Injection moulding parameters influence on weight quality of complex parts by means of DOE application: Case study

Injection moulding is a very popular plastic transformation process to produce complex parts, since a mould is capable to reproduce different shapes, and also due to its economic advantages for large series. However, defects can appear into the pieces, leading to reject them in the quality control.In order to control these defects, it is necessary to know the influence of the injection parameters on the quality of the part. Several researchers applied Design of Experiments (DOE) methods to injection process, with good results for test or lab specimens, but many industrial parts usually have more complex geometries or singularities.This paper introduces a case study focused on the application of DOE in a complex part because it has an integrated hinge. In this case study, the weight and pressure curves (hydraulic and specific) are analysed depending on the variation of process parameters. The authors would show that the analysis has found a strong influence of the geometry on the results which sometimes are different to the expected ones.In authors’ opinion, the use of this procedure it still is a challenge, and this example could assist future attempts to create new expert systems in injection moulding.     

Test methodology for the formulation and selection of components of thread moulding oils

In the course of a Kplus research project which focused on the internal thread moulding process, a test methodology for evaluating the efficiency of forming oils was developed. The various tests should provide tribological characteristic values, such as coefficient of friction, with respect to the real production environment. To guarantee the transferability of results, the complex of loads acting on the components in the model tests must correspond to the real forming process. Especially parameters such as contact pressure, relative speed in the forming zone and the increase of surface of the workpiece during the forming process must be considered in tribological testing. The tribological data provided information to select components of thread moulding oils and to optimise the formulation. Best practice moulding oils must satisfy different demands in respect of physical, physical–chemical, chemical and additional properties. In addition to the cooling function and the reduction of friction, there is also a significant influence on the quality of the workpiece and the moulding process itself. Copyright © 2008 John Wiley & Sons, Ltd.     

Process optimisation of transfer moulding for electronic packages using artificial neural networks and multiobjective optimisation techniques

Transfer moulding is the most common process for the encapsulation of electronic packages in semiconductor manufacturing. Quality of the moulding is affected by a large number of mould design parameters and process parameters. Currently, the parameters setting is performed by experienced engineers in a trial and error manner and often the optimal setting can not be obtained. In the face of global competition, the current practice is inadequate. In this research, a process optimisation system for transfer moulding of electronic packages is described which involves design of experiments (DOE) techniques, artificial neural networks (ANNs), multiple regression analysis and the minimax method. The system is aimed to determine the optimal mould design parameters and process parameter settings of transfer moulding of electronic packages for multiobjective problem. Implementation of the optimisation system has demonstrated that the time for the determination of optimal mould design parameters and process parameters setting can be greatly reduced and the parameters setting recommended by the system can contribute to the good quality of moulded packages without relying on experienced engineers.     

Bootstrap method approach in designing multi-attribute control charts

In a production process, when the quality of a product depends on more than one correlated characteristic, multivariate quality control techniques are used. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In monitoring the quality of a product or process in multi-attribute environments in which the attributes are correlated, several issues arise. For example, a high number of false alarms (type I error) occur and the probability of not detecting defects (type II error) increases when the process is monitored by a set of independent uni-attribute control charts. In this paper, to overcome these problems, first we develop a new methodology to derive control limits on the attributes based on the bootstrap method in which we build simultaneous confidence intervals on the attributes. Then, based upon the in-control and out-of-control average run length criteria we investigate the performance of the proposed method and compare it with the ones from the Bonferroni and Sidak’s procedure using simulation. The results of the simulation study show that the proposed method performs better than the other two methods. At the end, we compare the bootstrap method with the T ² control chart for attributes.     

Injection moulding optimisation of multi-class design variables using a PSO algorithm

Injection moulding optimisation seeks to achieve the highest possible moulding quality under the specified constraints. To this end, the factors (design variables) affecting the moulding quality should be adjusted, including those of process parameters, mould design, part geometry, etc. Past work in this aspect is primarily focused on tuning the process parameters and mould design (e.g., gate location, runner and cooling channel layout), with less attention on the part geometry, and none on them all. To address this problem, this paper presents a PSO (particle swarm optimisation) algorithm for the optimisation of multi-class design variables, such as the part thickness, process parameters (melt temperature, mould temperature, injection time) and gate location. The optimisation is targeted at different aspects of moulding quality, including part warpage, weld lines, air traps, and so on. In applying the PSO algorithm, the paper proposes a modified elite archiving method, which can expedite the convergence speed, hence improving the efficiency of the algorithm. A computer program was developed that automates the steps such as adjusting the part thickness, the injection moulding process parameters and the gate location, activating the CAE software to simulate the injection moulding process, retrieving the simulation results, and evaluating the objective functions. The whole procedure iterates a number of generations by following the search process of the algorithm. A case study was also presented to illustrate as well as to test the proposed methodology, which was demonstrated as both effective and efficient.     

A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Moulding

Determination of the initial process parameters for injection moulding is highly skilled task and is based on a skilled operator's "know-how" and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed appraoch was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly from which good quality moulded parts can be produced without relying on experienced moulding personnel.     

Experimental investigation of laser transformation hardening of low alloy steel using response surface methodology

In this research, a systematic investigation on laser transformation hardening (LTH) process is carried out on high-strength low-alloy medium carbon steel, EN25 using design of experiments (DOE). The effect of input process parameters like laser power, travel speed over the response hardened width (HW), hardened depth (HD), and hardened area (HA) are analyzed. The experimental trials are conducted based on the design matrix obtained from the 3^k full factorial design (FFD) using a 2 kW continuous wave Nd:YAG laser power system. A quadratic regression model is developed to predict the responses using response surface methodology (RSM). Based on the developed mathematical models, the direct and interaction effects of the process parameters on LTH are investigated. The optimal hardening conditions are identified to maximize the HW and minimize the HD and HA. The results of the validation test show that the experimental values quite satisfactorily agree with the predicted values of the mathematical models and hence, the models can predict the response adequately.     

Friction materials development by using DOE/RSM and artificial neural network

A group of non-asbestos organic based friction materials containing 16 ingredients were investigated in this work using the techniques of design of experiment (2^k DOE), response surface methodology (RSM), and artificial neural network (ANN). The ingredients effects on three friction characteristics including 1st fading rate, 2nd fading rate, and speed sensitivity were studied by 2^k DOE. Five ingredients of phenolic resin, synthetic graphite, potassium titanate, mineral fiber, and calcium silicate were found to be statistically significant for these responses and should be studied further. In the meantime, an artificial neural network with Elman recurrent configuration was trained and tested using the data generated from dynamometer tests in 2^k DOE experiments. Concerning the confounding of two-ingredient interaction effects and main effects, response surface methodology was employed to optimize the friction material formulation. The well trained and tested Elman artificial neural network was then used to predict the friction characteristics of the trials generated by RSM. Based on the ANN prediction and RSM analysis, an optimization of material formulation was obtained and validated by experiments.     

Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology

This paper presents a systematic methodology to analyze the shrinkage and warpage in an injection-molded part with a thin shell feature during the injection molding process. The systematic experimental design based on the response surface methodology (RSM) is applied to identify the effects of machining parameters on the performance of shrinkage and warpage. The experiment plan adopts the centered central composite design (CCD). The quadratic model of RSM associated sequential approximation optimization (SAO) method is used to find the optimum value of machining parameters. One real case study in the injection molding process of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) cell phone shell has been performed to verify the proposed optimum procedure. The mold temperature (M _T), packing time (P _t), packing pressure (P _P) and cooling time (C _t) in the packing stage are considered as machining parameters. The results of analysis of variance (ANOVA) and conducting confirmation experiments demonstrate that the quadratic models of the shrinkage and warpage are fairly well fitted with the experimental values. The individual influences of all machining parameters on the shrinkage and warpage have been analyzed and predicted by the obtained mathematical models. For the manufacture of PC/ABS cell phone shell, the values of shrinkage and warpage present the reduction of 37.8 and 53.9%, respectively, using this optimal procedure.     

Process model of digital manufacturing

Simulation of the business process in manufacturing is considered. The main attributes and stages of the business process are described. For the example of automated machining, the business process is broken down into its components. A logical relation is established between the subprocesses (functions). Their inputs and outputs controlling the implementation of the process are described. A process model of digital manufacturing is developed in the methodology of the IDEF0 standard.     

A system for supporting rapid assembly modeling of mechanical products via components with typical assembly features

The objective of this study is to optimize the performance of discrete production systems by integration of computer simulation, design of experiment (DOE), and Tabu search (TS). Optimizing performance of a steelmaking workshop was considered as the case of this study, but it could be used to optimize the throughput of other production system. The simulation model is built by considering all major and detailed operations and interacting systems of the workshop. The results and the structure of the integrated simulation model are verified and validated by t test. To integrate simulation outputs with DOE, decision making parameters are defined as number of machines, operators, etc. (k factors). To estimate and assess the effects of each of the factors and their two-way interactions on response variable, a complete 3 ^k factorial design with lower and upper limits and a center point is considered. Furthermore, response surface methodology (RSM) is used to optimize the response variable. Because a first-order model may not be adequate for the RSM, a polynomial order regression equation is developed by least square method. By steepest ascent, the local optimum is identified. However, the global optimal solution is computed by Tabu search which uses a metaheuristic approach. Previous studies use integration of DOE and simulation to find optimum alternative. This is usually conducted by RSM and steepest ascent which locates local optimum solution. However, integration of DOE and TS locates global optimum solution.     

A combined FEA and design of experiments approach for the design and analysis of warm forming of aluminum sheet alloys

This paper presents a methodology to effectively determine the optimal process parameters for warm forming of lightweight materials using finite element analysis (FEA) and design of experiments (DOE). The accuracy and effectiveness of FEA are verified through comparisons of the achievable part depth values and forming limits predicted from FEA with those from experimental measurements in a wide range of warm-forming conditions. A DOE approach along with FEA is proposed to offer rapid and relatively accurate design of warm-forming process, especially for large parts that require 3D FEA. In addition, strain distributions on the formed part are obtained under a variety of process conditions to gain a better understanding of the warm-forming mechanisms and further investigate the effects of forming temperature, friction condition, forming speed, and blank holding pressure on forming performance. Results of this study reveal that much-improved formability can be efficiently gained with a well-controlled warm-forming parameters.     

Design and analysis of experiments in CMM measurement uncertainty study

In applying a coordinate measuring machine to measure a mechanical object, many factors affect the measurement uncertainty. Although a number of studies have been reported in evaluating measurement uncertainty, few have applied the factorial design of experiments (DOE) to examine the measurement uncertainty, as defined in the ISO Guide to the Expression of Uncertainty in Measurement (GUM). This research applies the DOE approach to investigate the impact of the factors and their interactions on the uncertainty while following the fundamental rules of the GUM. The measurement uncertainty of the location of a hole measured by a coordinate measuring machine is used to demonstrate our methodology.     

A computational system for process design of injection moulding: Combining blackboard-based expert system and case-based reasoning approach

The process design of injection moulding involves the selection of the injection moulding machine, mould design, production scheduling, cost estimation, and determination of injection moulding parameters. Expert system approaches have been attempted to derive the process solution for injection moulding in the past few years. However, this approach has been found to be incapable of determining the injection moulding parameters owing to the difficulty in setting the moulding parameters. In addition, the existing expert systems for process design lack the proper architecture for organising a heterogeneous knowledge source. In this paper, the combination of a blackboard-based expert system and case-based reasoning approach is introduced to make up the deficiencies of the existing expert-system approach to the process design, from which a computational system for process design of injection moulding, named CSPD, was developed and described. CSPD first derives the process solution including the selection of injection moulding machine and mould base, tooling cost, and processing cost estimation, and production scheduling based on the blackboard-based expert-system approach. It is then followed by the determination of the injection moulding parameters based on the case-based reasoning approach and the previously derived partial solution.     

基于实验设计和神经网络的冲压信号特征提取与诊断

制造过程中的各变量因素交互作用复杂,对其进行在线质量控制的前提是考虑交互影响,并获得足够的实时噪声因素信息来支持相应的控制策略。随着传感技术的发展,在计算能力和信号处理技术的强力支持下,可以实现对噪声因素的实时估计,进而对加工参数进行相应调整。基于此,本文提出了一种方法,在实验设计基础上,结合主成分分析(PCA)用于信号生成和预处理,并使用神经网络分类器对冲压信号进行特征提取与诊断,以获取更多的实时信息。论文还以一个实例证明方法有效且实用。     

<Emphasis Type="Bold">Optimisation of Plastic Injection Moulding Process with Soft Computing</Emphasis>

The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimise the process parameters. An approximate analysis model is developed using a Back-propagation neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimisation problem, a nonbinary genetic algorithm is applied to solve the optimisation model. The effectiveness of the improved strategy is shown by an example.     

Microscopic studies of the effect of laser irradiation parameters of photopolymer plate on the quality of flexographic printing plate produced by the digital method

The process of making of flexographic printing plate by the digital method proceeds in the following stages: back exposure, laser irradiation, main exposure, washing, post exposure, and finishing of the photopolymer plate. Laser irradiation is one of the basic technological stages, in which a negative of the reproduced image is created on the black layer of photopolymer plate. The article presents the results of studies of the effect laser irradiation parameters on the quality of flexographic printing plate produced by digital method. Stereoscopic optical microscope was used to determine the effect of laser power and rotation speed of the drum of digital imager in the laser irradiation of photopolymer plate on tonal value and precision of reproducing of halftone dots in various parts of multitonal halftone image reproduced on the printing plate. The results of the studies will enable to complement our knowledge on the effect of individual parameters of laser irradiation process on the quality of digital flexographic printing plates. Microsc. Res. Tech. 79:1007–1016, 2016. © 2016 Wiley Periodicals, Inc.     

Evaluating and controlling process variability in micro-injection moulding

Microsystem technologies require relatively strict quality requirements. This is because their functionalities are usually dependent on stringent requirements of dimensions, masses or tolerances. When mass-producing micro-components, e.g. replication of disposable microfluidic diagnostics devices, the consistency of the produced components could be significantly affected by process variability. The variability could be associated with a specific process parameter or could be a result of process noise. This paper presents a methodology to assess and minimise process variability in micro-injection moulding, an example of well-established mass-production techniques for micro-components. A design-of-experiments approach was implemented, where five process parameters were investigated for possible effects on the process variability of two components. The variability was represented by the standard deviation of the replicated part mass. It was found that melt temperature was a significant source of variability in part mass for one of the components, whilst the other was affected by unsystematic variability. Optimisations tools such as response surfaces and desirability functions were implemented to minimise mass variability by more than 40%.     

An integrated framework of statistical process control and design of experiments for optimizing wire electrochemical turning process

Design of experiments (DOE) and statistical process control (SPC) have been separately used in many traditional and non-traditional machining processes, but recently these two approaches are being combined and reevaluated for more effective use and accurate conclusions. DOE and SPC are very efficient tools to maintain the process on target and within boundaries of natural variations and to achieve the maximum accuracy and effectiveness of an experimental program. This paper proposes an integrated framework of SPC and DOE to execute the experimental procedures and to investigate a reliable mathematical model for optimizing the wire electrochemical turning process (WECT). WECT is a non-traditional machining process which has tremendous applications in modern industries especially in the aerospace and military industries. Response surface methodology is used to determine the sufficient number of experiments and also the recommended values of the input parameters. Univariate and multivariate control charts are used to assess the statistical control of the output parameters. Multi-objective optimization is conducted for determining the optimum values of the input parameters.     

A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach

Process design of injection moulding involves the selection of the injection moulding machine, mould design, production scheduling, cost estimation, and determination of injection moulding parameters. An expert system approach has been used to derive the process solution for injection moulding over the past few years. However, this approach is found to be incapable of determining the injection moulding parameters owing to the fragile nature of the knowledge for setting the moulding parameters. In addition, the existing expert systems for process design lack proper architecture for organising heterogeneous knowledge sources. In this paper, the combination of a blackboard-based expert system and a case-based reasoning approach is introduced to eliminate the deficiency of the existing expert-system approach to process design, from which a computational system for the process design of injection moulding, named CSPD, has been developed. CSPD first derives the process solution including the selection of the injection moulding machine and the mould base, tooling cost, processing cost estimation, and production scheduling based on the blackboard-based expert-system approach. It is then followed by the determination of the injection moulding parameters based on the case-based reasoning approach and the previously derived partial solution.