On-line measurement of cavity pressure during injection molding via ultrasonic investigation of tie bar

Quality control is a crucial issue in the injection molding process with target of obtaining a high yield rate and reducing production cost. Consequently, effective methods for monitoring the injection conditions (e.g., pressure, velocity, and temperature) in real-time and adjusting these conditions adaptively as required to ensure a consistent part quality are essential. This study proposes a quality index based on the clamping force increment during the injection cycle, as determined by four strain gauges attached to the tie bars of the injection molding machine. Also, various quality indexes for online quality monitoring and prediction purposes based on the pressure, viscosity, and energy features extracted from the pressure profiles obtained at the load cell, nozzle, and molding cavity, respectively, are compared. The feasibility of the proposed quality indexes is investigated experimentally for various settings of the barrel temperature, back pressure, and rotational speed of the plasticizing screw. It is shown that all of the quality indexes are correlated with the injection-molded quality and hence provide a feasible basis for the realization of an on-line quality monitoring and control system. Particularly, the tie-bar elongation quality index requires no modification or invasion of the injection molding system or cavity and hence provides a particularly attractive solution for monitoring and controlling the part quality.

Section: Literature Reviewmentioning

confidence: 99%

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Chen

Tseng²,

Huang³

2019

“…Recently, we employed an ultrasonic method to measure the stress on the tie bars of an injection molding machine, therefore monitoring the service situation of the machine [53,54]. We also established a physical model to estimate the correlation between the cavity pressure and stress on the tie bars and as such achieved an indirect measurement of the cavity pressure via the stress on the tie bars [55].…”

Section: Ultrasound Ultrasound Waves Are Mechanical Wavesmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Self Cite

Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

“…Injection molding is regarded as the most important method for mass-producing plastic parts [1][2][3][4], and it is complicated because of steep thermal gradients and complex flow geometries; many studies have been conducted in this field [5][6][7][8][9][10]. e product weight of a plastic part is considered one of the most significant properties, especially for plastic parts with high precision requirements, such as plastic lenses.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Zhao

Dong

Zhang

et al. 2020

Self Cite

Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.