Int J Plast Technol DOI 10.1007/s12588-015-9115-2 R E V I E W A RT I C L E

Process parameter optimization of plastic injection molding: a review Satadru Kashyap 1 & Dilip Datta 1

Received: 7 August 2014 / Accepted: 22 September 2015 # Central Institute of Plastics Engineering & Technology 2015

Abstract Over the years, injection molding has been a premier manufacturing technique in the production of intricate polymer components. Its molding efficiency rests on the shoulders of multiple process and machine parameters, which dictate the final product quality in terms of multiple output responses. It is imperative to state that a precise optimization of various input parameters is paramount for achieving the desired quality indices. In this article, a review of different techniques employed till date for optimizing various injection molding parameters is presented along with their advantages and limitations. It is found in the review that a complete intelligent technique operable without human interference is yet to be developed. Keywords Injection molding . Process parameter . Optimization

Introduction With the advent of high grade production technology; apart from miniaturization of components; intricacy, lightness and precision to design have become important factors today for commercial viability of plastic products. Due to its versatility in terms of mold and process design, injection molding has been paramount for decades in massproduction of intricate plastic products, which produces components in their near net shape with excellent dimensional tolerance. Consequently, in order to obtain a product with optimum quality, harnessing machine and process parameters of injection molding has been a premier area of research over the years [39].

* Dilip Datta [email protected]; [email protected] Satadru Kashyap [email protected] 1

Department of Mechanical Engineering, Tezpur University, Napaam, Tezpur 784 028, India

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The injection molding process incurs considerable changes in rheological and thermo-mechanical properties of polymeric materials and their composites due to highly varying stresses at various points of the process, materials processing at melt temperatures, and high cooling rates of the final product. It is imperative to state that properties of injection molded materials play a crucial role in obtaining the net shape of a final product. A material endures large amounts of shear stresses during injection molding, and rheological properties can be quantified to understand the stability of feedstock during molding. Rheological properties consisting of variation in viscosity with shear rates were studied by many researchers [34, 41, 53, 86, 87, 105]. Feng et al. [34] showed viscosity as a function of shear rate at different temperatures for high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)-HDPE blends (Fig. 1), where change in the melt viscosity was attributed to entanglement/disentanglement of molecular chains under high external forces of injection molding. In nonlinearly behaved (non-Newtonian behaviour) injected materials a small variation in shear rate results in large changes in melt viscosity, which is undesirable in setting up process parameters. Moreover, the thermal properties of extruded feedstocks, which can be obtained from DSC (Differential Scanning Calorimetry) plots, provide basic guidelines for the subsequent molding steps by showing a clear picture of various phases and their corresponding melting points [34, 41, 86, 87, 105]. As obtained by Feng et al. [34], Fig. 2 shows DSC curves for UHMWPE-HDPE polymer blends at different melt temperatures of injection molding, denoting the change in the crystal structure of the blends. Additionally, the PVT (Pressure-Volume-Temperature) behaviour of injection molded products should also be consulted beforehand as it provides specific volume of melt in the mold cavity as a function of cavity pressure and temperature, which helps in understanding the compressibility and the effects of temperature of the melt during molding operation. In order to avoid part relaxation problem, the holding pressure is to be selected from the PVT diagram in such a way that the cavity pressure comes near to the atmospheric pressure on mold opening. PVT diagrams for

Fig. 1 Plots of steady viscosity versus shear rate at different temperatures (reprinted from Feng et al. [34] with permission from Springer Science and Business Media)

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Fig. 2 DSC melting curves for UHMWPE/HDPE blend samples at three melt temperatures of core melts (190, 210 and 230 °C) (reprinted from Feng et al. [34] with permission from Springer Science and Business Media)

different materials are available in literature [79, 86–88, 93], as an example Fig. 3 shows the PVT diagram of polystyrene for specific volume versus temperature at different pressures. In view of above, various factors affecting the injection molding have to be analyzed thoroughly before deciding the feasibility of producing a product with the desired quality and intricacy. Such factors can be classified into three categories as follows [12]: 1. Machine parameters (independent variables): Barrel temperature, nozzle temperature, coolant temperature, packing pressure, holding pressure, back pressure, injection pressure, sequence and motion, switch-over point, injection speed, screw speed, shot volume, cushion, etc.

Fig. 3 PVT diagram of polystyrene: specific volume versus temperature at different pressures (reprinted from Quach and Simha [93] with permission from AIP Publishing LLC, ©1971)

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2. Process parameters (dependent variables): Mold temperature, melt temperature, cooling temperature, melt pressure, melt-front advancement, shear stress, injection time, filling time, packing time, holding time, cooling time, mold open time, injection rate, material flow rate, rate of heat dissipation and cooling, pressure switch-over, etc. 3. Quality indices (final responses): Part dimension, shrinkage, warpage, sink marks, appearance and strength at weld lines, and other aesthetic defects such as burn marks, gate blushes, surface texture, etc. The machine parameters can be controlled either by using effective sensors in the machine or by upgrading various machine components. The process parameters are dependent on process conditions, material properties, and mold design. On the other hand, the quality indices are the target variables, which are to be precision controlled for obtaining the desired net shape and intricacy. However, the extent of effect of each of the machine and process parameters on the final responses may not be the same or inter-related. Further, the machine and process parameters can form multiple sets of input, resulting in different output responses. However, a huge hindrance occurs in identifying the significant input parameters along with their inter-dependency on each other and their relationships with output responses. In earlier days, a machine was set up on trial and error basis by testing different sets of process and machine parameters based on the intuition and experience of machine operators. Such a manual process often led to wastage of resources as the effects of various underlying parameters could not be predicted beforehand. Moreover, drift in the machine settings over a period of time also resulted in upsetting of product quality. Nowadays, advanced technologies have given machines with better varieties and better control over parameters. However, it is still a daunting and expensive task to experimentally examine the output responses corresponding to various combinations of input parameters, or to quantitatively determine the inter-dependency between input parameters and output responses. Hence, there has been requirement of techniques which can design experiments with minimum number of trials, identify significant variables affecting the process, precisely predict final responses, and include material and machine constraints in addition to process constraints. With the progression of computation, many sophisticated studies are being conducted in these directions. This paper reviews the past and present research conducted till date towards using various optimization techniques for precise prediction of machine and process parameters so as to achieve the net shape of an injection molded product with desired quality at the minimum cost (resource and time). The article is organized as follows: the formulation of the injection molding optimization, followed by research conducted so far using different optimization techniques and subsequently, the article is concluded with some future research directions.

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Formulation of the injection molding optimization The injection molding optimization problem can be formulated as in Eq. (1).
T Determine x ≡ x j ; j ¼ 1; 2; …; m and
T to optimize Qðx; yÞ ≡ fq ðx; yÞ; q ¼ 1; 2; …; n subject to hi ðx; yÞ ≤ 0 ; ðlÞ ðuÞ x j ≤ x j≤ x j ; ðlÞ

ðuÞ

yk ≤ yk ≤ yk ;

9 y ≡ ðyk ; k ¼ 1; 2; …; pÞT > > > > > = i ¼ 1; 2; …; c ð1Þ > > > j ¼ 1; 2; …; m > > ; k ¼ 1; 2; …; p

where x and y are design vectors representing machine and process conditions with (u) (l) (u) (x(l) j ,xj ) and (yk ,yk ) as the ranges of parameters xj and yk, respectively; Q is the desired overall quality to be optimized (maximized or minimized), subject to constraints hi representing limitations on cost, loss, efficiency, etc.; and fq are quality indices, such as shrinkage, warpage, defects, sink marks, etc. The number and type of parameters, quality indices and constraints in Eq. (1) may vary in different investigations. But the essence of most of the works conducted till date was to optimize the overall quality of injection molded components by taking into consideration all the machine and process parameters and constraints affecting final responses. Dang [28] broadly classified the optimization techniques, implemented to plastic injection molding, into direct discrete methods (no mathematical function defining the relation between input and output) and meta-model based methods (approximate models built before applying an optimization process). Many works have been conducted using mathematical, computational and statistical techniques towards development of the injection molding process in terms of better predictability of processing parameters and establishing the inter-dependency of multiple input parameters on multiple output responses. These techniques can broadly be classified as past approaches, Taguchi method, modern computational methods, and real time techniques. Different works conducted so far under these four categories are discussed under their respective headings.

Past approaches During the incipient stage of injection molding, trial and error methods, based on experience and instincts of molding operators, were used to obtain products with lesser defects. Many miscellaneous approaches were proposed in the 1980s and 1990s towards predicting either the ranges of input parameters or the process on case to case basis [80]. These approaches can be classified as process window approach, case based reasoning approach and expert system based approach. In the process window approach, the relationship between injection pressure and melt temperature is used to create a process window, within which the molding process is carried out. Boundaries of the window are fixed based on two facts: (a) a too high

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injection pressure creates flash, while a too low one creates short shot, and (b) a too high melt temperature creates material degradation, while a too low one requires high injection forces. Using this approach, various process parameters were optimized [7, 32, 80, 82, 83, 90, 91]. However, the approach suffers from the inability for tackling multiple inputs and multiple outputs (MIMO). The case based reasoning approach matches a new problem with one or more previous cases from a library and the most comparable ones are used to provide a solution to the case at hand. The approach was implemented for cost estimation of injection molds and mold parts [32, 114], for determination of molding parameters [56, 57], and for setting of initial parameters [99]. Although it was a quick solution approach, but its effectiveness is dependent upon the size and relevance of the case library. Moreover, the use of this approach restricts the development of any new concept or method. The expert system based approach is a subset of artificial intelligence technique, and it has two components: (a) a knowledge based component containing facts and heuristics about the topic and their inter-relationships, and (b) an inference engine for simulating the end results. Expert systems based approaches were developed to provide either qualitative decisions on parameter changes [71]; or quantitative decisions on parameter settings, viz. significant parameters that might develop defects in products [43], likely solutions based on certainty factors [8, 45] and fuzzy logic [112]. However, in order to provide accurate solutions to different cases, a huge pool of knowledge from various domains (molding engineer, machine manufacturer, heat transfer, rheology, material selection and others) is required for developing expert systems and their regular upgradation based on technology advancement.

Taguchi method for design of experiments The Taguchi method is used for design of experiments (DOE) with the motto to obtain desired results by establishing the best combinations of design parameters through reduced time, cost and number of experiments [106, 107]. It employs some orthogonal arrays taking into account the number of parameters and their levels as well as the number of trials to be conducted, and subsequently determines the best combinations of parameters based on the signal to noise ratio. As an example, Taguchi L27313 implies a system comprising 13 parameters each with 3 levels, which requires only 27 trial experiments, instead of 313, for obtaining the desired result. In the injection molding of polyethylene and styrene, Chang and Faison [10] used the Taguchi method for finding the best combination of input parameters so as to minimize the shrinkage in the components, and subsequently applied Analysis of Variance (ANOVA) for finding the relative contribution of each input parameter and the most significant parameters affecting the shrinkage. The application of similar procedures in injection molding are found in some other works, e.g., minimization of friction coefficient and strain [118], minimization of warpage and shrinkage [84], and minimization of shrinkage in polypropylene and polystyrene components [4]. Various forms of the Taguchi method were applied in many other works concerning with injection molding optimization, e.g., determination of the optimum parameters based on visual quality and shrinkage [111], evaluation of the process parameters by avoiding silver streak defects in injection molded automobile plastic bumpers [13], evaluation of

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the influence of process parameters on the dimensional variation of injection molded ceramic [109], evaluation of process parameters by minimizing warpage in thin-shell plastic products [39], identification of parameters affecting shrinkage and warpage in thin-wall cellular phone covers [66], study of the effects of design parameters on shrinkage [15], determination of the optimal values of process parameters by minimizing warpage of a rectangular plate [108], study of the effect of input parameters on optimizing mechanical properties of recycled plastic parts [76–78], etc. In the recent application of the Taguchi method, Kang [46] optimized input parameters by minimizing shrinkage and warpage, Wang et al. [116] optimized input parameters in order to improve the compressive property of a brake booster valve body, and Karasu et al. [47] reduced the number of trial runs needed before mass production. Although the Taguchi method has been widely accepted in manufacturing including injection molding, certain hindrances are still associated with it. Firstly, the optimum level of a process parameter is to be determined from a prefixed set of levels. However, since the range of a parameter is continuous, the estimated optimum level of the parameter may not match with any of the chosen levels. Secondly, the selection of process parameters and their ranges and levels requires thorough understanding of the whole process, and as such meticulous experimental planning and interpretation are needed. Lastly, although the method reduces the number of experiments, still it requires certain experiments to be performed beforehand as per the chosen orthogonal array, thus causing wastage of resources and human errors.

Modern computational methods With the ever increasing intricacy of product design and hindrances developed in injection molding, a gradual paradigm shift towards modern sophisticated computational techniques has occurred in the last few years. It was essential to confront with the challenges posed by MIMO parameters, requirement of huge experimental analysis, and limited knowledge about the intra- and inter-relationships between input and output parameters. The modern computational techniques consist of hard computing and soft computing. The hard computing usually involves finite element or finite difference based simulation for obtaining acceptable input parameters, while the soft computing predicts the performance of injection molding by modeling the process environment through artificial intelligence techniques. Hard computing techniques Hard computing in injection molding is performed using either commercial software packages or optimization modules. Some commercial software packages were developed years ago by applying finite difference or finite element methods to the filling and post filling stages of injection molding [27, 38, 113]. Nowadays, many sophisticated software packages are available, which can simulate, analyze, and generate injection molding data based on selected input and output parameters. Numerous works using such software packages have been reported, e.g., Moldflow® in the fabrication of window frames using rice husk filled polyethylene composites [94], Moldflow® combined with Autodesk Inventorr for

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modeling and simulating shallow and flat thin walled molds of a polymer composite [5, 6], Moldflow® to determine significant parameters affecting the shrinkage of molded components [69], Moldflow® to study material distribution and melt flow behaviour in sandwich molding process [92], Moldflow® combined with the Taguchi method to determine the optimal design parameters by minimizing the warpage of gas assisted molding components [14], and Moldflow® integrated with the Taguchi method to study the effects of processing parameters on the molding of ultra-thin wall polymer components [102]. Apart from that, Moldflow® was applied to some other similar works also, e.g., simulation of feedstock properties for powder injection molding of thermal management devices [86], feedstock properties and injection molding simulations of bimodal mixtures [49], measurements of powder-polymer mixture properties and their use in powder injection molding simulations [48], powder injection molding of ceramic engine components for transportation [61], effects of nanoparticle addition on processing of alloys [85], powder injection molding of parts for UAV engine components [74], etc. In the case of optimization modules, significant works include Kriging models to minimize the warpage in plastic cellular phones with reduced computational resources than that required by Moldflow® [35–37], translational propagation algorithm for minimizing the warpage in terms of process parameters in the molding of an automotive car handle [68], goal programming to optimize the performance of a plastic injection molding by formulating the product defect in terms of imprecise fuzzy values [3], sequential simplex method combined with Moldflow® to minimize the warpage and shrinkage of an automotive ventiduct grid in terms of process parameters [33], probability-based concept integrated with a Gaussian process surrogate model for improving the accuracy and robustness of injection molding with reduced computation [117], heat expansion method integrated with a 3D registration and the NewtonRaphson method for optimizing the size of shrinkmolds used in show manufacturing [44], etc. The hard computing of injection molding process involves finite element analysis requiring a huge computational time, which may not be available in a real-time shop floor. Moreover, the commercial software packages may fail to consider many machinespecific constraints, such as specifications and capabilities. In addition, the adjustment of multiple input values in the simulation and interpretation processes of these techniques requires expert knowledge of the injection molding process. Soft computing techniques The widely applied artificial intelligence in soft computing of injection molding includes artificial neural network, evolutionary algorithms and hybridized techniques. Artificial neural network (ANN) The primary advantage of applying ANN for optimization of injection molding process is its capability for mapping out nonlinear relationships between a devious set of input and output data through a learning procedure, as well as its ability to interpret MIMO parameters. A huge number of works applying ANN to injection molding has been reported in the specialized literature, which include the prediction of injection pressure

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and injection time by training and testing ANN using process dataset generated by the C-Mold® software [120], injection pressure and injection time of metal molding process by a feed forward ANN integrated with the Gauss training method [119], nonlinear tensile modulus of injection molded polycarbonate samples as a function of process parameters [50], changes required in process parameters in order to achieve the desired final response in terms of dimensional accuracy [60], optimum control of the injection molding ram velocity using an ANN-based predictive learning controller [40], quality of molding process and machine parameters using an ANN-based flash monitoring system integrated with vibration monitoring and threshold prediction based on process parameter settings [16], process parameter based shrinkage using a forward mapping ANN model and shrinkage based optimal set of process parameters using a reverse mapping ANN model [73], optimum molding parameters for minimum defects in molded parts [89], better influence of process parameters on shrinkage in comparison to Moldflow® [115], etc. Due to their fast response and higher accuracy, the back propagation neural networks (BPNN) is also widely preferred in injection molding [11, 72], e.g., computation of mold complexity index based on difficulty in manufacturing injection molds [95], reduction in time requirement for planning and optimizing injection molding process parameters by supporting BPNN with experimental data [97], BPNN trained and tested with data generated by the Taguchi method for more accurate and effective prediction of warpage and shrinkage behaviour of injection molded thin walled parts than those of C-Mold® software and Taguchi method [67], BPNN trained and tested with data generated by Moldflow® for evaluating the optimal set of process parameters and warpage of injection molded automobile glove compartment by deriving relationships among them [123], etc. Although applied, ANN to injection molding has certain drawbacks. ANN works like a black box as a user has no control over output after feeding input and defining a general architecture of the molding process. It becomes difficult also to assess the accuracy and inter-relationships of input and output as peeking inside the hidden layers of ANN is not possible. Additionally, identifying the appropriate numbers of nodes and hidden layers is also a tough job, requiring the process to be retested if any data goes beyond its predictable range. Further, due to the requirement of increasing number of epochs with increasing nonlinearity of test data, BPNN models may consume extra time making them slower to train the process. Evolutionary algorithm (EA) In modern times, EAs have become influential in optimizing injection molding operations due to their capability of finding global optima without or little problem information and also to accommodate multiple objectives. Major works on injection molding process optimization using EAs include genetic algorithm (GA) for optimizing processing parameters [2, 29, 51] and for minimizing shrinkage in runner diameters [1], multi-objective GA for estimating runner size in a multiple cavity injection mold [124], optimization of cooling system layout in plastic injection molding [58, 62, 96], multiobjective GA for investigating performance characteristics of injection molding machine [31], GA to optimize input parameters so as to get the desired response in terms of dimensional shrinkage of injection molded plastic parts [23], GA-based intelligent conceptual mold layout design system to avoid human errors and design flaws in

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producing optimum conditions [9], and particle swarm optimization (PSO) for estimating process parameters with the assumption that most of the injection molded specimens have sheet like morphology [125]. Although applied widely, EAs also have certain drawbacks. The effectiveness of an EA is dependent upon the selection of fitness functions, process parameter ranges and elite preservation mechanism. Additionally, sometime the non-deterministic features of EAs make the search process tedious and difficult to reach the optima. Hybrid approaches Due to various drawbacks of an individual algorithm like ANN or EA, sometimes two or more techniques are combined as a hybrid approach to exploit their specific advantages which would not have been possible with their sole usage. Many such hybrid techniques have been investigated to injection molding optimization also, like GA with the Taguchi method for minimizing warpage of molded components [98], GA with ANN for optimizing the initial process settings [81], genetic neural fuzzy system with 2-stage hybrid learning algorithm to predict product weight [63, 64], GA with BPNN to achieve the optimal quality in terms of shear stress [101] and to minimize volumetric shrinkage [100], the Taguchi method combined with ANN and GA to achieve the minimal single response output in terms of warpage in a bus ceiling lamp base [55] and to save energy by multi-objective optimization of process parameters [70], the Taguchi method combined with BPNN and GA to determine the set of data in multiple-input single-output (MISO) by optimizing product weight [17] and to achieve multi response outputs [20], the Taguchi method and response surface method combined with BPNN and GA for predicting mechanical properties by estimating an optimal set of process parameters [110], the Taguchi method with Moldflow® for finding the efficient frontier for a thin digital camera cover in a MIMO environment [24], Moldflow® and orthogonal experiment method integrated with BPNN and GA to determine the optimal set of process parameters for optimizing warpage and clamp force [122], the variable complexity method combined with BPNN and GA to mice manufacturing for optimizing multiple objectives [26], GA with response surface methodology to achieve the optimal single response in terms of warpage in thin shell plastic parts [54] and to minimize sink depth in thermoplastic components [75], simulated annealing with ANN to predict part warpage in runner system by optimizing the runner dimensions [121], GA with a gradient-based method to find the optimum process parameters [59], PSO with ANN to optimize process parameters [103], BPNN with the Taguchi method and Davidson-Fletcher-Powell method to determine multiple input process parameters in order to achieve the desired product weight as the single output [18, 19], the Latin hypercube sampling method combined with the Kriging method and multi-objective PSO to achieve a better Pareto frontier by reducing simulation cost [21], the response surface method integrated with Moldflow® and Lingo software to optimize process parameters with corresponding output of warpage and shrinkage [22], GA with the mode-pursuing sampling method for achieving minimum warpage [30], ANN and artificial bee colony algorithm to determine the set of process parameters by minimizing warpage of molded components [42], the Taguchi method and ANOVA combined with GA and PSO to obtain optimal input and output process parameters by decreasing the process variance [25], Moldex3D software

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along with the radial basis function network based sequential optimization method in order to reduce warpage in the final plastic molded products with process parameters as design variables and short shot as design constraint [52], etc. Although various hybrid models are being put forward, a wide confusion has arisen nowadays among end users due to the huge variety and sheer quantity of those models as to which one is superior and commercially viable. Moreover, ability of those models, to be easily integrated with real time machines in shop floors and customized as per end users’ requirements, still leaves a big question mark on their practical utility. Additionally, barring a very few works, almost all others consider approximate techniques and optimize injection molding parameters as a single-objective function, either by adding weighted average of different criteria or by considering only a single criterion at a time, which is not an appropriate approach in the case of MIMO parameters of injection molding.

Real time techniques The need of the hour is to formulate a process with additional capabilities to customize it as per the requirements of users and to incorporate changes in the process due to real time optimization during injection molding operation. However, very few works have been conducted to formulate a novel real time process, which would not only precisely set the initial parameters but also optimize the process during the molding operation. Three such works are addressed here. Li et al. [65] proposed a GA-based real-time process optimization system, which not only caters to the initial injection molding parameter settings but also provides for online correction of any defect. In this system, trial runs are performed, using a fuzzy inference model to replace defective parameters from the GA generated initial process parameters, until a satisfactory product quality is obtained and the final fuzzy inference engine is fully developed for integration with the molding machine for real time process optimization and correction of defects. In the optimization process, however, some process parameters are predicted based on the geometric approximation of a rectangular edge gate plate. In addition, relations among various process parameters are formulated with some simplified assumptions. Although the system provides time savings compared to other simulating techniques, it has the drawbacks of using a search engine whose library data may not be capable for handling intricate molding components. Moreover, the optimization model considers only three quality indices, and their values are predicted by approximate methods which might not be accurate in certain cases. Stanek et al. [104] employed Moldflow® which offers automated process set up through a series of velocity and pressure phase configuration sequences, online process optimization through a robust process window formulated by an automated DOE, and production control through maintenance of the obtained optimized conditions. These facilities provide for better control of the injection molding process and minimization of the defects in components. This software seems to be a good solution for real time usage in the shop floor. However, no information is provided regarding the construction of the automated DOE in the optimization process. In a recent work, Zhao et al. [126] have developed an algorithm which intakes signals from the sensors fitted to the injection molding machine and continuously

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monitors the process in real time identifying different stages of the process. Based on the data obtained from the technique, the filling-to-packing switchover point and packing time are optimized accordingly.

Future scope Based on the literature survey carried out, it is observed that an ample scope is still there for conducting computational research in the area of injection molding process optimization. The following are few suggestions in which directions research can be conducted in future: 1. To develop a procedure having enough capability for precise prediction of process parameters, not only during the initial machine setup but also during operation, with additional features to be easily integrated into the machine setup. Such procedures also should be less time consuming and ergonomically sound, so as to be feasible in any shop floor. 2. Instead of developing a host of different models, more work may be conducted in developing a few standard models which would have the capabilities to avoid any confusion at the level of end users. 3. Although different input parameters may influence different output responses, many researchers simplify the problem by considering only a few assorted parameters and desired quality indices. Since injection molding is a complex process with MIMO features, effective study may be performed by considering it as a constrained multi-objective optimization problem with MIMO parameters. 4. Since injection molding parameters and machine constraints may be continuous as well as discrete based on the type and capability of machines and processes, models may be framed with the provision to customize for both discrete and continuous variables as per the requirements of end users.

Conclusions Intricate design, stringent quality tolerances and time constraints in today’s industrial scenario definitely calls for optimization techniques with a capability for precise prediction of process and machine parameters used in injection molding. A review of different techniques, used over the years in optimization of injection molding parameters for obtaining the desired quality indices, is conducted in this article. It is discussed how the earlier trial and error methods, based on the experiences and instincts of molding operators, have been gradually improved for better and precise prediction of different input and output parameters by optimizing injection molding through formal techniques developed by exploiting the advancement of technology and soaring ideas. Further, various drawbacks, faced by the proposed approaches in their practical implementations, are also discussed. Finally, some future research scopes in the area of injection molding process optimization are suggested.

Int J Plast Technol Acknowledgments The first author would like to thank the Department of Science and Technology – Scientific and Engineering Research Board (DST-SERB), Govt. of India, for providing the funding for this research vide SERB Project No: SB/FTP/ETA-88/2013.

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