Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations

This paper describes the development of a fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) system for injection molding processes. The goal is to employ a fuzzy neural network to predict flash in injection molding operations when using recycled mixed plastics. Major processing parameters, such as injection speed, melt temperature, and holding pressure, are varied within a small range. The vibration signal data during the mold closing and injection filling stages was collected in real-time using an accelerometer sensor. The data was analyzed with neural networks and fuzzy reasoning algorithms, in conjunction with a multiple-regression model, to obtain flash prediction threshold values under different parameter settings. The FNN-IPMFP system was shown to predict flash with 96.1% accuracy during the injection molding process.     

Development of an in-process Pokayoke system utilizing accelerometer and logistic regression modeling for monitoring injection molding flash

Today, using recycled materials is a common practice in plastic industries for the sake of saving material cost and pursuing sustainable manufacturing. The recycled materials may have some properties (for example, fluidity and viscosity) significantly different from the primary plastic resin, which may lead to quality problems. An in-process Pokayoke system was developed in this research to monitor injection molding parts’ flash caused by adding a foreign polymer in the lab test, which was used to simulate the recycled plastic. The proposed system employed an accelerometer to capture the injection molding vibration signals. The featured injection molding vibration signals were identified through data analyses, and they were then used as input variables through logistic modeling to predict flash in an injection molding process that utilizes pure polystyrene (PS) mixed with a small portion of low-density polyethylene (LDPE). The testing results indicated that this Pokayoke system could monitor the injection molding flash status caused by the mixed material with approximately 95 % accuracy while the injection molding is in process. This Pokayoke system can help the injection molding machine take immediate actions to avoid wastes caused by flash.     

Development of an in-process, gap-caused flash monitoring system in injection molding processes

This paper describes the development of an in-process, gap-caused flash monitoring (IGFM) system for injection-molding machines. An accelerometer sensor was integrated in the proposed IGFM system to detect the difference of the vibration signals between flash and non-flash products in the last period of the injection molding filling stage. Through an approach suggested by the statistical process control mechanism, the threshold in the decision-making mechanism of this system was established. That threshold then was used by the IGFM system to determine if flash occurred in the molded products when the machine was running. An experiment was designed and performed by manipulating the variables of gap and no-gap. The experimental testing results indicated that this system could successfully monitor injection-molded products’ flash status with approximately 94.7% accuracy while the machine was in process .     

Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding industry. Selecting the proper process conditions for the injection molding process is treated as a multi-objective optimization problem, where different objectives, such as minimizing product weight, volumetric shrinkage, or flash present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an experiment-based optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process. The development integrates Taguchi’s parameter design method, neural networks based on PSO (PSONN model), multi-objective particle swarm optimization algorithm, engineering optimization concepts, and automatically search for the Pareto-optimal solutions for different objectives. According to the illustrative applications, the research results indicate that the proposed approach can effectively help engineers identify optimal process conditions and achieve competitive advantages of product quality and costs.     

A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data

Nondestructive online monitoring of injection molding processes is of great importance. However, almost all prior research has focused on monitoring polymers in molds and damaging the molds. Injection molding machines are the most important type of equipment for producing polymeric products, and abundant information about actual polymer processing conditions can be obtained from data collected from operating machines. In this paper, we propose a nondestructive online method for monitoring injection molding processes by collecting and analyzing signals from injection molding machines. Electrical sensors installed in the injection molding machine, not in the mold, are used to collect physical signals. A multimedia timer technique and a multithread method are adopted for real-time large-capacity data collection. An algorithm automatically identifies the different stages of the molding process for signal analysis. Moreover, ultrasonic monitoring technology is integrated to measure the cavity pressures. Experimental results show that our nondestructive method can continuously monitor the injection molding process in real time and automatically identify the different stages of the molding process. The packing parameters, including the filling-to-packing switchover point and the packing time, can be optimized based on these data. Furthermore, the ultrasonic reflection coefficient and the actual cavity pressure have similar trends, and our technique for measuring the cavity pressure is accurate and effective.     

An integrated parameter optimization system for MISO plastic injection molding

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L₂₅ orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.     

PIC18F66J60以太网模块的注塑机远程监控系统设计

为实现注塑机生产远程数据采集、远程监控等功能,开发了基于PIC18F66J60以太网模块注塑机生产远程监控系统。将分散注塑机串口数据采集与监控设备连接,并通过以太网发送到生产调度中心,进而实现对注塑机生产过程进行远程监控。     

In-situ density measurement for plastic injection molding via ultrasonic technology

Density variation during the injection molding process directly reflects the state of plastic melt and contains valuable information for process monitoring and optimization. Therefore, in-situ density measurement is of great interest and has significant application value. The existing methods, such as pressure−volume−temperature (PVT) method, have the shortages of time-delay and high cost of sensors. This study is the first to propose an in-situ density measurement method using ultrasonic technology. The analyses of the time-domain and frequency-domain signals are combined in the proposed method. The ultrasonic velocity is obtained from the time-domain signals, and the acoustic impedance is computed through a full-spectral analysis of the frequency-domain signals. Experiments with different process conditions are conducted, including different melt temperature, injection speed, material, and mold structure. Results show that the proposed method has good agreement with the PVT method. The proposed method has the advantages of in-situ measurement, non-destructive, high accuracy, low cost, and is of great application value for the injection molding industry.     

运用统计小波的光纤光栅结构健康监测技术

针对振动型结构健康监测方法的特点,搭建了基于非平衡M-Z干涉仪和相位载波解调技术的光纤光栅损伤识别系统。运用小波包分解振动信号,建立了基于小波包节点能量相对变化率之和的结构损伤识别指标;介绍了统计过程控制原理,推导了使用均值-极差控制图分析损伤识别指标,识别结构连续损伤的过程。实验测试了铝制简支梁结构处于健康状态和3种损伤状态下的各40次振动信号。信号时域图显示各状态振动信号持续时间均约为0.05ms,幅值基本相同。依据结构健康状态下的统计过程控制限(12.85,41.35)进行了均值-极差控制图损伤识别分析,结果表明,搭建的损伤识别系统能连续地对结构进行健康监测。     

Use of autocorrelation of wavelet coefficients for fault diagnosis

This paper presents a novel time–frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.     

基于振动法的电力变压器在线监测系统

电力系统安全运行是关系国计民生的大事,而电力变压器故障是造成电力安全的重大隐患,因此,研究电力变压器监测系统具有重要意义。基于振动法的变压器在线监测系统,通过测量正在运转的变压器油箱表面的振动信号,采用Hilbert-Huang变换对电力变压器振动信号进行理论分析,获得变压器油箱表面振动信号具有的本质特性,以发现变压器的故障及其损伤程度。该方法能够有效反应变压器运行过程中铁心和绕组的状态,因而具有一定的应用前景。     

PLC在注塑机控制系统中的应用

SZ-250A型注塑机是一种中小型塑料注射成型机,它将颗粒状的塑料加热熔化到流动状,用注射装置快速、高压注入模腔,保压一定时间,冷却后成型为塑料制品。根据其工艺流程及工作控制要求,设计了以PLC为控制核心的控制系统,给出了此控制系统软硬件设计的全过程。通过实践使用证明,该控制系统具有快速、高效、高可靠性、抗干扰能力强等特点,实现了注塑机注塑全过程的自动控制。     

基于单片机的注塑机温控系统设计

塑料注塑成型机的诸多输入、输出参数中,料筒温度是工作过程中的重要被控参数。论文针对传统注塑机温控系统存在的超调量大、调节时间长等诸多不足,提出了以单片机为控制核心,应用模糊控制理论,设计出模糊控制器,实现注塑机温度的实时控制,对于提高注塑机温度控制精度具有较好的意义。     

Quality control of the injection molding process using an EWMA predictor and minimum–variance controller

This study presents an exponentially weighted moving average predictor and minimum–variance controller for the quality control of plastic injection molding processes. This is a slow drifting problem of quality control during plastic injection molding processes. In order to have good product quality for plastic injection molding process, a proposed approach was applied to achieve the desired process control quality during the control process. To simplify the process model and reduce system loads, design of experiments technique was adopted to analyze the important factors that had significant effects on the product quality and their relative correlations. The results of this research showed that the proposed approach was effective for a quality control of plastic injection molding process. This cannot only steadily control the manufacturing process to reduce product loss and maintenance time due to unforeseen malfunctions, but they can also increase the efficiency of the equipment and the process.     

基于开放式数控系统的车刀磨损监控技术研究

研究了采用工业PC机、电机控制卡和数据采集卡构成的开放式数控系统的体系结构和功能。在加工过程中，数据采集单元实时采集刀具的振动信号，经过数字滤波，运用多元回归建模技术，建立了车削过程刀具磨损监测模型。该方法也可以方便地建立适合不同切削过程要求的监控模块。实验表明，在开放式数控系统平台上，可实现车刀磨损程度监控技术，保证了加工过程的稳定性。     

[成形过程的数据挖掘与深度学习方法]塑料注射成形智能技术及其应用

总结了当前塑料注射成形的产业需求和技术瓶颈,阐明了未来的发展趋势。根据塑料注射成形特性,提出“注射成形智能制造体系”的科学框架,建立以传感技术、工业以太网及互联网为基础的智能注射成形解决方案。围绕智能设计、智能优化、智能监控及制造数据平台四个层面,总结了注射成形中知识的组织与重用、自主决策与优化、过程感知与检测及云服务等技术,为实现塑料注射成形与新一代人工智能技术的深度融合指出了重要发展方向。     

Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems

A suitable setting of threshold levels has been a dilemma for engineers in a number of science and industrial fields. It is a common problem in monitoring, where deviation from a correct state needs to be detected. As the number of monitored values in modern systems reaches thousands, threshold calculations became a significant yet frequently underestimated concern. Due to a prohibitive cost of manual threshold setting, many systems generate thousands of false alarms consequent upon running on default threshold levels. In the paper, the authors illustrate a methodology for automatic threshold calculations in a large monitoring system. The paper is mainly addressed to engineers and machine monitoring systems developers, therefore selected statistical topics were treated briefly with main focus on practical solutions. Two fundamental data types are considered; namely, vibration signal measures, which can be extended to any nonnegative data, and symmetrical process values. As it is shown, these data types have significantly different probability distributions. Since real data seldom fits the Gaussian model, an investigation of several distributions and their comparison is presented. The proposed approach is validated on four datasets including process values from a gas compressor and vibration signal measures from a wind turbine.     

基于协同式专家系统的SPC诊断系统研制

从专家系统的功能立体结构分析入手，分析了SPC专家诊断系统的建立。通过对相关数据的模糊量化分析和过程分析，建立了DBKAS和SPC数据分析诊断子系统，以及相关参数数据管理子系统的SPC专家诊断系统，并将之应用到生产实践。     

Intelligent control of gas-assisted injection molding control systems

This study described the design and construction of gas-assisted injection molding systems incorporating a traditional injection molding machine. This combined system is called a gas-assisted injection molding control system (GAIMCS). The mathematical model of GAIMCS with nonlinear dynamics is difficult to establish accurately. Therefore, model-free intelligent control strategies were developed to control this system and evaluate its control performances. This work presents two intelligent control strategies: (1) traditional fuzzy controller (TFC), and (2) grey prediction fuzzy controller (GPFC). The GAIMCS was controlled by the GPFC, which was compared to a TFC to evaluate the system control performance. The GPFC achieves better control performances in accelerating rise time, and it reduces the system steady-state error better than the TFC for high-pressure gas control in GAIMCS, based on the verified experimental results.     

A combined analytical hierarchical process (AHP) and Taguchi experimental design (TED) for plastic injection molding process settings

Injection molding process is without doubt a multi-objective process if processing time, productivity, effectiveness, and the multi-criteria quality of the product are taken into consideration. Process settings affect the degree by which these objectives are realized. This work suggests a new proposal for evaluating optimal process settings through the handling of the plastic injection molding process in the same approach as a traditional multi-objective multi-criteria process. In a sense, there are numerous objective functions including cooling time, volumetric shrinkage, warpage, sink marks, residual stresses, and various process settings including temperature, pressure, etc. Within the suggested proposal, the Taguchi experimental design is used to generate a balanced set of experiments to explore the process; then, the finite element software SIMPOE is used to evaluate the behavior of the injection molding at each experimental setting. Analytical hierarchical process is then employed for multiple comparisons of the objectives and experiments as such to give the overall objective weight for each process setting (experiment). Analysis of variance is then used to evaluate the significant factors and the optimal setting of the process. This technique proved effective to obtain compliance between process design and several common manufacturer preferences, although the considered part was not changed.