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A fuzzy set based cost model for additive manufacturing is proposed. The model is illustrated through an example of selective laser sintering (SLS), a commonly used additive manufacturing (AM) process. Initially, a deterministic cost model is developed. It is then converted to a fuzzy set based model by considering uncertainty in build time estimation and cost components. For the specific case of SLS, a build time estimation procedure is developed as a part of the cost model. A method to evaluate the reliability of the cost estimate in AM and its significance is also introduced. The cost model is illustrated through two examples of products manufactured by SLS.
相似文献Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.
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