Integration of Multivariate Statistical Control Chart and Machine Learning to Identify the Abnormal Process Parameters for Polylactide with Glass Fiber Composites in Injection Molding; Part I: The Processing Parameter Optimization for Multiple Qualities of Polylactide/Glass Fiber Composites in Injection Molding

Abstract

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm2, bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Study of Synthesis of Dual-Curing Thermoplastic Polyurethane Hot-Melt Adhesive and Optimization by Using Gray Relational Analysis to Apply in Fabric Industry to Solve Seamless Bonding Issues

“…The ANOVA results for the influence of each control factor on the double-cured polyurethane hot-melt adhesive are shown in Table S8 (Supplementary Document) . The ANOVA was needed after the main effect analysis was completed for further confirmation of the significance and magnitude of the effect of each control factor [ 34 ]. The ANOVA showed that three important factors which affect the shear strength with F-values are, accordingly, the NCO:OH ratio, with an F-value of 362.33, the photocurable monomer, with an F-value of 42.37, and the polyol, with an F-value of 18.11 (if the F-value > 5, it is important, but if the F-value < 5, it is not important), which is consistent with the results obtained from the main effect analysis.…”

“…The S / N ratios were calculated for each experimental group using the obtained results from the peel strength analysis through the use of LTB characteristics [ 34 ], and they are presented in Table 8 . The response table and response plot of the main effect analysis are shown in Table 9 and Figure 9 , respectively.…”

Effects of process parameters on mechanical properties and interface of 3D printed bamboo‐inspired CCFR‐PLA/TPU composites

“…In orthogonal test, range analysis is often used to determine the influence of various factors on the results 49,50 . The above research shows that nozzle temperature, printing speed, extrusion width, and printing layer height will affect the mechanical properties of the specimen.…”