: mixing and cleaning of fouling in pipes. A reflection-mode ultrasonic sensing technique was used, which monitors the sound wave reflected from the interface between the vessel wall and process material. Overall, the Single Feature method produced the highest prediction accuracies: up to 96.0% and 98.4% to classify the completion of mixing and cleaning, respectively; and R2 values of up to 0.947 and 0.999 to predict the time remaining until completion. These results highlight the potential of combining ultrasonic measurements with transfer learning techniques to monitor industrial processes. Although, further work is required to study various effects such as changing sensor location between source and target domains

Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods

Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods

Ultrasonic measurements and machine learning methods to monitor industrial processes

The process manufacturing sector is increasingly using the collection and analysis of data to improve productivity, sustainability, and product quality. The endpoint of this transformation is processes that automatically adapt to demands in real-time. In-line and on-line sensors underpin this transition by automatically collecting the real-time data required to inform decision-making. Each sensing technique possesses its own advantages and disadvantages making them suitable for specific applications. Therefore, a wide range of sensing solutions must be developed to monitor the diverse and often highly variable operations in process manufacturing. Ultrasonic (US) sensors measure the interaction of mechanical waves with materials. They have benefits of being in-line, real-time, non-destructive, low in cost, small in size, able to monitor opaque materials, and can be applied non-invasively. Machine Learning (ML) is the use of computer algorithms to learn patterns in data to perform a task such as making predictions or decisions. The correlations in the data that the ML models learn during training have not been explicitly programmed by human operators. Therefore, ML is used to automatically learn from and analyse data. There are four main types of ML: supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised and unsupervised ML are both used in this thesis. Supervised ML maps inputs to outputs during training with the aim being to create a model that accurately predicts the outputs of data that was not previously used during training. In contrast, unsupervised learning only uses input data in which patterns are discovered. Supervised ML is being increasingly combined with sensor measurements as it offers several distinct advantages over conventional calibration methods, these include: reduced time for development, potential for more accurate fitting, methods to encourage generalisation across parameter ranges, direct correlations to important process information rather than material properties, and ability for continuous retraining as more data becomes available. The aim of this thesis was to develop ML methods to facilitate the optimal deployment of US sensors for process monitoring applications in industrial environments. To achieve this, the thesis evaluates US sensing techniques and ML methods across three types of process manufacturing operations: material mixing, cleaning of pipe fouling, and alcoholic fermentation of beer. Two US sensing techniques were investigated: a non-invasive, reflection-mode technique, and a transmission-based method using an invasive US probe with reflector plate. The non-invasive, reflection-mode technique is more amenable to industrial implementation than the invasive probe given it can be externally retrofitted to existing vessels. Different feature extraction and feature selection methods, algorithms, and hyperparameter ranges were explored to determine the optimal ML pipeline for process monitoring using US sensors. This facilitates reduced development time of US sensor and ML combinations when deployed in industrial settings by recommending a pipeline that has been trialled over a range of process monitoring applications. Furthermore, methods to leverage previously collected datasets were developed to negate or reduce the burden of collecting labelled data (the outputs required during ML model training and often acquired by using reference measurements) for every new process monitoring application. These included unlabelled and labelled domain adaptation approaches. Both US sensing techniques investigated were found to be similarly accurate for process monitoring. To monitor the development of homogeneity during the blending of honey and water the non-invasive, reflection-mode technique achieved up to 100 % accuracy to classify whether the materials were mixed or non-mixed and an R2 of 0.977 to predict the time remaining (or time since) complete mixing was achieved. To monitor the structural changes during the mixing of flour and water, the aformentioned sensing method achieved an accuracy of 92.5 % and an R2 of 0.968 for the same classification and regression tasks. Similarly, the sensing method achieved an accuracy of up to 98.2 % when classifying whether fouling had been removed from pipe sections and R2 values of up 0.947 were achieved when predicting the time remaining until mixing was complete. The non-invasive, reflection-mode method also achieved R2 values of 0.948, Mean Squared Error (MSE) values of 0.283, and Mean Absolute Error (MAE) values of 0.146 to predict alcohol by volume percentage of alcohol during beer fermentation. In comparison, the transmission-based sensing method achieved R2 values of 0.952, MSE values of 0.265, and MAE values of 0.136 for the same task. Furthermore, the transmission-based method achieved accuracies of up to 99.8 % and 99.9 % to classify whether ethanol production had started and whether ethanol production had finished during an industrial beer fermentation process. The material properties that affect US wave propagation are strongly temperature dependent. However, ML models that omitted the process temperature were comparable in accuracy to those which included it as an input. For example, when monitoring laboratory scale fermentation processes, the highest performing models using the process temperature as a feature achieved R2 values of 0.952, MSE values of 0.265, and MAE values of 0.136 to predict the current alcohol concentration, compared with R2 values of 0.948, MSE values of 0.283, and MAE values of 0.146 when omitting the temperature. Similarly, when transferring models between mixing processes, accuracies of 92.2 % and R2 values of 0.947 were achieved when utilising the process temperature compared with 92.1% and 0.942 when omitting the temperature. When transferring models between cleaning processes, inclusion of the process temperature as a feature degraded model accuracy during classification tasks as omitting the temperature produced the highest accuracies for 6 out of 8 tasks. Mixed results were obtained for regression tasks where including the process temperature increased model accuracy for 3 out of 8 tasks. Overall, these results indicate that US sensing, for some applications, is able to achieve comparable accuracy when the process temperature is not available. The choice of whether to include the temperature as a feature should be made during the model validation stage to determine whether it improves prediction accuracy. The optimal feature extraction, feature selection, and ML algorithm permutation was determined as follows: Features were extracted by Convolutional Neural Networks (CNNs) followed by Principal Component Analysis (PCA) and inputted into deep neural networks with Long Short-Term Memory (LSTM) layers. The CNN was pre-trained on an auxiliary task using previously collected US datasets to learn features of the waveforms. The auxiliary task was to classify the dataset from which each US waveform originated. PCA was applied to reduce the dimensionality of the input data and enable the use of additional features, such as the US time of flight or measures of variation between consecutively acquired waveforms. This CNN and PCA feature extraction method was shown to produce more informative features from the US waveform compared to a traditional, coarse feature extraction approach, achieving higher accuracy on 65 % of tasks evaluated. The coarse feature method used commonly extracted parameters from US waveforms such as the energy, standard deviation, and skewness. LSTM units were used to learn the trajectory of the process features and so enable the use of information from previous timesteps to inform model prediction. Using LSTM units was shown to outperform neural networks with feature gradients used as inputs to incorporate information from previous timesteps for all process monitoring applications. Multi-task learning also showed improvements in learning feature trajectories and model accuracy (improving regression accuracy for 8 out of 18 tasks), however, at the expense of a greater number of hyperparameters to optimise. The choice to use multi-task learning should be evaluated during the validation stage of model development. Unlabelled and labelled domain adaptation were investigated to transfer ML knowledge between similar processes. Unlabelled domain adaptation was used to transfer trained ML models between similar mixing and similar cleaning processes to negate the need to collect labelled data for a new task. Transfer Component Analysis was compared to a Single Feature transfer method. Transferring a single feature was found to be optimal, achieving classification accuracies of up to 96.0% and 98.4% to predict whether the mixing or cleaning processes were complete and R2 of up to 0.947 and 0.999 to predict the time remaining for each process, respectively. The Single Feature method was most accurate as it was most representative of the changing material properties at the sensor measurement area. Training ML models across a greater process parameter range (a greater range of temperatures; 19.3 to 22.1°C compared with 19.8 to 21.2°C) or multiple datasets improved transfer learning to further datasets by enabling the models to adapt to a wider range of feature distributions. Labelled domain adaptation increased model accuracy on an industrial fermentation dataset by transferring ML knowledge from a laboratory fermentation dataset. Federated learning was investigated to maintain dataset privacy when applying transfer learning between datasets. The federated learning methodology performed better than the other methods tested, achieving higher accuracy for 14 out of 16 machine learning tasks compared with the base case model which was trained using data solely from the industrial fermentation. This was attributed to federated learning improving the gradient descent operation during network optimisation. During the federated learning training strategy, the local models were trained for a full epoch on each dataset before network weights were sent to the global model. In contrast, during the non-federated learning strategy, batches from each dataset were interspersed. Therefore, it is recommended that the order that the data is passed to the model during training should be evaluated during the validation stage. Overall, there are two main contributions from this thesis: Development of the ML pipeline for process monitoring using US sensors, and the development of unlabelled and labelled domain adaptation methods for process monitoring using US sensors. The development of an ML pipeline facilitates reduced time for the deployment of US sensor and ML combinations in industrial settings by recommending a method that has been trialled over a range of process monitoring applications. The unlabelled and labelled domain adaptation methods were developed to leverage previously collected datasets. This negates or reduces the burden of collecting labelled data in industrial environments. Furthermore, the pipeline and domain adaptation methodologies are evaluated using a non-invasive, reflection-mode US sensing technique. This technique is industrially relevant as it can be externally retrofitted onto existing process equipment. The novelty contained within this thesis can be summarised as follows: • The use of CNNs and LSTM layers for process monitoring using US sensor data: CNNs were used to extract spatial-invariant features from US sensor data to overcome problems of features shifting in the time domain due to changes in temperature or sound velocity. LSTM units were used for their ability to analyse sequences and understand temporal dependencies, critical for monitoring processes that develop over time. Feature extraction using CNNs was shown to produce more informative features from the US waveform compared to a traditional, coarse feature extraction approach, achieving higher accuracy on 65 % of tasks evaluated. LSTM units were shown to outperform neural networks with feature gradients used as inputs to incorporate information from previous timesteps for all process monitoring applications. • Evaluating the omission of the process temperature as a feature for process monitoring using US sensor data: This indicates whether the US sensor and ML combinations could be used in industrial applications where measurement of the process temperature is not available. Overall, it was found that ML models which omitted the process temperature were comparable in accuracy to those which included it as an input (for example, R2 values of 0.952, MSE values of 0.265, and MAE values of 0.136 when including temperature compared with R2 values of 0.948, MSE values of 0.283, and MAE values of 0.146 were obtained when omitting the temperature to predict the current alcohol concentration during laboratory scale fermentation processes). • The use of labelled and unlabelled domain adaptation for US data for process monitoring: Unlabelled domain adaptation was used to transfer trained ML models between similar mixing and similar cleaning processes to negate the need to collect labelled data for a new task. Labelled domain adaptation increased model accuracy on an industrial fermentation dataset by transferring ML knowledge from a laboratory fermentation dataset. • The use of labelled and unlabelled domain adaptation on features extracted from US waveforms: This allows the domain adaptation methods to be used for diverse US waveforms as, instead of aligning the US sensor data, the US waveform features are used which provide information about the process being monitored as they develop over time. • The use of federated learning and multi-task learning with US data: Federated learning was investigated to maintain dataset privacy when applying transfer learning between datasets. Multi-task learning was investigated to aid LSTM unit learning of the process trajectory. The federated learning methodology performed better than the other methods tested, achieving higher accuracy for 14 out of 16 ML tasks compared with the base case model. Multi-task learning also showed improvements in learning feature trajectories and model accuracy (improving regression accuracy for 8 out of 18 tasks evaluated), however, at the expense of a greater number of hyperparameters to optimise. • The use of data augmentation for US data for process monitoring applications: Data augmentation was a component of the convolutional feature extraction method developed in this thesis. Data augmentation artificially increased the dataset size to train the convolutional feature extractor while ensuring that features specific to each waveform, rather than the position or magnitude of features, were learned. This improved the feature-learning auxiliary task the CNN was trained to perform which classified the dataset from which each previously collected US waveform originated

Book of abstracts of the 10th International Chemical and Biological Engineering Conference: CHEMPOR 2008

This book contains the extended abstracts presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, over 3 days, from the 4th to the 6th of September, 2008. Previous editions took place in Lisboa (1975, 1889, 1998), Braga (1978), Póvoa de Varzim (1981), Coimbra (1985, 2005), Porto (1993), and Aveiro (2001). The conference was jointly organized by the University of Minho, “Ordem dos Engenheiros”, and the IBB - Institute for Biotechnology and Bioengineering with the usual support of the “Sociedade Portuguesa de Química” and, by the first time, of the “Sociedade Portuguesa de Biotecnologia”. Thirty years elapsed since CHEMPOR was held at the University of Minho, organized by T.R. Bott, D. Allen, A. Bridgwater, J.J.B. Romero, L.J.S. Soares and J.D.R.S. Pinheiro. We are fortunate to have Profs. Bott, Soares and Pinheiro in the Honor Committee of this 10th edition, under the high Patronage of his Excellency the President of the Portuguese Republic, Prof. Aníbal Cavaco Silva. The opening ceremony will confer Prof. Bott with a “Long Term Achievement” award acknowledging the important contribution Prof. Bott brought along more than 30 years to the development of the Chemical Engineering science, to the launch of CHEMPOR series and specially to the University of Minho. Prof. Bott’s inaugural lecture will address the importance of effective energy management in processing operations, particularly in the effectiveness of heat recovery and the associated reduction in greenhouse gas emission from combustion processes. The CHEMPOR series traditionally brings together both young and established researchers and end users to discuss recent developments in different areas of Chemical Engineering. The scope of this edition is broadening out by including the Biological Engineering research. One of the major core areas of the conference program is life quality, due to the importance that Chemical and Biological Engineering plays in this area. “Integration of Life Sciences & Engineering” and “Sustainable Process-Product Development through Green Chemistry” are two of the leading themes with papers addressing such important issues. This is complemented with additional leading themes including “Advancing the Chemical and Biological Engineering Fundamentals”, “Multi-Scale and/or Multi-Disciplinary Approach to Process-Product Innovation”, “Systematic Methods and Tools for Managing the Complexity”, and “Educating Chemical and Biological Engineers for Coming Challenges” which define the extended abstracts arrangements along this book. A total of 516 extended abstracts are included in the book, consisting of 7 invited lecturers, 15 keynote, 105 short oral presentations given in 5 parallel sessions, along with 6 slots for viewing 389 poster presentations. Full papers are jointly included in the companion Proceedings in CD-ROM. All papers have been reviewed and we are grateful to the members of scientific and organizing committees for their evaluations. It was an intensive task since 610 submitted abstracts from 45 countries were received. It has been an honor for us to contribute to setting up CHEMPOR 2008 during almost two years. We wish to thank the authors who have contributed to yield a high scientific standard to the program. We are thankful to the sponsors who have contributed decisively to this event. We also extend our gratefulness to all those who, through their dedicated efforts, have assisted us in this task. On behalf of the Scientific and Organizing Committees we wish you that together with an interesting reading, the scientific program and the social moments organized will be memorable for all.Fundação para a Ciência e a Tecnologia (FCT

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018