Speaker
Description
Manufacturing is the foundation of any industrialized country and involves making products from raw materials using various processes. Additive manufacturing (AM) was initially developed as a technique for rapid prototyping to visualize, test, and authenticate a design before end-user production of the design. FDM is the most commonly used additive manufacturing process for the construction of a product and/or prototype. It has many process parameters that affect the quality of manufactured products. The proper selection of process parameters is required to produce products at a low cost while enhancing mechanical properties, build time, part quality, etc. Therefore, in the past, researchers have optimized the process parameters to obtain the desired product. In the present study, an overview of FDM process parameters and a review of different design optimization methods are summarized. Some of the DOE techniques coupled with optimization methods such as the Taguchi method, Response Surface Method (RSM), Artificial Neural Networks (ANN), Factorial Design Method (FDM), Genetic Algorithm (GA), etc. are presented. The review period ranges from 2000 to july 2023. The present review work presents well-organized information in terms of objectives, process parameters, constraints, and optimization techniques for the optimization of FDM additive manufacturing techniques that can be beneficial for successful researchers to ascertain the gap in the research.
| Conference Topic Areas | Track8: Design Optimization, Additive Manufacturing Technologies & Applications |
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