Investigation on surface finish and metallic particle emission during machining of aluminum alloys using response surface methodology and desirability functions

The surface finish of a mechanical part plays an important role as it determines the part’s field performance. The machining parameters and conditions governing the part surface finish also impact on the other machining process performance indicators such as tool wear, tool life, cycle time, machining cost, and undesirable emissions of aerosols and metallic particles. In today’s metal cutting industry, a major concern is the occupational safety and health hazard associated with cutting fluids usage and metallic particle emission. It is therefore necessary to determine machining conditions that could improve the part surface finish while maintaining low the aerosol emission. In this research study, statistical methods are used to study the surface finish parameters and the metallic particle emissions during milling of aluminum alloys (6061-T6, 7075-T6, and 2024-T351) with two coated carbide tools (TiCN and a multilayer TiCN?+?Al₂O₃?+?TiN). Following an implementation of multilevel design of experiment, machining trials and determination of mains most influential factors, surface responses and desirability functions are used to determine the best process operational conditions and windows. The results of this research demonstrate that TiCN-coated tool generates fewer respirable airborne particles during machining than multilayers TiCN?+?Al₂O₃?+?TiN-coated tool. Overall, it is shown that the use of TiCN coating tool provides a better opportunity for an environmentally benign dry machining along with improvement on surface quality.     

The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines

Bearings are known as the vital parts of machines, and their condition is often critical to the success of an operation or process. Presence of a film of lubricant such as grease between the bearing surfaces minimizes the friction and surface wear. Contaminated grease or lack of lubricant may lead to an ineffective bearing performance or malfunction of the machinery parts. Therefore, in order to avoid unexpected breakdowns, reliable and robust bearing condition monitoring techniques are demanded. According to previous studies, acoustic emission (AE) signals contain valuable information that can be used for bearing condition monitoring and fault detection. The main objective of this study is to evaluate the effectiveness of AE signal parameters to distinguish between lubricated and dry bearings under similar operating conditions. To this end, a low-speed rotating test rig is manufactured and used. Eight levels of rotational speeds and four levels of radial loads were applied to the test rig shaft end, which is connected to the testing bearing. In each test, seven time domain AE parameters were computed. The statistical tools were also used to present the dominant experimental variables on AE signal parameters. According to experimental results, it was found that four AE parameters can be used to distinguish between dry and lubricated bearings.     

Fine and Ultrafine Particle Characterization and Modeling in High-Speed Milling of 6061-T6 Aluminum Alloy

In this study, an experimental investigation was carried out on fine and ultrafine metallic dust emission during high-speed milling of 6061-T6 aluminum alloy in wet and dry conditions. Measurements of dust emission were conducted using a scanning mobility particle sizer spectrometer and an aerodynamic particle sizer spectrometer. These instruments were used to characterize particles in the micrometer and the nanometer size ranges. It was confirmed that the machining process produces nanoparticles as small as 10 nm and that the characteristics of the generated nanoparticles are not significantly influenced by the cutting conditions. The cutting forces and chip compression ratio were measured to validate the proposed dust generation model based on an energy approach. Good agreement was observed between the model and the experimental measurements for the investigated conditions. It was demonstrated that the majority of generated dust is caused by deformations in the primary shear zone. In addition, the percentage of generated dust is significantly influenced by deformation conditions in the chip formation zone. It was found that high cutting speeds could reduce the percentage of the generated particles during the milling process.     

A force-temperature model including a constitutive law for Dry High Speed Milling of aluminium alloys

In this study, a new analytical cutting force model was developed for the Dry High Speed Milling of aluminium alloys. The proposed model requires only work-material properties and cutting conditions to estimate the cutting forces and the temperature during end-milling processes. The transformation from orthogonal-to-oblique cutting was used to study the end-milling operation. A new method, which includes a Needleman-Lemonds constitutive equation, was introduced to simultaneously determine the average shear stress and the cutting temperature in the primary and secondary shear zones. The model validation was performed by comparing the prediction for machining Al6061-T6 and Al7075-T6 aluminium to mechanistic model results and experimental results. The computed temperature results were compared to published data. A good agreement was observed for both force and temperature results, for the investigated cutting conditions.     

The Effect of the Heat Treatment on the Dust Emission During Machining of an Al-7Si-Mg Cast Alloys

This paper reports on the effect of artificial aging on the machinability of Al-7Si-Mg (A356) cast alloys for the as-received alloy, solution heat-treated (SHT) alloy and then aged SHT alloy at 155, 180, and 220 °C, respectively. The influence of heat treatment on the machinability of the alloys studied was considered using innovative criteria such as dust emission. The effect of various lubrication modes including dry, mist, and wet process, as well as cutting speed and feed rate, was also investigated. The results obtained from the statistically designed experiments indicate that at the same cutting conditions, the A356-T7 heat treatment generates less dust emission level compared to other various heat treatments (there is 32% less airborne swarf produced than with A356-T6). Aging at low temperature was observed to produce the greatest level of the dust emission while the aging at higher temperatures is accompanied by a reduction in the dust emission level. Fracture surface analysis using scanning electron microscope, has shown that dust emission levels were strongly dependent on the nature of the fracture surface of the alloys studied, with different heat treatments. A change in chip formation was also found to be a function of age hardening and dust emission during machining of the tested aluminum alloy. A correlation was established between the cutting speed, the feed rate, and the dust emission, which is useful for determining the conditions required for minimal dust emission.     

Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis

The stability of high-speed machining operations determines the reliability of machine tools and the quality of machined parts. Chatter-free cutting conditions are difficult to predict as they require accurately estimated dynamic modal parameters. A spectrogram analysis and impact tests for different configurations of the machine tools were conducted to compare the modal parameters at 0 rpm tests and during machining tests. Variations of between 2% and 8% were observed for the natural frequencies and between 2 and 10 times for the damping ratios.The operational modal analysis (OMA) is considered as a powerful tool for dynamic modal parameter estimations during machining operations. A complete methodology for applying this technique for machining operations was detailed. It was demonstrate how the OMA can be industrially exploited. The proposed approach was successfully applied during the high-speed machining of the 7075-T6 aluminum alloy to extract machine-tool parameters. Two different numerical approaches were used: the autoregressive moving average method (ARMA) and the least square complex exponential method (LSCE), both of which generated similar results. The dynamic parameters found using the operational modal analysis were used to predict machine dynamic stability lobes, and through experimental validation, it was shown that some depths of cut that are stable with standard stability lobes become unstable with dynamic stability lobes.