Acoustic maps are valuable tools for visualizing mechanical conditions in industrial processes, but their quality is often compromised by noise, interference, and low signal sensitivity. This work presents an enhanced methodology that combines the fusion of acoustic emission (AE) and vibration signals with digital image processing techniques to improve the visual quality of the generated maps. Pixel formation was refined using bidimensional rms, improving smoothing. Subsequently, a distribution adjustment block generated a composite signal with balanced contributions from AE and vibration, increasing image interpretability while preserving the relevance of each sensing modality. Experiments were conducted on an aluminum oxide grinding wheel employed in tangential surface grinding, which was dressed with a single-point diamond tool at different depths, with reference patterns in the shapes of plus (+) and tee (T) machined onto its surface. The resulting acoustic maps achieved a correlation index of 0.830 with the reference images, representing an average improvement of 22.87% compared to AE-only approaches. The proposed methodology proved particularly effective under intermediate and deep dressing conditions, offering a robust, noninvasive, and efficient solution for grinding wheel monitoring, with potential compatibility with digital manufacturing systems.

In certain applications of acoustic emission sensors, acoustic maps can be generated from captured signals. The work “In-Dressing Acoustic Map by Low-Cost Piezoelectric Transducer” introduces an innovative technique using these sensors to map grinding wheel surfaces, essential for finishing machined parts. However, producing sharp acoustic maps is challenging due to industrial interference. This study explores digital image processing techniques to enhance these maps, using cloud-based tools. Techniques such as smoothing, equalization, and edge detection (Sobel, Canny, Roberts, and Prewitt) were applied. The processed acoustic maps revealed sharper details, enabling more accurate assessments of dressing conditions. The results demonstrate the effectiveness of digital image processing when applied to acoustic maps, significantly improving the evaluation of the dressing process and contributing to the development of Industry 4.0.

The brewing industry is expanding with the rise of many small breweries. These are typically small and medium-sized enterprises producing a few hectoliters of beer per batch, often with limited investment capacity for equipment. Centrifugal pumps play a crucial role in microbreweries, facilitating the movement of wort throughout various stages of the brewing process. Failures in these systems, such as valve positioning issues or blockages, can lead to longer production times, increased energy consumption, and potential quality issues. This study explores a soft sensor approach for developing IFDs (Intelligent Fault Detection systems) by using pump drive data—current, torque, and power factor—without the need for additional sensors. Data were collected via a managed switch, and models were trained using Support Vector Machine and Multilayer Perceptron algorithms. The results indicate that this IFD method holds great potential for enhancing automation and maintenance in small breweries.

The condition of abrasive grinding wheels after the dressing process can be visualized through acoustic maps, which enable the identification of patterns associated with the wheel topography and surface characteristics. Initially, acoustic maps were generated exclusively from acoustic emission (AE) signals. Due to the low sharpness of these maps, resulting from the inherent nature of acoustic emission signals, multimodal fusion techniques combining acoustic emission and vibration signals (AE + Vib) were developed, as well as approaches based on digital image processing (AE + Vib + DIP). Traditionally, the quantitative evaluation of these acoustic maps has been based on cross-correlation, in which regions of interest in the topological map are compared with predefined patterns. However, this approach does not consider analyses related to background noise in the images, which can affect the sharpness and contrast of the acoustic maps, making their visual interpretation more difficult. Therefore, this study effectively evaluates the background noise in acoustic maps obtained using different methods. The study investigates background homogeneity and the spatial definition of regions of interest through statistical metrics and digital image processing techniques. The results demonstrate that the integration of multiple information sources significantly reduces background noise, leading to substantial improvements in the overall quality of the acoustic maps.

Fused Filament Fabrication (FFF) stands out due to its simple operating principle, low production cost, and wide range of available materials. However, the inherent anisotropy of thermoplastics frequently leads to interlinear void formation during deposition, compromising the mechanical properties of printed parts. Currently, the lack of effective in situ monitoring methods for the spatial localization of defects restricts the widespread adoption of FFF in industrial applications. As an alternative, vibration-assisted inspection has gained recognition as a developing non-invasive technique for process data acquisition. To improve knowledge to this topic, this study proposes extracting dynamic characteristic curves from objects constructed via FFF. The proposed methodology involves introducing two identical electrodynamic transducers in a pitch–catch configuration, where one transducer acts as an emitter and the other as a receiver, both integrated into the printer’s heated bed. The energy propagating through the bed reflects the vibration signals of the part under construction, which are collected by the receiver transducer. The results present a novel methodology to locate structural damage indicators during object fabrication by characterizing the medium, the heated bed and, consequently, the printed part itself. Thus, it was possible to identify the highest-magnitude bands and, based on them, generate acoustic maps that detect potential anomalies during the process.