This study focused on the modeling and production of the injection moulded Polyvinylchloride-Sawdust (PVC-sawdust) composite. The PVC material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the central composite design (CCD). The two screw plunger injection moulding machine with maximum clamping force of 120 tons and shot capacity of 3.0oz was used to produce Polyvinylchloride-Sawdust (PVC-Sawdust) composite at various temperature. The produced composites were evaluated for their mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology (RSM) was used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced PVC-sawdust composite. The optimization results for PVC-Sawdust composite shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with values of 43.70MPa, 48.38MPa, 61.41MPa and 3.42GPa respectively obtained at barrel temperature of 224.65 o C and polymer level of 61.46% respectively while percentage elongation and average deflection were minimized with values of 65.43% and 4.23 cm respectively. A desirability of 0.952 was obtained which shows the adequacy of the model terms. The models were validated using coefficient of determination (R 2 ). The coefficient of determination (R 2 ) obtained ranged from 0.9213 (92.13%) to 0.981 (98.10%) which indicates that a substantial good fit was achieved by the model developed. The values obtained from the validation of these models were therefore found to be satisfactory, and shows good predictability of the model and its adequacy.

This study focuses on the optimisation of the injection moulded Polypropylene-Sawdust composite. The Polypropylene material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the design of experiments using the central composite design (CCD). The two screw plunger injection moulding machine was used to produce Polypropylene-Sawdust composite at various temperature. The produced composite was evaluated for its mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology (RSM) and artificial neural networks (ANN) were used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced Polypropylene-sawdust composite. The models were validated using coefficient of determination (R 2), the coefficient of determination (R 2) obtained ranged from 0.9435 (94.357%) to 0.9988 (99.88%) which indicates that a substantial good fit was achieved by the developed models. A desirability of 0.952 was obtained which shows the adequacy of the model terms the optimization results for Polypropylene-Sawdust composite shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with a values of 31.90 MPa, 41.94 MPa, 88.22 MPa and 2.72 GPa respectively which was obtained at barrel temperature of 224.65 o C. Percentage elongation and average deflection were minimized with a values of 74.12% and 6.46 cm respectively. The artificial neural networks gave the optimal of the two examined models.

The interaction of process parameters in the production of polyvinyl chloride-grass composite poses great challenges in the polymeric industries. Most manufacturing processes of injection moulded polyvinyl chloride-grass composite products have been by trial and error due to inadequate knowledge in process parameters and their interactions. This study investigates the effects of process parameters such as percentage by volume of material, material type, barrel temperature and their interactions on the mechanical properties of the produced polyvinyl chloride-grass composite using split-split plot design. The results of the calculated Fisher's ratio (F_cal) at significant value of 0.05 for the process parameters and their interactions ranges from-855.35 to 1.00, and were presented on ANOVA table. The results obtained shows that these process parameters contribute significantly to the production of Polyvinyl chloride-grass composite in polymeric industries.

In the machining world, development of predictive models is one remedy to reducing tool failure and its associated challenges like reduction in integrity of machined parts, production shutdown and idle time for machine operators. In this research, we want to show how robust the Finite Element (ANSYS) method is, by comparing its predictive capacity to the experimental machining operation.To achieve the scope of the study, Seventeen (17) varying set of experiments were conducted for the cutting tool using the three levels Box-Behnken's design (BBD) of experiment at varying process parameters of 200-600 rpm spindle speed, 0.05-0.15mm/rev feed rate and 0.5 -1.5mm depth of cut. During, the orthogonal cutting of AISI 1010 mild steel measuring 200mm length by 44mm diameter, the electrical strain gauge connected to the Electronic strain meter E10 was used to measure the tools' induced strains from where the equivalent von mises stresses were generated for the research. The finite element software was then used to model the HSS tool for prediction of the concerned response based on the designed matrix generated by the Design Expert. The experimental values were compared with the ANSYS simulated values using the absolute mean percentage error and the reliability plot.At the end, both the experimental and FEM (ANSYS) readings were in close agreements with a mean absolute percentage error of 0.544%. Therefore, this research has clearly shown that ANSYS is a very robust expert tool that can be used to model and predict von mises stresses in HSS cutting tool.

This study demonstrated the effectiveness of the finite element method in predicting the cutting temperature in carbide inserted cutting tool. A three-factor Box-Behnken design, making seventeen cutting tests of a cylindrical mild steel bar with 200 mm length by von mises diameter was employed to study the numerical and the experimental predictions. The data obtained from the tool tests was compared and statistically analyzed using the reliability plots. On comparison, both the experimental and finite element method (FEM) analysis by ANSYS ® readings were in close agreements, with the minimum and maximum error of 0.010% and 0.895%, respectively. In conclusion, the research clearly shows that ANSYS ® is a very efficient expert tool for modeling and predicting the cutting temperature of carbide insert cutting tool in dry turning operation using mild steel.