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Additively manufactured carbon fiber reinforced-polyamide component: a study based on statistical modeling and regression analysis

聚酰胺 组分(热力学) 回归分析 统计分析 材料科学 复合材料 线性回归 成分分析 统计模型 回归 统计 数学 物理 热力学
作者
Dhinakaran Veeman,Hrishikesh Dutta,Murugan Vellaisamy
出处
期刊:Engineering research express [IOP Publishing]
卷期号:6 (3): 035433-035433 被引量:1
标识
DOI:10.1088/2631-8695/ad7a52
摘要

Abstract This paper presents the experimental assessment of the hardness characteristic of additively manufactured polyamide (PA 6) composite reinforced with carbon micro-fibers. The carbon fiber-reinforced polyamide (CFPA) components were manufactured using the additive manufacturing technique—fused deposition modeling (FDM). The experiments were conducted for testing the hardness of the samples using a Shore-D hardness tester. The novel contributions of the work towards the manufacturing fraternity include selecting a scantly researched material like CFPA, and the elaborative investigation of hardness variation with the alteration of the prime parameters pertaining to FDM. The effect of the print-related parameters, namely, layer height (LH), infill density (ID), and raster orientation (RO) on the hardness of the CFPA component was studied, and the results were analyzed using statistical analysis tool ‘analysis of variance (ANOVA)’. Moreover, a regression model was developed to predict the output response, i.e. hardness for different combinations of the input parameters. Considering an ID of 100% and an RO of 0°, the hardness value of 93.89 at 0.1 mm LH reduced to 88.44 at 0.3 mm LH, depicting a reduction of 5.81%. An increasing trend was observed for hardness with the increase in ID for all the levels of LH and RO. The highest value of hardness (93.89) was achieved at an ID of 100%, with the LH and RO values kept at 0.1 mm and 0°, respectively. The ANOVA suggested that the effect of all three parameters is significant in the study, ID being the most affecting parameter with an effect contribution of 37.88%. The fitness of the adopted model was well justified by the high R-sq value of 0.9618 and significantly low error values in the range of 0.002–0.08.

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