The main objective of this study is to review existing research on the application of fused deposition modeling (FDM) for 3D printing of continuous fiber reinforced composites (CFRCs). An overview of additive manufacturing technology production techniques is provided first, followed by a look into FDM technology. The articles on CFRC printing were then summarized. The type of reinforcing material and matrix utilized, the studied parameters, the mechanical tests, and their results, are all listed. Various pre-processing, processing, and post-processing conditions, as well as their impact on CFRC mechanical properties, were also discussed. Finally, several study gaps were identified and suggestions for further research were presented.
Fixture error is one of the error sources in machining operations. Locator position inaccuracy and locator height error are the main sources of fixture error. The optimal positions of the locators are a critical problem for minimizing the geometrical and dimensional error of workpiece. This article proposes a genetic algorithm–based optimization method to arrive at a layout of locators for minimum machining error in 3-2-1 locating approach. The focus of this optimization is the positional tolerance of holes. So, a mathematical model of the hole position tolerance with respect to variation of locator position is developed. The planes of the workpiece actual coordinate system are mathematically modeled on the workpiece theoretical coordinate system. The capability of the proposed approach has been shown by using an example. The result shows that the proposed genetic algorithm method can be used to calculate locating errors and find the optimal locating layout within the specified tolerance range, which is critical for fixture design in hole-making process.
Setup planning and fixture design are two main tasks for the integration of design and manufacturing process. Setup planning identifies which features must be machined in each setup and determines locating datum for each setup, whereas fixture planning determines precise locating and rigid clamping of workpieces according to a part design and process requirements. So, a close interaction exists between setup planning and fixture design. This research deals with the problems of setup and fixture planning for the machining of prismatic parts. In the first step, a new heuristic method is presented to plan the setups with accurate respect to datum faces in design and manufacturing. Two concepts, namely, ''inferiority face'' and ''control face,'' have been used for this purpose. In the next step, a mathematical model is used to define the primary, secondary and tertiary fixture planes with respect to locators' position. This model determines relationship between misplacement of locators and dimensional and geometrical specifications of workpiece. The main purpose of developing the model is to determine the effect of locator height error on hole position tolerance. The capability of this model is verified by simulation in ''motion study'' module in SolidWorks software. This approach is very useful in the hole-making process. The system is developed in Visual Basic on a SolidWorks platform. The effectiveness of the system is verified by an industrial component.
Comprehensive process planning is the key technology for linking design and the manufacturing process and is a rather complex and difficult task. Setup planning has a basic role in computer-aided process planning (CAPP) and significantly affects the overall cost and quality of machined parts. This paper presents a generative system and particle swarm optimisation algorithm (PSO) approach to the setup planning of a given part. The proposed approach and optimisation methodology analyses constraints such as the TAD (tool approach direction), the tolerance relation between features and feature precedence relations, to generate all possible process plans using the workshop resource database. Tolerance relation analysis has a significant impact on setup planning to obtain part accuracy. Based on technological constraints, the PSO algorithm approach, which adopts the feature-based representation, optimises the setup planning using cost indices. To avoid becoming trapped in local optima and to explore the search space extensively, several new operators have been developed to improve the particles' movements, combined into a modified PSO algorithm. A practical case study is illustrated to demonstrate the effectiveness of the algorithm in optimising the setup planning.
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