Fabio Isaac Ferreira

The monitoring of manufacturing processes is essential to ensure the quality and structural integrity of the produced parts. However, several processes still require human intervention for defect detection, such as 3-D printing. This paper investigates the potential of a monitoring system based on the electromechanical impedance (EMI) technique to identify defects in 3-D printed parts. A piezoelectric diaphragm was affixed to the print bed, and different printing conditions were used to manufacture the parts. The experimental results demonstrated that the EMI technique can infer defect areas by calculating the root mean square deviation (RMSD) index from the imaginary part of the impedance at a frequency range of (123.5-124.0) kHz. Notably, the diaphragm used in this study was obtained at a cost of less than one US dollar, and its performance exhibited a strong correlation. These observations suggest that the proposed system possesses considerable potential for effective monitoring of the 3-D printing process.

One of the most promising monitoring techniques is based on electromechanical impedance (EMI) transducers, which are low-cost components and allow easy implementation. EMI signal features showed good results for some applications in previous studies, but it is still unexplored for machining processes, which is essential for metal-cutting industries. This paper presents a new approach to verify the applicability of EMI measurements to monitor surface quality after the plunge cylindrical grinding of non-flat parts, which require low roughness and tight dimensional tolerances. Tests were carried out in a camshaft grinder and two low-cost piezoelectric diaphragms were attached to each workpiece to guarantee redundancy. Roughness $\text{R}_{\mathbf {a}}$ was also monitored to check existence of correlation with the EMI signals and relevant discussions regarding monitoring by EMI are presented. Results showed a great correlation between EMI measurement and surface roughness: 0.94 and 0.80 for both diaphragms. Since these diaphragms cost less than 1 US dollar and good correlation was verified, this monitoring system is promising to replace the traditional ones.

New alternatives for monitoring the ceramic grinding process have been studied. Monitoring vibration signals is one of the most successful methods because some characteristics that describe the behaviour and influence of the process on ground parts are only noticeable by studying such signals. This study aims to monitor the finishing of advanced ceramics during the surface grinding process via digital processing of the vibration signals. Experimental tests were performed using a surface tangential grinding machine with a diamond grinding wheel and alumina (Al ₂ O ₃ ) test specimens. The vibration signal was measured by an accelerometer and recorded by an oscilloscope at a 2 MHz sampling rate. The tests were conducted at different depths of cut for two workpiece speeds ( v _w ) under mild and severe machining conditions. Confocal microscopy and surface roughness R _a measurements were performed after grinding each workpiece to classify the samples. Digital signal processing was performed to achieve feature extraction. A frequency range of 800 Hz–2 kHz was most strongly related to the post‐grinding ceramic condition. A correlation was found between vibration and integrity of the ceramic workpiece because the vibration signal was proportional to the surface roughness for each cutting depth used. To support the conclusion presented, a statistical analysis through variance by analysis of variance was performed.

The grinding process is a manufacturing process that is performed at the end of the machining chain. In such step, the workpiece has a high added-value and therefore critical problems are not allowed, as they may lead to scrap and significant financial loss. Regenerative chatter is the most common form of self-excited vibration. It can occur often because most metal cutting operations involve overlapping cuts which can be a source of vibration amplification. The occurrence of the chatter phenomenon in the grinding process is considered a critical problem because it causes several negative effects, such as poor surface quality, unacceptable inaccuracy, and disproportionate tool wear, to name a few. The objective of this research work is to monitor the onset of the chatter phenomenon in grinding of SAE 1045 steel with an aluminum oxide grinding wheel by digital signal processing techniques of the acceleration signals. Experimental tests were performed in a surface grinding machine and using a data acquisition system with sampling frequency of 2 MHz. The data was analyzed with respect to the frequency content and the Ratio of Power (ROP) statistical parameter obtained for normal and chatter conditions. Visual inspections through digital images of the workpieces surfaces were conducted and then chatter frequency was determined. Results showed that the magnitudes related to the frequencies close to 30 Hz change as chatter phenomenon occurs. The ROP shows features related to the chatter vibrations. The methodology presented herein therefore allows for the automation and optimization of the grinding process.

Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re‐establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single‐point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter ‘counts’ was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.