Search Results (9)

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection. Full article

Porous structures exhibiting randomly sized and distributed pores are required in biomedical applications (producing implants), materials science (developing cermet-based materials with desired properties), engineering applications (objects having controlled mass and energy transfer properties), and smart agriculture (devices for soilless cultivation). In most cases, a scaffold-based method is used to design porous structures. This approach fails to produce randomly sized and distributed pores, which is a pressing need as far as the aforementioned application areas are concerned. Thus, more effective porous structure design methods are required. This article presents how to utilize fractal geometry to model porous structures and then print them using 3D printing technology. A mathematical procedure was developed to create stochastic point clouds using the affine maps of a predefined Iterative Function Systems (IFS)-based fractal. In addition, a method is developed to modify a given IFS fractal-generated point cloud. The modification process controls the self-similarity levels of the fractal and ultimately results in a model of porous structure exhibiting randomly sized and distributed pores. The model can be transformed into a 3D Computer-Aided Design (CAD) model using voxel-based modeling or other means for digitization and 3D printing. The efficacy of the proposed method is demonstrated by transforming the Sierpinski Carpet (an IFS-based fractal) into 3D-printed porous structures with randomly sized and distributed pores. Other IFS-based fractals than the Sierpinski Carpet can be used to model and fabricate porous structures effectively. This issue remains open for further research. Full article

Industry 4.0 requires phenomenon twins to functionalize the relevant systems (e.g., cyber-physical systems). A phenomenon twin means a computable virtual abstraction of a real phenomenon. In order to systematize the construction process of a phenomenon twin, this study proposes a system defined as the phenomenon twin construction system. It consists of three components, namely the input, processing, and output components. Among these components, the processing component is the most critical one that digitally models, simulates, and validates a given phenomenon extracting information from the input component. What kind of modeling, simulation, and validation approaches should be used while constructing the processing component for a given phenomenon is a research question. This study answers this question using the case of surface roughness—a complex phenomenon associated with all material removal processes. Accordingly, this study shows that for modeling the surface roughness of a machined surface, the approach called semantic modeling is more effective than the conventional approach called the Markov chain. It is also found that to validate whether or not a simulated surface roughness resembles the expected roughness, the outcomes of the possibility distribution-based computing and DNA-based computing are more effective than the outcomes of a conventional computing wherein the arithmetic mean height of surface roughness is calculated. Thus, apart from the conventional computing approaches, the leading edge computational intelligence-based approaches can digitize manufacturing processes more effectively. Full article

Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process. Full article

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices. Full article

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation. Full article

The grinding process is usually one of the last stages in the manufacturing process chain since it can provide superior surface finish and closer dimensional tolerances. However, due to peculiarities of the grinding process, a workpiece material is susceptible to many problems, and demands a reliable real-time monitoring system. Some grinding monitoring systems have been proposed by means of sensors. However, the literature is still scarce in terms of employing time–frequency analysis techniques during the grinding of ceramics. Thus, this paper proposes an application of a low-cost piezoelectric transducer (PZT) in the analysis of the surface quality of ceramic workpieces during the grinding process by means of the frequency–time domain technique along with the ratio of power (ROP) parameter. An integrated, high-cost, commonly-used acoustic emission (AE) sensor was employed in order to compare the results with the low-cost PZT transducer. Tests were performed using a surface grinding machine. Three depth of cut values were selected in order to represent slight, moderate, and severe grinding conditions. Signals were collected at 2 MHz. The short-time Fourier transform (STFT) was studied in order to obtain the frequency variations over time. An analysis of the ROP values was performed in order to establish a correlation with the surface roughness. The ROP values are highly desirable for setting a threshold to detect the workpiece surface quality and for implementing it into a monitoring system. The results using the PZT transducer showed a great similarity to those obtained using the AE sensor. Full article

Grinding is one of the most commonly used finishing processes in the manufacture of precision components that also needs to be monitored. Monitoring of the workpiece surface quality is considered highly complex due to particularities of the cutting tool and material removal mechanism. In this context, the monitoring of the grinding process is very important for the metalworking industry and a topic of great interest for machining researchers. Many studies on grinding process monitoring have been developed and most of them focus on process automation. The objective of this work is to monitor the workpiece material removal during grinding by using piezoelectric transducers in the emitter and receiver modes along with digital signal-processing techniques. Tests were performed on a peripheral surface grinding machine equipped with an aluminum oxide grinding wheel. The SAE 4340 steel grade was used as workpiece material. The transducer signals were sampled at a sampling frequency of 2 MHz. The digital signal processing was performed through spectrum analysis and the application of techniques such as root mean square. The mass of the workpieces was measured by means of a digital scale prior to and after grinding tests. The number of grinding passes was varied in order to increase the material removal. The results show that the monitoring technique proposed in this work is sensitive to the material removal in the grinding process. The appropriate selection of frequency bands allows for the best diagnosis in relation to the events that occur during the grinding process. Full article