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AbstractDuring machinability, the combination of machining process parameters and the material properties of the component to be machined plays an important role. Material properties depend upon the type of phase form present and the grain size of the formed phases, which in turn depends upon the prior treatment given to alter the initial room temperature types and form. Accordingly, spheroidization treatment was carried out on medium carbon steel (AISI1040) by altering the initial room temperature structure through normalizing and hardening treatment. Machinability experiments were performed on CNC machine by varying machining process constraints. Tool wear and surface roughness of the machined component obtained by turning were analyzed and correlated. Using Minitab and full factorial design, the ANOVA study was carried out. With the help of regression analysis, residual and main effect plots combined optimization (tool wear and surface roughness) was targeted. ANOVA result shows excellent machinability for the as-bought-spheroidized condition where feed has a 67% contribution to tool wear (TW) whereas the depth of cut has a 71.91% contribution to surface roughness (SR). Also, the optimized regression values obtained for machining parameters are feed (0.39 mm/rev), depth of cut (0.6 mm), and spindle speed (780 rpm) with composite desirability of 0.8174. TW and SR experimental values for the optimized machining parameters are 0.039 mm and 2.89 μm, respectively, and the difference between the actual and optimized values is less than 5%.

The adoption of Lean Six Sigma has become increasingly widespread in manufacturing industries worldwide. It is a powerful methodology for optimizing operational efficiency and quality, leading to increased productivity, reduced costs, and higher customer satisfaction. However, implementing LSS effectively, especially in Indian manufacturing sectors striving for sustainability, presents significant challenges. In this paper, the authors have presented the critical factors that impede the successful integration of sustainable Lean Six Sigma practices within the Indian manufacturing landscape. Through a systematic review of existing literature and empirical studies, authors have identified the eighteen failure factors. To prioritize these factors, authors have employed the Best Worst Method (BWM), a decision-making tool that aids in ranking factors based on their importance and impact. It is a multi-criterion decision-making (MCDM) methodology to solve different types of fact-based decision--making problems. This method involves initially identifying the best (most important) and worst (least important) factors, followed by pairwise comparisons to determine their relative significance. Results depict that "Failure of leadership to inspire and motivate", "Lack of well-defined framework for executing initiatives", and "High implementation cost and poor estimation of cost" are three top-level critical failure factors. [ABSTRACT FROM AUTHOR]

In recent years, abrasive waterjet machining has emerged as a promising machining technique for the machining of composites because of its non-thermal nature. In the present study, the effect of machining parameters on the quality of machining has been studied and the process parameters have been optimized to machine jute-fiber-reinforced polymer composites. The design of the experiment was used to predict the combination of the input parameters for L27 experiments. Taguchi and response surface methodology (RSM) techniques were employed to analyze the experimental data and identify the optimum combination of process parameters to achieve as little delamination as possible. The results indicate that an increase in the values of the parameters traverse speed and abrasive mass flow rate leads to an increase in the damage obtained. However, an increase in the value of the parameter stand-off distance minimizes the damage produced. To achieve minimum delamination, the optimum combination of input parameters is obtained through Taguchi and RSM. For the present experimental condition, to achieve minimum delamination, the parameter traverse speed should be set at 20 mm/min, the stand-off distance should be 4 mm, and the abrasive flow rate should be set at 0.25 kg/min. The results confirm that the optimum combination of parameters obtained through both approaches is similar. This investigation results indicated a significant improvement in the cutting quality with reduced damage, achieved through the optimized process parameters. For the considered range of parameters, graphs are plotted such that any intermediate values can be anticipated within the considered range without performing any further experiments. The present work signifies the effect of fiber orientation on delamination.

This project looks at the different maintenance philosophies and the importance of vibration analysis in predictive maintenance. Since most industries and plants make use of rotational equipment, vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages other related equipment and to avoid unwanted breakdowns and downtime. Vibration analysis can help increase the lifetime of equipment when the faults are diagnosed at the right time. Vibration analysis of a rotating table top model is also done to show that some faults might exist even though they are not visible to the naked eye.