Experimental studies have been carried out to establish the possibility of using vibratory machining technology through shock-wave transmission for oxide coating preparation on aluminum-alloyed machine components and also to discuss the technological possibilities of applying vibration mechanochemical solid lubricant coatings based on MoS2 to improve the surface quality and performance properties of machine component parts. The coating characteristics are determined by measuring and comparing certain tribological properties of the samples before processing, after normal coating, and after vibratory coating process. A deeper study with a scanning microscope was made by comparing result of normal and vibratory coating. The vibratory coating shows a reduction of grain sizes, a regular orientation of the grain, and a dense grain structure leading to the formation of a thin layer covered by a film orientated parallel to the surface of friction giving an imparted surface finish. The reduction of microroughness is also accompanied with good performances in terms of increasing in wear resistance and decreasing in coefficient of friction. This reflects the presence of complex influence of mechanical and chemical components in the formation of coating on superficial layers during lower shock-wave vibration giving at the end structured ameliorated state of surface that leads to an increase in the part lifespan and equally shows technological opportunities that can be used to improve surface quality and performance properties of machine component parts.

Maintaining the quality of breathing air in urban and industrial areas is one of the biggest challenges faced by humanity in the modern era. Diesel engines, as one of the main providers of energy supply for modern equipment and transport, are also unfortunately contributing highly to the deterioration of air quality. A recent research path on the limitation of diesel engine emissions is the use of alternative fuel from vegetable or animal fats or oil called biodiesel. Although the use of biodiesel has proven its efficiency in reducing emissions, it remains a problem to maintain the engine’s efficiency when shifting to biodiesel, especially due to its injection and atomization properties; most of the recent research focused on improving biodiesel fuel quality by blending it with traditional diesel fuel, but few works can be found on the regulation or control of diesel engine process when shifting to 100% biodiesel fuel (B100). This work proposes a fuel control strategy and methodology based on diesel engine operating data obtained from an experimentally designed rate of injection model (ROI) at different injection pressures and a jet and spray droplet distribution validated a two-zone model. Results show that B100 gives a higher amount of about 8% of injected fuel, a longer jet penetration of about 20 mm higher at 100 MPa injection pressure, a wider cone angle, and about a 40% increase of coarseness of the jet distribution. The experimental and numerical-based control strategy provides interacting relationships between B100 properties and specific engine features where actions shall be made to keep the engine’s efficiency when the shift is made; meanwhile, the algorithm provides a hierarchical step-by-step correcting procedure taking into account the possible degradation that could occur from the use of B100 in diesel engines.

This work investigates the effect of low frequency vibratory processing for cleaning and washing various machine components parts from rusts and old paints deposits. The experimental investigation was carried out with special prepared samples that were weighted and exposed to paints and rust contaminants. These samples were treated in universal horizontal vibration machine UVHM 4 × 10 with different combination of instrumental processing medium, process fluid, machine amplitude and frequency of oscillations. They were periodically reweighted after processing and compared to etalon with control of quantity of dust that have been removed, sample cleanliness and also other functional parameters. Statistical analysis has been used to characterize ongoing process and full factorial analysis to establish experimental parameters dependency. The result is showing the complex dependence of samples cleanliness to each processing parameters like processing time, amplitude of oscillations, frequency of oscillations, process fluid parameters, instrumental medium, etc. Between this parameters although the most important successively the amplitude of oscillations, the frequency of oscillations the processing medium and the processing fluid depending to his considered composition, the optimal processing time can be reach only by complex combination of all this parameters every of them carry an amplify coefficient. Low frequency oscillations can be used to monitor and optimize washing and cleaning operations of paints and rusts contaminations. That guarantees process automation, its effectiveness for a large industrial application.

The objective of this work is to improve the combustion management in W18V50DF dual fuel engine by determining for a desired power, the optimal values of the parameters of pressures and subsequently to map them in real time based on a power set point. The interest was mainly focused in pressure parameters, other being considered as constant. Two methods have been used, namely mathematical modeling and learning by neural networks. The results show that, in the beginning mathematical modeling result helps to monitor the ongoing process and with longer learning period the result with neural network become better and significant due to the adaptation to the reality. Furthermore, the neural network method improves significantly in the long term the rationalization of fuel consumption in such a system in order to significantly reduce the carbon dioxide emission rate. Finally, work has proved that for an immediate result mathematical model can be used but without robustness on the control process, this is obtained by a neural network. But this approach requires a good data base and long learning time.