Mechanical Engineering Objective By Ds Kumar Pdf Free Download

[Lucrecio Poinson]

Theobjective of the design course is to teach senior engineering students a top-down systems design process that enables and encourages innovation. Students then apply this process to a challenging design project. Thus, the nature and scope of potential projects are broad and open to discussion. Capstone design course emphasizing the application of analytical/computer and experimental methods to the solution of a broad range of practical problems in mechanical engineering. Integrates knowledge gained from all required mechanical engineering courses in a major system design project. This is a two semester course, where the students work as a team under supervision of a faculty mentor.

Our senior design project provides students with a unique opportunity to apply their classroom knowledge to real-world engineering challenges. By partnering with industry leaders, we aim to enhance the quality and impact of our project while also fostering valuable connections between academia and industry.

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We believe that your company's expertise and resources would greatly enrich our senior design project. Your sponsorship can take various forms, including financial support, provision of equipment or materials, mentorship, or any other contribution that aligns with your organization's goals and capabilities.

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The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the on-demand necessity to perform surgery during space missions. Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing. Among all 3D printing techniques, fused deposition modelling (FDM) is a low-cost and more rapid printing technique. This article proposes the fabrication of surgical instruments, namely, forceps and hemostat using the fused deposition modeling (FDM) process. Excellent mechanical properties are the only indicator to judge the quality of the functional parts. The mechanical properties of FDM-processed parts depend on various process parameters. These parameters are layer height, infill pattern, top/bottom pattern, number of top/bottom layers, infill density, flow, number of shells, printing temperature, build plate temperature, printing speed, and fan speed. Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid (PLA) parts printed by FDM. The experiments have performed through Taguchi's L27 orthogonal array (OA). Variance analysis (ANOVA) ascertains the significance of the process parameters and their percent contributions to the evaluation indexes. Finally, as a multi-objective optimization technique, grey relational analysis (GRA) obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties. Scanning electron microscopy (SEM) examines the types of defects and strong bonding between rasters. The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength (42.6 MPa) and modulus of elasticity (3274 MPa).

Additive manufacturing (AM), alternatively known as 3D printing (3DP), is continuously proving itself as a revolutionizing technique due to a remarkable reduction in product development cycle time in designing and rapid manufacturing of a new product. The AM can build even a complex part with a high level of customization in a single step without requiring specific material handling equipment. Due to this, it is an attractive technology for modern-day applications, including biomedical, engineering, intricate objects, automotive components, and green manufacturing of parts.

In the fused deposition modelling (FDM) process, the 3D object builds additively from the bottom to the top layer in the exact shape of the CAD model. The 3D geometrical CAD model of an object converts into STL format, then uploaded into apposite software, where the STL file slices in layers before sending it to the 3D printer machine. The FDM observes striking a balance between cost-effectiveness and strength of parts for a given thermoplastic polymer; therefore, it is pretty popular among novice researchers for quick prototyping and manufacturing functional parts [1, 2].

It is evident from the literature review that various researchers have studied the mechanical properties of the FDM printed parts by considering the influence of a limited number of process parameters. There is thus an enormous scope to thoroughly investigate the mechanical properties of the biopolymer PLA parts printed via the FDM process under the consideration of process parameters as more as possible. Several researchers have performed multi-objective optimization with principal component analysis (PCA), WPCA-based desirability function, RSM-based fuzzy-logic, Taguchi method coupled with non-dominated sorting genetic algorithm II, etc. In the current scenario, Taguchi-based grey-relational analysis has also gained substantial importance because of transforming multi-responses into a single function that is, of course, easy to handle. In actual practice, the fabricated part must possess more than one mechanical property concurrently to overcome various loadings. Therefore, it is necessary to trade off various mechanical properties in a fabricated part per the customers' demand. Grey relational analysis has been employed for multi-response optimization of quality characteristics to address this concern.

This paper mainly explores the influence of process parameters on the mechanical properties of the FDM printed parts. These parameters are layer height (A), infill pattern (B), top/bottom pattern (C), number of top/bottom layers (D), infill density (E), flow (F), number of shells (G), printing temperature (H), build plate temperature (I), printing speed (J), and fan speed (K). All discussed process parameters in the literature survey are available in the latest slicer software Ultimaker Cura 4.9.1. In order to identify the feasible range of process parameters performing screening experimentation that follows a one-factor-at-a-time approach. The levels of the printing parameters were selected as given in Table 1.

ASTM D638 16 standard considered to design the 3D model of the tensile test specimen in the solid-work software. Figure 1 indicates the schematic diagram and dimensions of the specimen. A common desktop printer, Delta Wasp 2040 Turbo2, is used for rapid manufacturing and prototyping of small-scale functional products, as shown in Figure 2. The printer is a single extruder having a nozzle diameter of 0.4 mm, which uses a filament of 1.75 mm diameter. This study considered 11 control parameters, and their values were adjusted by open-source slicer software, Ultimaker Cura 4.9.1. The experiments are designed based on Taguchi's DOE approach. Here, 11 process parameters, each with three levels, are considered, which have 22 degrees of freedom (DOFs). Therefore, Taguchi's L27 (313) OA has 26 DOFs specifically chosen for the experimentation. Many factors under consideration eliminated this article's scope of the interaction study. A flat position (X-Y plane) in which the deposition of the fused filament aligns in the direction of pulling was selected to print the samples through 3D printer. Each experiment is replicated three times for printing tensile test specimens. The experimental raw data of testing and signal-to-noise (S/N) ratio data for all responses indicate in Table 2. A direct contact micro-computer-controlled electronic horizontal extensometer (Model PC-2000, capacity 20 kN) measured the tensile stress and drew the stress-strain diagrams of each test with a crosshead speed of 3 mm/s. The slope of the stress-strain diagram determines the modulus of elasticity (E).

This paper describes a grey-based Taguchi method for optimizing multi-response manufacturing problems. At first, the optimization is done with the Taguchi method for each response separately, and then subsequently, the multi-response values are dealt with grey relational analysis (GRA).

Taguchi Method performed the optimization of performance characteristics of the FDM process. It is also primarily used to improve product quality by pursuing a methodical approach towards using a unique OA design based on the number of process parameters for conducting a small number of experiments and analyzing the experimental results. Taguchi determined the loss function as a deviation between the experimental and target values. Subsequently, the loss function expresses in terms of mean squared deviation (MSD) and, thus, signal-to-noise (S/N) ratio. Each trial of experiments had conducted three times to obtain the MSD because both measures, average and variability, are contained in the MSD. The replication determines a variance index called the signal-to-noise (S/N) ratio [24]. The S/N ratio was determined using Eq. (1) to analyze further:

The MSD is defined differently for each quality characteristic. In this paper, only one type of quality characteristic, "larger the better", is chosen for two responses: Tensile strength and modulus of elasticity. Eq. (2) represents the corresponding expressions for MSD for the "larger the better" type of response:

ANOVA as a standard analysis tool ascertains the significance of the factors at a 95% confidence level and their percent contributions in affecting the responses. Comparing the calculated values of the F-ratio with the tabulated value of the F-ratio at a stated significance level counts the significance or insignificance of a particular factor. A factor is said to be significant if its calculated F-ratio is greater than the tabulated F-ratio. If any, the insignificant factors pooled, and the pooled ANOVAs for the two responses, as shown in Table 3.

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