Effect of the impact conditions on the mechanical properties of injection‐molded parts

Barbosa, Carlos N.; Viana, J. C.; Franzen, Markus; Simões, Ricardo

doi:10.1002/pen.23135

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…coefficients in the estimated model divided by the number of experiments. 36 Central composite design. CCD is a first-order (2 k ) design augmented by additional center and axial points to allow estimation of the tuning parameters of a second-order model.…”

Section: Rsmmentioning

confidence: 99%

Search for accurate RSM metamodels for structural engineering

Kalita

Dey

Haldar

2019

Journal of Reinforced Plastics and Composites

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With rapid advancement in computing power and development of numerical tools and scientific theories in fields like structural engineering, simple experiments can now be carried out in-silico. However, simulating many real-life phenomena in analytical fields still remains largely intractable or requires huge computational resources. A number of researchers have developed suitable metamodels to reduce the computational time needed to solve complex structural problems. As such, response surface method has become quite popular due to its versatility and ability to reduce even the most hard-to-model engineering problems into a simple polynomial form. The number and type of sampling points needed for building the response surface approximation are selected by design of experimentation techniques like Box-Behnken design, central composite design, D-optimal design, etc. One design may be appropriate for some particular problems, while a different design would perform better on others. To assess the performance of such metamodels, statistical measures like R 2 and error-based metrics like root-mean-squared error have been commonly used by researchers. In this paper, extensive numerical experiments are performed to gauge the performance of Box-Behnken design, central composite design and D-optimal designs as a metamodeling tool in structural engineering. The insufficiency of classical statistical accuracy measures like R 2 , R 2 adj and R 2 pred is demonstrated, and the need for measures based on external test data such as Q 2 F1 , Q 2 F2 or Q 2 F3 is stressed. Subsequently, some recommendations are also made on building metamodels for structural engineering problems.

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Section: Rsmmentioning

confidence: 99%

Search for accurate RSM metamodels for structural engineering

Kalita

Dey

Haldar

2019

Journal of Reinforced Plastics and Composites

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“…The complex relationships among processing conditions, morphology and mechanical properties of injection molded thermoplastics is a very important research area, both in industrial and academic settings. Design of experiments (DOE) and other techniques have been used as powerful approaches to achieve increased understanding of the IM process, again contributing to significant improvements in product and process quality while decreasing the manufacturing costs .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive study on the relationships between the processing, the microstructure, and mechanical properties of injection molded polypropylenes

Barbosa

Simões

Franzen

et al. 2017

Polymer Engineering & Sci

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The relationship between the morphologies of different injection molded polypropylenes and their tensile properties measured at high strain rate were investigated. A threefold systematic analysis is proposed by establishing the following relationships: (i) processing‐morphology; (ii) processing‐mechanical properties; and (iii) morphology‐mechanical properties. Experimental techniques were used to assess the structural heterogeneity of the moldings, namely: polarized light microscopy (skin thickness), wide‐angle X‐ray scattering (crystallinity, molecular orientation, and polymorphism of the skin layer), and differential scanning calorimetry (bulk crystallinity). High velocity tensile tests (1 m/s) were recorded by a high speed camera that runs 20k frames per second. The influence of multiple processing variables on the morphology and properties was studied adopting a structured statistical approach by means of two statistical tools: analysis of variance and response surface methodology. The most important molding variables and their interactions were identified. Straightforward relationships between the morphology and the tensile properties were established by fitting the experimental data to polynomial equations, using a least‐square minimization procedure. The melt temperature was identified as the most significant variable for the development of the morphologies and the mechanical response of the moldings. The skin thickness, its crystallinity and molecular orientation (flow direction) influence, to a certain degree, the tensile properties of the materials. POLYM. ENG. SCI., 58:E215–E225, 2018. © 2017 Society of Plastics Engineers

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