Nanoparticles dispersed in lubricants are being studied for their ability to reduce friction and wear. This paper examines SAE 5W-30 oil enhanced with dispersed graphene nanoplates for tribological and rheological properties. Graphene nanoplate (GNs) concentration effects on the rheological and tribological properties of 5W-30 base oil (0.03, 0.06, 0.09, 0.12, and 0.15 wt percent) were tested. Under various loads, a four-ball testing model was used to conduct a tribological analysis (200, 400, 600, and 800 N). Kinematic viscosity is calculated, and base oil and nanofluid-added 5W30 lubricant are compared for thermal conductivity and flashpoint. Wear scar and coefficient of friction improved by 15% and 33% with nano-additives. When related to the base oil, the flashpoint, thermal conductivity, kinematic viscosity, and pour point all increased, by 25.4%, 77.4%, 29.9%, and 35.4%, respectively. The addition of GNs improved the properties of 5W30 engine oil.

Fused Deposition Modeling (FDM) is a Layered Manufacturing (LM) process in which progressive 2D layers of material are kept making a 3D part. To optimize the building operation, investigation is necessary to study the effect of process parameters. This study examines the impact of three filling patterns “Triangles”, “Cross” and “Cross 3D” with three filling densities (25%, 50%, and 75%) in three orientations (“Flat”, “on long edge”, and “on short edge”) on Ultimate Tensile Strength (UTS), hardness and the printing time of Polylactic Acid (PLA) material. In this work, tensile specimens were built according to ASTM D638 on an open-source 3D printer. The UTS were collected using WDW-2000 computer control electronic universal testing machine. Also, the hardiness value was measured using shore A hardness durometers DIN 53505 and ASTEM D2240. In addition, the building time was conducted by implementing “Cura 4.6.” slicer software. The results show that the filling pattern, orientation, and density, at which the part is built, have a significant effect on the strength, hardness, and building time of the part. for light structure parts 25% density, it is recommended to build part with “Triangles” “on long edge” to obtain the highest strength (31.02MPa) which improved by 74.3%, with (281HV) at min. time (1hr:10min) which reduced by 46.1%. For dense structure parts, “Triangles”, “on long edge” are recommended to give the highest strength (42.12MPa), which be improved by 69.8%, (282HV) at min. time (1hr:35min) which reduced by 36.2%. For medium construction 50% the parts can be built by the following parameters “Cross”, “on long edge” to obtain the highest strength (38.48MPa) with (283HV) at min. possible time (1hr:55min).

In additive manufacturing (AM), it is necessary to study the surface roughness, which affected the building parameters such as layer thickness and building orientation. Some AM machines have minimum layer thickness that doesn't satisfy the desired roughness. Also, it produces a fine surface that isn't required. This increases the building time and cost without any benefits. To overcome these problems and achieve a certain surface roughness, a prediction model is proposed in this chapter. Regression models were used to predict the surface roughness through the building orientation. ANN was used to predict the surface roughness through the building orientation and the layer thickness together. ANN was constructed based on experimental work that study the effect of layer thickness and building orientation on the surface roughness. Some data were used in the training process and others were used in the verification process. The results show that the layer thickness parameter has an effect more than the building orientation parameter on the surface roughness.

Background Additive manufacturing method is used for manufacturing of solid three-dimensional parts. It requires less human efforts and manufacturing time for parts is less. Different process parameters such as layer thickness, building orientation, infill type, and infill percentage affect the building time, model cost, mechanical properties, and surface roughness. The presented paper develops an algorithm for adapting layers and generating tool-paths. This algorithm can improve the fabrication efficiency and geometrical accuracy in the additive manufacturing (AM) of complex models. The proposed algorithm consists of three modules that identify the optimal process parameters, named as part building orientation, layer thickness, strategy type for internal filling, and slope of the tool-path. Results The input is the PTS file that contains the points of the layers contour of the computer-aided design (CAD) model. All the modules for the proposed algorithm were implemented using the MATLAB R2019a programming language software. The main finding results showed that the fabrication with an adaptive layer thickness was more time-efficient. The build time was reduced up to 47.3%. The developed tool-path generation strategies (contour offset and zigzag line tool-path) can effectively balance the AM surface quality and fabrication efficiency requirements. Conclusion In this research, the AM users can benefit by saving the cost and time. The parts were fabricated with a high degree of accuracy, and the surface finish was suitable for determining the optimal process parameter.