Fatigue behavior of hybrid continuous-discontinuous fiber-reinforced sheet molding compound composites under application-related loading conditions

Hybrid continuous-discontinuous sheet molding compound (SMC) composites are considered suitable candidates for structural automotive applications, due to their high mass-specific mechanical properties combined with high geometrical flexibility and low costs. Since structural automotive parts are subject to repeated loading, profound knowledge of their fatigue behavior is required. This paper presents an experimental study on the bending fatigue behavior of hybrid SMC with discontinuous glass fibers in the core and unidirectional continuous carbon fibers in the face layers. Effects of hybridization on the S-N behavior and stiffness degradation have been analyzed in constant amplitude fatigue tests under 3-point bending load at different temperatures and frequencies. Microscopic investigations on polished specimen edges were used to study the damage behavior. The ultimate flexural strength at quasi-static (UFS$^S$) and fatigue strain rate (UFS$^F$) of the hybrid composite was 54 % and 59 % higher than that of discontinuous SMC, respectively. In contrast, the flexural fatigue strength at 2.6⋅10S$^6$ cycles increased by 258 %. The relative stiffness degradation of the hybrid composites was smaller during most of their fatigue lives due to the continuous carbon fiber reinforcement. The carbon fiber ply on the compression loaded side was the first ply to fail. Fatigue stress significantly decreased at 80 °C due to early kinking of the continuous carbon fiber-reinforced ply on the compression loaded side. Variation of frequency had no significant effect on the fatigue behavior of both discontinuous and continuous-discontinuous SMC

Fatigue behavior of hybrid continuous-discontinuous fiber-reinforced sheet molding compound composites under application-related loading conditions

Hybrid continuous-discontinuous sheet molding compound (SMC) composites are considered suitable candidates for structural automotive applications, due to their high mass-specific mechanical properties combined with high geometrical flexibility and low costs. Since structural automotive parts are subject to repeated loading, profound knowledge of their fatigue behavior is required. This paper presents an experimental study on the bending fatigue behavior of hybrid SMC with discontinuous glass fibers in the core and unidirectional continuous carbon fibers in the face layers. Effects of hybridization on the S-N behavior and stiffness degradation have been analyzed in constant amplitude fatigue tests under 3-point bending load at different temperatures and frequencies. Microscopic investigations on polished specimen edges were used to study the damage behavior. The ultimate flexural strength at quasi-static (UFSS) and fatigue strain rate (UFSF) of the hybrid composite was 54 % and 59 % higher than that of discontinuous SMC, respectively. In contrast, the flexural fatigue strength at 2.6⋅106 cycles increased by 258 %. The relative stiffness degradation of the hybrid composites was smaller during most of their fatigue lives due to the continuous carbon fiber reinforcement. The carbon fiber ply on the compression loaded side was the first ply to fail. Fatigue stress significantly decreased at 80 °C due to early kinking of the continuous carbon fiber-reinforced ply on the compression loaded side. Variation of frequency had no significant effect on the fatigue behavior of both discontinuous and continuous-discontinuous SMC

Study Of The Ability To Detect Humor In Visual Images By 2-5 Year Olds

Problem Understanding the impact that humor can have as a form of therapy has been studied mostly in relation to mental and spiritual healing. As a result, little focus has been given to understanding the types of humor and how one’s understanding of and appreciation for types of humor develop over time. Gaining an understanding of humor development is important due to discoveries that the use of humor is a great intervention tool when working with children. Nevertheless, the use of pictures (with humor) is often used within speech therapy sessions, but seldom used correctly due to the lack of understanding of humor development in children. Method This study was carried out by individually removing each participant from the classroom A total of 12 pictures were presented to each child (i.e. three groups of four pictures) via the iPad. When the first photo grid was presented, the experimenter directed the participant’s attention to the reference picture, and created a story line to explain the reference picture. Then the experimenter directed the participant’s attention to the other three pictures by saying, “…Point to the picture that makes you laugh the most.” The participant then selected from the three alternatives, with the expected selection to be the one of incongruency. Each participant was given a range of 0 to 90 seconds to observe each photo grid and select a response. Results A Pearson product-moment correlation coefficient showed that there was a positive correlation on all of the dependent variables (types of humor) and some independent variables (i.e. language and gender), as well as between gender and hyperbolic humor type. Repeated measures ANOVA resulted in significant difference in participants’ ability to correctly identify incongruent elements in types of humor based on gender, language and age. A multiple regression analysis was done and resulted in there being a high level of significance for the independent variables age grouping, gender and language skills to operate as successful predictors of overall correct identification of incongruence in the dependent variables. Conclusion It is important to take into consideration the age, gender and type of humor as well as the language skill level of each client, because these aspects could have a major impact on the success or failure of a session and overall work with a client

Study of material homogeneity in the long fiber thermoset injection molding process by image texture analysis

To quantify the homogeneity of fiber dispersion in short fiber-reinforced polymer composites, a method for image texture analysis of 3-dimensional X-ray micro computed tomography (µCT) images is presented in this work. The adaption of the method to the specific requirements of the composite material is accomplished using a statistical region merging approach. Subsequently, the method is applied for evaluating the homogeneity of specimens from an intermediate step of the long fiber thermoset injection molding process as well as molded parts. This new injection molding process enables the manufacturing of parts with a flexible combination of short and long glass fibers. By using a newly developed screw element based on the Maddock mixing element design, the material homogeneity of parts molded in the long fiber injection molding process is improved

Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images

Fiber orientation tensors (FOT) are used as a compact form of representing the mechanically important quantity of fiber orientation in fiber reinforced composites. While they can be obtained via image processing methods from micro computed tomography scans (CT), the specimen size needs to be sufficiently small for adequate resolution – especially in the case of carbon fibers. In order to avoid massive workload by scans and image evaluation when determining full-field FOT distributions for a plaque or a part, e.g., for comparison with process simulations, the possibilities of a direct interpolation of a few measured FOT at specific support points were opened in this paper. Hence, three different tensor interpolation methods were implemented and compared qualitatively with the help of visualization through tensor glyphs and quantitatively by calculating originally measured tensors at support points and evaluating the deviations. The methods compared in this work include two algebraic approaches, firstly, a Euclidean component averaging and secondly, a decomposition approach based on separate invariant and quaternion weighting, as well as an artificial intelligence (AI)-based method using an artificial neural network (ANN). While the decomposition method showed the best results visually, quantitatively the component averaging method and the neural network behaved better (that is for the type of quantitative error assessment used in this paper) with mean absolute errors of 0.105 and 0.114 when calculating previously measured tensors and comparing the components. With each method providing different advantages, the use for further application as well as necessary improvement is discussed. The authors would like to highlight the novelty of the methods being used with small and CT-based tensor datasets

Graduates

https://thekeep.eiu.edu/commencement_fall2015/1029/thumbnail.jp