Fins are a type of heat exchanger that is attached to a part of the product and is mainly used to enhance the heat transfer rates. Fins are generally manufactured using conventional manufacturing (CM) methods such as extrusion, die casting, and forging. Additive manufacturing (AM) is a modern manufacturing technique that is one of the emerging methods to manufacture metallic components such as fins. Some of the advantages of using AM is that it is much more cost-efficient, reduces a lot of material wastage than CM methods, as well as more time-efficient. The AM process, which will be used for the manufacture of fins, is either Directed Energy Deposition (DED) or Powder Bed Fusion (PBF). AM printed fins can even be used in applications that require components to operate at very high temperatures. In this research paper, the effect on heat transfer rates of the fins manufactured using different AM techniques is carried. Furthermore, an analysis of the thermal properties and heat transfer rates of multiple 3D printable materials will be conducted using the ANSYS Workbench Mechanical 2018 software. These will then be compared with fins manufactured with CM techniques. The expected research outcomes are that the fins manufactured using AM techniques will show better thermal properties than the CM method, and hence AM will be a great replacement for CM techniques given the introduction of more 3D printable materials in near future.
The use of hand trolleys plays an important role in all construction and manufacturing industries. Hand trolleys are designed and manufactured according to various governing features like load-capacity, materials, and ergonomic design. The fundamental objective of a hand trolley is to provide a hassle-free mode of transporting heavy objects from one location to another. It is a well-known fact that the demand for consumer products increases day by day and the technological advancements and development in the industries contribute to the increase in the demand for products in the market. To meet this demand, production facilities must be expanded and the speed of manufacturing and assembly must be increased. The hand trolleys that exist today are not optimized for assembly. Therefore, the assembly times are huge and this drives up the cost of production drastically. Design For Assembly (DFA) evaluation is an accurate and systematic methodology to evaluate how well a product is designed for assembly. Each component must be conceptualized and designed in such a way that it aligns and mates efficiently and easily. This includes the design and processing of the component in a specific manner with respect to shape, size, tolerances, and surface finish. A hand trolley often is an assembly of several individual parts. Every individual part has to be planned, designed, and manufactured separately. Hence, when individual components are designed and manufactured with ease of assembly in mind, the result is a significant reduction in assembly time. This results in savings of resources and capital.
The surface finish of ground samples is highly influenced by the grinding parameters, grinding conditions and the type of grinding wheel. This paper emphasizes on the effect of various grinding factors such as the grinding conditions, the type of grinding wheel and operating process parameters like depth of cut and table speed on the surface roughness of the ground samples. Two types of grinding wheels alumina (Al2O3) and cubic boron nitride (CBN) were used for grinding AISI D3 tool steel under dry and wet conditions. The material removal rate and surface roughness were evaluated for all the ground samples. The results showed that wet grinding outperformed dry grinding and provided a better surface finish while using both grinding wheels. Machine Learning was implemented to optimize the grinding parameters. Multi-objective optimization using genetic algorithm was done and a Pareto frontier chart was made to help determine what values for the input parameters would achieve the required outputs such as material removal rate and surface roughness. Two different approaches Genetic Algorithm and Principle Component Analysis were then compared for multi-objective optimization. The type of grinding wheel used had a dominant effect on the surface roughness of ground samples while the depth of cut had a lesser effect.
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