This work investigates the cut quality characteristics of SS321 using plasma arc cutting. The SS321 has a wide range of applications such as in chemical storage, exhaust manifolds of automotives and aircraft. The intricate shapes for this material are very difficult to cut using conventional machining process. Hence, plasma arc cutting is used. The input cutting parameters are cutting speed, current, stand off distance and gas pressure. For each input parameter, three levels are considered and, therefore, total numbers of experimental runs are 3 9 3 9 3 9 3 = 81. To minimize the number of runs, the Taguchi L9 orthogonal array is proposed which is having advantages of both minimum and maximum trial runs. The output parameters are surface roughness, kerf width and heat-affected zone. The experiments are carried out in Micro Step spol S.R.O. Plasma arc cutting machine. To find the best cutting parameters, the regression models are given as input of Matlab-Genetic Algorithm. The test results show that ANOVA models are significant. It is inferred that lower values of current and Standoff Distance give better surface roughness and minimum heat-affected zone.
Use of machine learning and artificial intelligence (AI) to analyze the complex interdependencies of production dataset has gained momentum in recent years. Machine learning and predictive algorithms are now used by manufacturers to fine-tune the quality of their products. WEDM of SS304 with process parameters such as pulse-on-time (Ton), pulse-off-time (T off), current (I), and voltage (V) was varied to study the effect of machining parameters such as Material Removal Rate (MRR) and surface roughness. Experiments were planned and executed according to the L’9 orthogonal array. Scanning Electron Microscope (SEM) was utilized to study the machined surface. An analysis of variance (ANOVA) was performed to determine the input and output significance. ANOVA results revealed that V (81.85%) and Toff (77.75 %) for surface roughness. Further to determine the relationship between variables, various regression models based on machine learning was tested. The effectiveness of the regression models were tested. From their output it was concluded that the multilayer perception model had the highest correlation coefficient (0.999) for MRR while for surface roughness it was (0.995).
In recent years, applications of Machine Learning and Artificial Intelligence are gaining momentum to the production researchers to analyze the complex interdependencies present in the production dataset. The manufacturers have started to incorporate machine learning approaches to the production process & predictive algorithms to fine-tune the quality of the product. The objective of the proposed work is to apply classification and regression algorithms to analyze the input process parameters for the pack boronizing process of SS410. To prepare the dataset, 9 experiments were carried out and the test specimens having ø 55 mm & thickness of 10 mm are pack boronized using the boronizing agent 325 mesh size. This process is carried out in 4.5 kW 'INDFURR' electric furnace with varying input parameters of temperature, time and gas pressure. The output parameters are boronizing thickness and microvickers hardness. The SEM and optical microscopic images of the specimen confirm the formation of the boronizing layer. To find the influence parameters, it is analyzed using ANOVA and Decision Tree algorithm. Both the techniques confirmed that time as the most significant parameter for boronizing thickness. For surface hardness, time & temperature are the major influencing parameters. Various regression models from machine learning were formulated to find the relationship between variables. Among these models, multilayer perception produced maximum correlation co-efficient & minimum root mean square error.
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