Honing processes provide a special cross-hatch pattern to the internal surface of cylinders that favors oil flow. However, along honing operation the abrasive grains wear out and lose their ability to cut material. The honing chips mixed with oil fill the pores of the abrasives and they start cutting in an incorrect way, leading to clogging. In the present paper, honing experiments were carried out according to a 3² factorial design, with different grain size and density of abrasive grains. Roughness, material removal rate, and tool wear were determined. Acoustic emissions were also measured and the chirplet concept was applied in order to detect differences between correct and incorrect cutting operations. As a general trend roughness and material removal rate increase with grain size and with density of abrasive. However, when clogging occurs roughness and material removal rate decrease, because the abrasive grains tend to deform the material instead of cutting it. When the honing process is working appropriately, the chirplet diagram of the harmonic part of the signal shows constant marks. On the contrary, when it does not work properly, marks disappear with time and their frequencies decrease. The results of the present paper will allow monitoring the honing process in order to change the abrasives when they are not working properly.

In this work, the analysis of the acoustic emission (AE) signal in grinding processes is addressed. The proposed analysis method decomposes the acoustic signal into three frequency ranges. The total energy of each range is determined, as well as the highest frequency. Different grinding experiments were carried out, according to a full factorial design of experiments (DOE), in which feed speed, depth of cut, and transversal step (table cross feed) were varied. Arithmetic average roughness Ra and the material removal rate (MRR) were determined. It was observed that Ra depends mainly on the transversal step, followed by feed speed and the interaction between the transversal step and depth of cut, while MRR is greatly influenced by the transversal step. According to multi-objective optimization with the Derringer–Suich function, in order to simultaneously minimize Ra and maximize MRR, a transversal step of 9 mm per longitudinal pass, feed speed of 20 m/min, and depth of cut of 0.020 mm should be selected.