This study focuses on the development of a compact model with improved interpretability compared to similar approaches, relating thermomechanical pulp (TMP) properties, quantified using a fiber analyzer, to Canadian standard freeness and handsheet properties. The data used in this study are obtained from TMP produced by a conical disc refiner. Utilizing the LASSO-regularized Latent Variable Regression (LASSO-LVR) model, we identified three key latent variables – representing shives content, fibrillation, and slender fines content – that accurately predict eight distinct handsheet properties. In a subsequent analysis, we investigated the linkage between refiner settings and Specific Refining Energy (SRE) to these key analyzer readings and, consequently, to handsheet properties. The inclusion of SRE as an internal state variable in the model significantly enhanced predictive accuracy, providing a foundation for more precise and energy-efficient control strategies in refining processes. 

Nitrocarburization is a process for surface hardening of metallic components. For quality control purposes, regular inspection of the achieved hard surface layer, known as the compound layer thickness, is necessary. This is done by cutting the samples and inspecting a cross-section with microscopy. Since this is a destructive and time-consuming technique, it limits the frequency of the testing, and hence there is a need to complement this method with a rapid, non-destructive alternative. This paper demonstrates how ultrasound in combination with a supervised learning approach, can be used to estimate compound layer thickness down to 10-20 microns.

The paper explores Structural Health Monitoring (SHM) using ultrasonic guided waves, to detect structural damage. Guided waves are highly sensitive to changes in material properties and environmental and operating conditions (EOCs), with temperature being a significant factor. A key challenge in guided waves based SHM is differentiating between damage and temperature-induced changes. This paper focuses on warping-based methods for temperature compensation. Dynamic Time-Warping (DTW) encounters challenges due to its quadratic complexity. However, applying constraints to DTW accelerates the warping process by limiting the scope of the warping path to specified areas. The applied constraint should align with the characteristics of the signal. In this paper, we propose a global constraint for temperature compensation of guided waves, referred to as the Triangular global constraint (Tri-DTW). The performance of the proposed method will be compared with the Sakoe-Chiba global constraint (SC-DTW). Tri-DTW performs well, demonstrating better warping performance with four times reduced computational complexity. The analysis also includes comparisons of warping performance, warping performance with respect to temperature and damage detection performance.

Additive manufacturing is known for producing complex metal components, particularly with materials like 316L stainless steel. However, ensuring the quality and microstructural consistency of such components remains a challenge, as traditional testing methods are often destructive and time-intensive. Data driven models that are used for non-destructive evaluation are often difficult to interpret. This study explores the use ultrasound measurements combined with a multivariate statistical technique (partial least squares), to estimate the material properties of steel samples and examining the relationships between ultrasound signals at various frequencies and material properties such as porosity, grain size, and hardness. This aims to enhance the interpretability of ultrasound testing for additive manufacturing. Our findings indicate that ultrasound backscatter can be effectively linked to key material properties.

Additive Manufacturing is used for printing parts with high precision and complex geometries, but achieving consistent material properties and avoiding defects is a challenge. This paper presents the use of ultrasound technology as a non-destructive method to optimize the additive manufacturing process. A factorial design is used to print 18 samples using the key process parameters such as Power, Speed, and Hatch Distance. The ultrasound measurements are carried out using a 7.5 MHz focused transducer to capture within-sample variation. The manufacturing parameters and ultrasound variation metric is converted to a response surface model which is then used to identify optimal manufacturing conditions that can help minimize process induced variation and get a consistent microstructure and achieve consistent mechanical properties.

Metal based additive manufacturing techniques such as laser powder bed fusion can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel samples. The steel samples are manufactured with varying process parameters (speed, hatch distance and power) in two batches that are placed at different locations on the build plate. These samples are examined with ultrasound using a focused transducer. The ultrasound scans are performed in a dense grid in the build and transverse direction, respectively. Part of the ultrasound data are used to train a partial least squares regression algorithm by labelling the data with the corresponding manufacturing parameters (speed, hatch distance and power, and build plate location). The remaining data are used for testing of the resulting model. To assess the uncertainty of the method, a Monte-Carlo simulation approach is adopted, providing a confidence interval for the predicted manufacturing parameters. The analysis is performed in both the build and transverse direction. Since the material is anisotropic, results show that there are differences, but that the manufacturing parameters has an effect of the material microstructure in both directions.

Introduction: Guided waves being highly sensitive to temperature variations, temperature compensation algorithms, such as Optimal Baseline Selection, Baseline Signal Stretch and Scale Transform demonstrate effective performance under limited conditions. Dynamic Time Warping (DTW) has shown excellent compensation performance, however it comes with a substantial computational burden of O(N*N), where N represents the number of samples in each signal. Methodology: DTW works by construction of cost matrix that maps every point in one time series to all the points in the other time series, that results in complexity O(N*N).This problem can be solved by narrowing the search window using global constraints. The two most common constraints in the literature are the Sakoe-Chiba band and the Itakura Parallelogram. This paper uses Sakoe-Chiba band as a global constraint, the Sakoe-Chiba band is defined through a window size parameter which determines the largest temporal shift allowed from the diagonal. Temperature compensation performance of DTW Sakoe-Chiba is tested using the available OGW dataset #2 provided by Jochen Moll et al. The OGW dataset has been generated using 12 number of piezoelectric transducers bounded to CFRP (Carbon Fiber Reinforced Plastic) plate and varying the temperature from 20-60 degree with an increment of 0.5 degree. The signal is recorded for 1300 microseconds, that results in 13000 samples/time stamps(N). Initial results show that the Sakoe-Chiba constraint based DTW performs well and at a significantly lower cost, as indicated below. Result: The largest temporal shift (r) is estimated using Local Peak Coherence (LPC),for signal at 20 and 60 degree r is 135 samples. This results in complexity O(N*2r) which is very less than conventional DTW compensation technique O(N*N). In the final paper, the analysis will be replicated across a range of temperatures, and the performance of damage detection will be thoroughly discussed.

This paper presents a feasibility study of an ultrasonic backscatter communication system designed for low-power, short-range data transmission, suitable for applications in IoT, biomedical implants, and underwater sensor networks. Our proposed system utilizes a very low power passive network to encode information by modulating the reflection properties of the backscatter medium. We demonstrate through experimental analysis the effective data transmission and detection. The key contribution of this study includes the development of a practical experimental setup using ultrasonic transducers, backscatter modulators, alongside signal processing techniques for optimal data extraction. Results indicate the successful data transmission and detection for a limited number of bits in a very high SNR regime, showcasing the performance of the transmission protocol.