Metal casting process involves processes such as pattern making, moulding and melting etc. Casting defects occur due to combination of

various processes even though efforts are taken to control them. The first step in the defect analysis is to identify the major casting defect

among the many casting defects. Then the analysis is to be made to find the root cause of the particular defect. Moreover, it is especially

difficult to identify the root causes of the defect. Therefore, a systematic method is required to identify the root cause of the defect among

possible causes, consequently specific remedial measures have to be implemented to control them. This paper presents a systematic

procedure to identify the root cause of shrinkage defect in an automobile body casting (SG 500/7) and control it by the application of

Pareto chart and Ishikawa diagram. with quantitative Weightage. It was found that the root causes were larger volume section in the cope,

insufficient feeding of riser and insufficient poured metal in the riser. The necessary remedial measures were taken and castings were

reproduced. The shrinkage defect in the castings was completely eliminated.

This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi L16 orthogonal array. The results indicate that feed rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the optimal cutting parameters to maximize productivity while improving surface quality.