Optimizing the Characteristics of the Laser Hardfacing Process Parameters to Maximize the Wear Resistance of Ni-Based Hard-Faced Deposits Using the RSM Technique

The nickel-based Colmonoy-5 hardfacing alloy is used to hard-face 316LN austenitic stainless steel components in fast reactors. The nominal composition (in wt%) was listed as follows: 0.01 C, 0.49 Si, 0.87 Mn, 17.09 Cr, 14.04 Ni, 2.56 Mo, 0.14 N, and balance Fe. Hardfacing is a technique of applying hard and wear-resistant materials to substrates that need abrasion resistance. The thickness of hardfacing deposit varies between 0.8 mm and 2 mm based on parameter combinations. In this study, laser hardfacing process parameters including laser power, powder feed rate, travel speed, and defocusing distance were optimized to reduce weight loss of laser hard-faced Ni-based deposit. The tribological characteristics of reactor-grade NiCr-B hard-faced deposits were investigated. The RSM technique was used to identify the most important control variables resulting in the least weight loss of the nickel-based alloy placed on AISI 316LN austenitic stainless steel. Statistical techniques like DoE and ANOVA are utilized. Changing the laser settings may efficiently track the weight loss of laser hard-faced nickel alloy surfaces. These are created using the response surface technique. The deposit produced with a laser power of 1314 W, powder feed rate of 9 g/min, travel speed of 366 mm/min, and defocusing distance of 32 mm had the lowest weight loss of 16.4 mg. Based on the F value, the powder feed rate is the major influencing factor to predict the hardness followed by power, travel speed, and defocusing distance.

OPTIMIZATION OF THE DESIGN AND OPERATIONAL PARAMETERS OF PLANTER FOR VEGETABLE PIGEON PEA (CAJANUS CAJAN L. MILLSP.) SEED

Precision machinery is one of the most important technology in the recent decades in respect to judicious use of resources. In precision machinery one of the most important machine is seeding machines because it picks the seed from the hopper and individually placed in field. An effort has been made to optimize the operational (forward speed and vacuum pressure) and design (nozzle diameter) parameters of the precision seed drill. For optimizing the metering mechanism three parameters i.e. nozzle diameters: 2.00, 2.50, 3.00, 3.50 and 4.0 mm; forward speed: 0.27, 0.55, 0.83, 1.11 and 1.38 m/s and vacuum pressure: 19.33, 39.32, 43.98, 58.64 and 68.63 kPa were selected. The seed to seed spacing was 300 mm. The RSM technique was used to optimize the above parameters. The machine was evaluated on the basis performance parameters like miss index, multiple index, quality of feed index and precision. The optimum value for forward speed, vacuum pressure and the nozzle holes diameter was 0.83 m/s, 43.98 kPa and 3.50 mm, respectively. The most important variable that governs planting phenomenon for vegetable pigeon pea seed is nozzle diameter as well as vacuum pressure.

Process modeling and optimization using ANN and RSM during dry turning of titanium alloy used in automotive industry

Lightweight and high strength of titanium alloys makes them suitable for automotive industry. Automobiles are in high demand in the future and new materials and technologies are continuously enhancing the performance of automobiles. This research work addresses the potential use and barrier faced by the automobile industry in using titanium alloy. In this experimental study, we tried to overcome the machining challenges faced by industries to machine widely used grade–5 titanium alloy. The machining is performed under a dry machining environment due to ecological and economic concerns. The controllable machining variables and their influence on the tool wear are determined. Statistical analysis is performed to assess the impact of controllable variables. As a result, the optimal parameters setting obtained using the desirability approach, and confirmation runs carried out at optimal conditions. Moreover, a mathematical relation developed using the RSM technique to predict the response. Later on, using better modeling techniques based on artificial intelligence, a better predictive model is developed which forms the basis of cyber physical system.