The impact of process parameters on test specimen deviations and their correlation with AE signals captured during the injection moulding cycle

Kusić, Dragan; Kek, Tomaž; Slabe, Janez Marko; Svečko, Rajko; Grum, Janez

doi:10.1016/j.polymertesting.2013.02.006

Cited by 26 publications

(12 citation statements)

References 25 publications

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“…By analyzing semicrystalline material (PP) [60,[70][71][72][73], PP filled with CaCO 3 [74], ceramic feedstock as Zr [75] (in combined effect with mold temperature) or W [62], authors found that packing pressure was the most influential factor (above different parameters considered).…”

Section: Processing Parameter Influencesmentioning

confidence: 99%

Review of Factors that Affect Shrinkage of Molded Part in Injection Molding

Annicchiarico¹,

Alcock²

2014

Materials and Manufacturing Processes

122

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Purpose: This paper reviews the factors affecting shrinkage of molded parts in injection molding. Methods: A selective screening of the papers published in the last 10 years was adopted. The review was organized according to molding scale (macro or micro) and by considering four branches of influence: material behaviors, processing parameters, mold, and specimen design. Results: Within the interval of confidence, at the macroscale, critical processing parameters were the temperatures, the packing parameters, cooling time, and injection speed; temperatures and packing parameters resulted critical factors at the microscale as well. Concerning the design aspects, the runner size and the ribs affect shrinkage at the macro and microscale, respectively. The analysis of the literature review has shown an absence of statistical approach for determining the material influences, a lack of information on shrinkage occur in powder-molded parts and the absence of data in specimen with dimensions below 10 m. Conclusions: The review collected the factors that affect shrinkage in injection molding, and identified three possible areas for further works.

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Section: Processing Parameter Influencesmentioning

confidence: 99%

Review of Factors that Affect Shrinkage of Molded Part in Injection Molding

Annicchiarico¹,

Alcock²

2014

Materials and Manufacturing Processes

122

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“…WorkDev = W 0 − W i = ∫ y 0 (t)dt − ∫ y i (t)dt (10) where: i = 1, 2, 3, …, N cycles. The compatibility of the "WorkDev" QI will be discussed in Section 3.3.1 and compared with the previously introduced QIs and the dynamic time warping (see next section).…”

Section: Work Deviationmentioning

confidence: 99%

“…A number of researchers in the field of sensor technology have studied different approaches to develop methods of process control, an optimization that could shorten the duration of metrological investigations for the approval of injection-molded components. Promising results are shown in studies where in-mold sensors are used for process regulation and monitoring, though the placement of sensors involves higher tooling costs [9][10][11][12][13]. Chen et al [14] have proved that part weight and thickness can be reliably monitored with the use of a linear variable differential transformer (LVDT) monitoring the mold separation (MS) distance.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips

Giannekas

Zhang

Tosello

2018

JMMP

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Injection molding has been increasing for decades its share in the production of polymer components, in comparison to other manufacturing processes, as it can assure a cost-efficient production while maintaining short cycle times. In any production line, the stability of the process and the quality of the produced components is ensured by frequently performed metrological controls, which require a significant amount of effort and resources. To avoid the expensive effect of an out of tolerance production, an alternative method to intensive metrology efforts to process stability and part quality monitoring is presented in this article. The proposed method is based on the extraction of process and product fingerprints from the process regulating signals and the replication quality of dedicated features positioned on the injection molded component, respectively. The features used for this purpose are placed on the runner of the moldings and are similar or equal to those actually in the part, in order to assess the quality of the produced plastic parts. For the purpose of studying the method’s viability, a study case based on the production of polymer microfluidic systems for bio-analytics medical applications was selected. A statistically designed experiment was utilized in order to assess the sensitivity of the polymer biochip’s micro features (μ-pillars) replication fidelity with respect to the experimental treatments. The main effects of the process parameters revealed that the effects of process variation were dependent on the position of the μ-pillars. Results showed that a number of process fingerprints follow the same trends as the replication fidelity of the on-part μ-pillars. Instead, only one of the two on-runner μ-pillar position measurands can effectively serve as product fingerprints. Thus, the method can be the foundation for the development of a fast part quality monitoring system with the potential to decrease the use of off-line, time-consuming detailed metrology for part and tool approval, provided that the fingerprints are specifically designed and selected.

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“…Huang et al [10] investigated the effects of high mold surface temperature generated by induction heating in enhancing the replication rate of microfeatures of LGPs. Kusić et al [11] investigated the influence of six injection molding process parameters on the postmolding shrinkage and warping of parts made from polypropylene filled with calcium carbonate, by carrying out experimental tests using the Taguchi method; the best combination of process parameters was found.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Enhancement for Viscoelastic Injection Rheomolding Using Hierarchy Orthogonal Optimization

Wang

Zhang

2018

Mathematical Problems in Engineering

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The viscoelastic injection molding involves multidisciplinary thermoplastic rheomolding parameters which is a complex mathematical problem. Particularly for rheomolding of complex parts with thin-walled structure, boss, and grooves, the increasing higher requirements on energy efficiency and rheomolding quality are put forward. Therefore, an energy-efficient enhancement method for viscoelastic injection molding using hierarchy orthogonal optimization (HOO) is proposed. Based on the thermoplastic rheomolding theory and considering the viscoelastic effects in injection molding, a set of partial differential equations (PDE) describing the physical coupling behavior of the mold-melt-injection molding machine is established. The fuzzy sliding mode control (FSMC) is used to reduce the energy consumption in the control system of the injection molding machine's clamping force. Then, the HOO model of viscoelastic injection rheomolding is built in terms of thermoplastic rheomolding parameters and injection machine parameters. In initial hierarchy, through Taguchi orthogonal experiment and Analysis of Variance (ANOVA), the amount of gate, melt temperature, mold temperature, and packing pressure are extracted as the significant influence parameters. In periodical hierarchy, the multiobjective optimization model takes the forming time, warping deformation, and energy consumption of injection molding as the multiple objectives. The NSGA-II (Nondominated Sorting Genetic Algorithm II) optimization is employed to obtain the optimal solution through the global Pareto front. In ultimate hierarchy, three candidate schemes are compared on multiple objectives to determine the final energy-efficient enhancement scheme. A typical temperature controller part is analyzed and the energy consumption of injection molding is reduced by 41.85%. Through the physical experiment of injection process, the proposed method is further verified.

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The impact of process parameters on test specimen deviations and their correlation with AE signals captured during the injection moulding cycle

Cited by 26 publications

References 25 publications

Review of Factors that Affect Shrinkage of Molded Part in Injection Molding

Review of Factors that Affect Shrinkage of Molded Part in Injection Molding

Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips

Energy-Efficient Enhancement for Viscoelastic Injection Rheomolding Using Hierarchy Orthogonal Optimization

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