Optimization Of Injection Molding Process Research Articles

Comparative Study of Multi‐objective Bayesian Optimization and NSGA‐III based Approaches for Injection Molding Process

AbstractInjection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, a data‐driven injection molding process optimization framework is developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian optimization (MBO), the framework is applied to a fan blade model for verification. Incorporating those objectives enables the selection of an injection molding machine with the proper maximum clamping force within acceptable tolerance and production time. Furthermore, non‐moldable regimes are considered in design space, which allows less prior knowledge for setting design space. The optimized results are compared with those obtained using NSGA‐III, a well‐established genetic algorithm‐based optimization technique, as a benchmark. The optimization frameworks produce a Pareto front for the three‐dimensional outputs, revealing distinct trade‐off relationships between cycle time and warpage, as well as clamping force and warpage. The MBO framework demonstrates a superior Pareto front compared to NSGA‐III when utilizing a limited data set, underscoring its benefits in scenarios where costly simulations or experiments are necessary. These findings are anticipated to contribute to the optimization of manufacturing processes, ultimately enhancing productivity in real‐world industries.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

In-mold condition-centered and explainable artificial intelligence-based (IMC-XAI) process optimization for injection molding

This paper proposes a novel injection molding process optimization method based on the in-mold condition (IMC) and interpreted influence of in-mold condition features on part quality. In-mold condition is crucial for process optimization because it represents the actual process condition in the cavity where the polymer material is formed into the final part shape. Traditionally, the analysis of in-mold condition heavily depends on domain knowledge and insight, which introduces bias and inconsistency in in-mold condition-based process optimization. This study aims at developing an intelligent, objective, and robust process optimization method that concentrates on the highly influential in-mold condition features concerning part-quality, as interpreted by explainable artificial intelligence (XAI). In this in-mold condition-centered modeling approach, the input process parameters and final part quality were associated with the in-mold condition, with their corresponding relationship modeled by two machine learning (ML) models, respectively. The effect of in-mold condition on part quality was interpreted by applying XAI on the ML model that describes the relationship between in-mold condition and part quality. Features in the in-mold condition profiles that have high influence on part quality are given more weight in the search for diverse in-mold conditions that satisfy multiple part-quality objectives. A feasibility check has been implemented to identify, among those potential in-mold conditions, the optimal one that is physically feasible based on the ML model that governs the relationship between the process parameters and in-mold condition. The proposed method not only pinpoints better optimized process parameters than the conventional approach that omits the in-mold condition and only considers the direct relationship between the process parameters and part quality, but also reveals the possibility of further quality improvement. The optimization tool of the proposed method can be found on an online interactive platform https://imc-xai-injmold-optimization-4e26a3e3ef41.herokuapp.com/, which was created to facilitate further research based on the proposed approach. In addition to process optimization, this approach can effectively contribute to intelligent manufacturing management and Industry 4.0.

Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III.

This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95-98%. In summary, this approach not only enables the prediction of the lens's residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes.

Design of Polymeric Orthopedic Screws with Variable Stiffness and Multi-Objective Optimization of Injection Molding Process

Design of Polymeric Orthopedic Screws with Variable Stiffness and Multi-Objective Optimization of Injection Molding Process

Multi-Objective Optimization of Liquid Silica Array Lenses Based on Latin Hypercube Sampling and Constrained Generative Inverse Design Networks.

Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and "Constraint Generation Inverse Design Network (CGIDN)" to achieve multi-objective optimization of the injection process, shorten the time to find the optimal process parameters, and improve the production efficiency of plastic parts. Taking the LSR lens array of automotive LED lights as the research object, the residual stress and volume shrinkage were taken as the optimization objectives, and the filling time, melt temperature, maturation time, and maturation pressure were taken as the influencing factors to obtain the optimization target values, and the response surface models between the volume shrinkage rate and the influencing factors were established. Based on the "Constraint-Generated Inverse Design Network", the optimization was independently sought within the set parameters to obtain the optimal combination of process parameters to meet the injection molding quality of plastic parts. The results showed that the optimal residual stress value and volume shrinkage rate were 11.96 MPa and 4.88%, respectively, in the data set of 20 Latin test samples obtained based on Latin hypercube sampling, and the optimal residual stress value and volume shrinkage rate were 8.47 MPa and 2.83%, respectively, after optimization by the CGIDN method. The optimal process parameters obtained by CGIDN optimization were a melt temperature of 30 °C, filling time of 2.5 s, maturation pressure of 40 MPa, and maturation time of 15 s. The optimization results were obvious and showed the feasibility of the data-driven injection molding process optimization method based on the combination of Latin hypercube sampling and CGIDN.

Optimization of Injection Molding Process for Kenaf and Polypropylene Composite to Improve Its Tensile Strength

Abstract: Process parameters directly affect the performance and quality of the composite of polypropylene (PP) and kenaf in injection molding. Optimize the parameter of injection molding with the help of response surface methodology (RSM). The important parameters are injection temperature, pressure, and holding time to optimize using the 3-factor, 3-level Box Behnken method. MINITAB 20 is used for statistical analysis of tensile properties of polypropylene and kenaf composite. In this paper the confirmation test is conducted to verify the predicted value of tensile strength and the results show the optimum value for injection temperature is 191.970°C, a pressure value is 40 bar, and the holding time is 13.8384 sec. the optimum parameters are used to manufacture the composite for better tensile strength and actual value of the composite is in the range of the predicted value.

A Holistic Approach to Cooling System Selection and Injection Molding Process Optimization Based on Non-Dominated Sorting.

This study applied a holistic approach to the problem of controlling the temperature of critical areas of tools using conformal cooling. The entire injection molding process is evaluated at the tool design stage using four criteria, one from each stage of the process cycle, to produce a tool with effective cooling that enables short cycle times and ensures good product quality. Tool manufacturing time and cost, as well as tool life, are considered in the optimization by introducing a novel tool-efficiency index. The multi-objective optimization is based on numerical simulations. The simulation results show that conformal cooling effectively cools the critical area of the tool and provides the shortest cycle times and the lowest warpage, but this comes with a trade-off in the tool-efficiency index. By using the tool-efficiency index with non-dominated sorting, the number of relevant simulation cases could be reduced to six, which greatly simplifies the decision regarding the choice of cooling system and process parameters. Based on the study, a tool with conformal cooling channels was made, and a coolant inlet temperature of 20 °C and a flow rate of 5 L/min for conformal and 7.5-9.5 L/min for conventional cooling channels were selected for production. The simulation results were validated by experimental measurements.

Multi response optimization of injection moulding process to reduce sink marks and cycle time

In the rapidly growing field of technology, there is a demand for consumer goods made of plastic. To meet needs of customer, plastic products are made in different ways. There are different plastic manufacturing process. Numerous plastic manufacturing process are thereInjection molding is involves injecting molten material into a mold and allowing it to take on the shape of the mold.There are wide ranges of products such as plastic chairs, Computer cabinet etc. are manufactured with the help of smaller and more complex profiles. Due to lack of dimensional accuracy these complex profile produces defects inside the mold. Current study provides insights into transparent thermoplastic Polypropylene products. Five plastic injection molding variable criteria are considered for reducing the depth of sink mark in injection molding for commercial grade transparent thermoplastic (Polypropylene). Melting temperature (8C), mold temperature (8C), packing time (seconds), ratio of ribs to walls (%) and rib to gate distance (mm). According to Taguchi's design L27 experiments are performed and factors are optimized using the Taguchi based utility concept for multi criteria problems. The sink mark's depth on the part's surfaceis considered as quality of product and cycle time is considered as the productivity of product. It is the one of the best techniques, which can maintain the equilibrium between two.

Optimization of Injection Molding Process by using different types of Conformal Cooling Channels

This paper objects to deliver a brief summary on different types of Conformal cooling channel designs available for injection molding process, Also, the conformal cooling channel plays very important role in plastic injection process of injection molding by improving its efficiency and quality thus, reducing the whole cycle time of production. A comparative study of different conformal cooling channels like Series and parallel Conformal cooling channel, square section conformal cooling channel (SSCCC), Spiral cooling channel, Scaffold conformal cooling channel, array of baffle conformal cooling channel, micro cooling channel and their effects on injection molding based manufacturing process are shown. Study related to the performance analysis of different conformal cooling channels is done by using “Autodesk moldflow Advisor”. This paper presents a succinct review of recent progress in Conformal cooling channels.

Optimization of Injection Moulding Process via Design of Experiment (DOE) Method based on Rice Husk (RH) Reinforced Low Density Polyethylene (LDPE) Composite Properties

Optimization of Injection Moulding Process via Design of Experiment (DOE) Method based on Rice Husk (RH) Reinforced Low Density Polyethylene (LDPE) Composite Properties

Optimization of Injection Molding Process of Transparent Complex Multi-Cavity Parts Based on Kriging Model and Various Optimization Techniques

The injection molding process of a mold with multiple cavities can reduce the mold space, but the process is complicated and the product is prone to warpage. In order to reduce the transparent parts warpage of such a mold, by optimizing injection molding process parameters, an optimization technique combining orthogonal experiment, Kriging model and an optimization algorithm is proposed. First, the orthogonal experiment is designed, based on Computer Aided Engineering (CAE) simulation results, while the significant factors affecting the warpage are selected. On this basis, the Kriging model is established to map the nonlinear function relationship between the significant factors and the warpage. Specifically in this paper, four types of correlation functions, affecting the model precision accuracy, are used to establish the Kriging model and the high-precision accuracy model is selected. A numerical optimization algorithm, direct search approach and global exploration method are used to optimize the model, so as to obtain the optimal injection molding parameters producing the minimum warpage. Finally, CAE simulation and experimental validation prove the effectiveness and reliability of the proposed optimization method.

Overview of injection molding process optimization technology

Injection molding process parameters have a very important influence on the molding quality of plastic parts. In this paper, the application of injection molding parameter optimization technology in the study of injection molding process is reviewed. The basic principles and characteristics of various optimization techniques are described. Optimization techniques mainly include: Optimization based on Taguchi method, Optimization of numerical simulation based on CAE technology, Optimization based on genetic algorithm, Optimization based on artificial neural network. The application types of optimization problems applicable to various optimization techniques are discussed.

Minimization of Sink Mark Defects in Injection Molding Process Optimization of a Composite Drive Shaft Used in Automobiles

The motive for this paper is to show an included technique for improving the excellent attributes of the infusion fashioned component (Honda Upper Part) being fabricated at assembling industry (Pakistan), wherein the dismissal price for Honda higher element became entrance ramp due to sink marks abandons. The proposed coordinated method grasps the concept of Design of Experiments (Taguchi Approach) and Response floor structure philosophy for infusion forming procedure enhancement. The Taguchi Method (TM) was utilized to brief rundown the elements that effectsly affect the sink tests in infusion shaped components. Moreover, the development method of Response Surface Methodology (RSM) changed into used for the trial research to comfy a forecast model that may be applied to decorate infusion shaping process as some distance as firstrate sink marks decrease. The outcome suggests that the sink marks lower Anticipated by way of the coordination of the Taguchi Method and RSM in truth dwindled from zero.0088 (Taguchi's final results) all the way down to 0.0080 mm. The genuine effects find that the becoming a member of of the Taguchi Method and RSM should safely improve the great. Essentially with those examinations, we attempted to open the imaginative and prescient of manufacturers and architects with regard to using included RSM/Taguchi approach using instantly cum connection relapse version with selected parametric/ranges placing for the dimensional exactness of the infusion shaped part, and in this way contributing closer to progress in procedure's unwavering high-quality. The incorporated method with relapse model for enhancement would not simply supply hypothetical well worth to the writing and makers/fashioners but can likewise be applied to diverse assembling organisations for high-quality elements technology.

Optimization of injection moulding process in manufacturing the flip cover of water meter

The conventional optimization process in Injection Moulding includes actual shop floor trials in which melt temperature, mold temperature, injection time, injection pressure, pattern, feeder size, shape and location cores, mould layout, gating etc. are changed in each iteration which involves high machining cost, tooling cost, modification cost, melting cost, and transportation cost as well as, materials, energy, time are wasted in each trial until and unless the required results are obtained. Water meter components (Flip Cover) are designed in CREO 5.0, and then components are 3D printed to cross check the dimensions and also to confirm whether all the other components can be accommodated or not. Then the mold flow analysis will be performed on a water meter components using different materials and changing the process parameters. The input process parameters considered are melt temperature, mold temperature and injection time, whereas the responses are warpage, volumetric shrinkage, cycle time and quality prediction. Grey relational analysis is carried out to determine the optimum injection moulding process parameters. L9 Orthogonal Array (OA) table is chosen for the analysis. The effort has been made to minimize the warpage, volumetric shrinkage, cycle time and maximize the quality prediction Mold cavity and core for the components are designed in CREO 5.0 and manufactured using P20 tool steel. Then the water meter components are manufactured by inputting the optimal process parameters in injection moulding machine to achieve high productivity and quality.

Injection Molding Process Optimization of Polypropylene using Orthogonal Experiment Method Based on Tensile Strength

The process parameters have direct influence on the performance and quality of final products of polypropylene (PP) in injection molding. In order to optimize injection molding process of PP and obtain expected tensile strength, tensile tests designed according to orthogonal experiment method of 7 factors at 3 levels were carried out on the injection molding machine, and relevant data were analyzed by range analysis method. The results show that, in the scope of given experiment, the process parameters affecting tensile strength of PP from high to low are injection pressure, melt temperature, cooling time, packing time, packing pressure, mold temperature and injection rate, and the optimal values of injection molding process parameters are melt temperature 200°C, injection pressure 5 MPa, injection rate 200 mms−1, packing pressure 3 MPa, packing time 40 s, cooling time 40 s and mold temperature 40°C, which is further verified by additional test.

Optimization of Injection Moulding Process in Manufacturing the Top Cap of Water Meter

Optimization of Injection Moulding Process in Manufacturing the Top Cap of Water Meter

Multi-Objective Optimization of Injection Molding Process for Determination of Feasible Moldability Index

A multi-objective optimization of injection molding for determination of feasible moldability index was investigated in this study. The feasible moldability indices were calculated by implementing Taguchi method and Fuzzy Analytic Hierarchy Process with the order performance by its similarity to the ideal solution technique. Major process parameters of the injection molding like melt temperature, gate design, filling and cooling times were selected as variables to minimize common manufacturing defects like shrinkage, warpage and short shot. The analytic hierarchy process was used to set initial weights of variables and seriousness of each defect was evaluated by fuzzy decision-making process. Finite Element Analysis of the injection molding process was performed for selected work piece using polypropylene material. Results showed that it is more convenient to calculate several sets of variable parameters with high moldability indices, rather than focusing on single configuration with the highest moldability index. It is clear that the combination of Taguchi with TOPSIS and FAHP gives more applicable alternatives with high moldability indices, if the solution with highest moldability index in not practical.

AI Based Injection Molding Process for Consistent Product Quality

Abstract In manufacturing processes, Injection Molding is widely used for producing plastic components with large lot size. So, continuous improvements in product quality consistency is crucial to maintaining a competitive edge in the injection molding industry. Various optimization techniques like ANN, GA, Iterative method, and simulation based are being used for optimization of Injection Molding process and obtaining optimal processing conditions. But still due to variation during molding cycles, quality failure occurs. As many constituents like process, Material, machine together yields product quality. This paper is focused on Real time AI based control of process parameters in injection molding cycle. Process parameters and their interrelationship with quality failure has been studied and later supposed to be used to generate algorithm for compensating the deviation of process parameters. Pressure and temperature sensor assisted monitoring system is used to collect data in real time and based on its comparison with the standard values an interrelationship is formed between parameters and plastic material properties. Algorithm generates new process parameter values to compensate the deviation and machine control follows the same. The entire process is supposed to be smart and automatic after being trained with AI and machine learning techniques. Simulation using Moldflow software and real industry collected data has been used for understanding whole molding process establishing relationship between failure and parameters. An automotive product in real industry is chosen for data acquisition, implementation and validation of entire AI based system.

Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO)

Injection molding of bi-aspheric lens using polycarbonate material with minimum variation in volumetric shrinkage is crucial for optical quality and is more challenging task among the researchers. In this paper, a hybrid artificial neural networks (ANN) and particle swarm optimization (PSO) technique is used to predict the optimal process parameters of injection molding process of the bi-aspheric lens. The developed ANN network (7-13-6) was trained as well as tested with experimental data sampled from statistical methods. The well trained and tested ANN network was coupled with improved PSO algorithm as a hybrid ANN-PSO to optimize the injection molding process parameters. The optimized injection molding process parameters obtained from hybrid ANN-PSO algorithm are validated with experiments using J. S. W injection molding machine. It is observed from the lens quality parameters that the proposed hybrid ANN-PSO method optimized the injection molding process of the bi-aspheric lens with an optical power of 27.73 Diopter and the lens posses seventh order spherical aberrations.