The behavior of the minimum quantity lubricant (MQL) technique was analyzed under different lubricating and cooling conditions when grinding ABNT 4340 steel. The comparative analysis of the residual stress values showed that residual compressive stresses were obtained under all the lubrication/cooling conditions and types of abrasive tools employed. The highest residual compressive stress obtained with the aluminum oxide grinding wheel with MQL under the condition of V= 30m/s for air and V= 40ml/h for lubricant was -376MPa against the -160MPa attained with conventional cooling, representing a 135% increase in residual compressive stress. The results show that method and quantity of lubricant and cooling are factors that influence the grinding process

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg–Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as “sharp” (with cutting capacity) or “dull” (with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

Researchers have evaluated a great number of monitoring techniques in order to control the surface condition of ground parts. Piezoelectric diaphragms of lead zirconate titanate are used in many fields, but these sensors are not common in the monitoring of the machining processes. This paper proposes a method for monitoring the workpiece surface condition (normal grinding and burn) by using a piezoelectric diaphragm and feature extraction techniques. A comparison is made with a conventional acoustic emission sensor, which is a traditional sensor in the monitoring of the machining processes. Grinding tests were performed in a surface-grinding machine with Society of Automotive Engineers (SAE) 1045 steel and cubic boron nitride (CBN) grinding wheel, where the signals were collected at 2 MHz. The workpieces were thoroughly analyzed through visual inspection, surface roughness and hardness measurements, and metallographic analyses. Study on the frequency content of both signals was carried out in order to select bands closely related to the workpiece surface condition. Digital filters were applied to the raw signals and features were extracted and analyzed. The root mean square values filtered in the selected bands for both sensors presented a better fitting to the linear regression, which is highly desirable for setting a threshold to detect burn and implementing into a monitoring system. Also, the basic damage index results show an excellent behavior for grinding burn monitoring for both sensors. The method was verified by using a different grinding wheel, which clearly shows its effectiveness and demonstrates the potential use of the low-cost piezoelectric diaphragm for grinding burn monitoring.