Multi Response optimization of injection moulding Process parameters to reduce cycle time and warpage

Singh, Gurjeet; Pradhan, Mohan Kumar; Verma, Aditya

doi:10.1016/j.matpr.2017.11.534

Cited by 40 publications

(23 citation statements)

References 17 publications

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“…Noniterative optimization methods are easy to implement and require less computation. ey have been widely used in research, including the following methods: case-based reasoning (CBR) [3], expert system, fuzzy system [99], and the Taguchi method [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116].…”

Section: Noniterative Optimization Methodsmentioning

confidence: 99%

“…It has a computational structure that consists of a number of highly interconnected processing units called neurons. e neurons sum up weighted inputs and then apply a linear or nonlinear [103,104,109,115,116,123] Shrinkage [103,109,111] Mechanical properties [105,106,108] Multiple properties [102,107] Dimensional accuracy [100] Residual stress [101,103] Central composite design (CCD)…”

Section: Taguchi Methodmentioning

confidence: 99%

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Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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

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Section: Noniterative Optimization Methodsmentioning

confidence: 99%

Section: Taguchi Methodmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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“…ese include the injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature. e injection speed and injection pressure have been identified in [30,31], whereas the cooling time, packing pressure, mold temperature, packing time, and melt temperature have been identified in [3,[31][32][33][34][35][36] as having an effect on the product quality. e experiments in this work were conducted on an injection molding unit known as the Arburg Allrounder 420 C [37], and a simple mold with product dimensions depicted in Figure 2 was used.…”

Section: Injection Molding Experimentsmentioning

confidence: 99%

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Mukras

2020

Advances in Polymer Technology

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This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

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“…This can be done in parallel with conventional tooling methods that are used to manufacture less complex geometries of the mould. Through AM inserts, conformal cooling channels can be manufactured that conform to the product geometry, which can reduce the cycle time in an IM process while simultaneously increasing part quality by eliminating warpage and other defects by means of a uniform heat transfer from the product [8,9].…”

Section: Introductionmentioning

confidence: 99%

Cost-Effectiveness of Direct Metal Laser Sintered Maraging Steel Inserts for Plastic Injection Moulding Process

Combrinck

Booysen

et al. 2019

SAJIE

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This paper describes an investigation into the possible heat transfer benefits of conformal cooling channels using maraging steel MS1 inserts, which could result in a reduction of cycle times and cost per product, and improve part quality by eliminating defects such as warpage and heat sinks. A manufacturing cost and lead-time comparison showed that a conventionally manufactured insert reached its break-even point after fewer injection moulding cycles than an additive manufactured insert, due to its lower manufacturing costs. During high-volume production, the additive manufactured insert becomes more profitable to use, due to its shorter cycle times.

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Multi Response optimization of injection moulding Process parameters to reduce cycle time and warpage

Cited by 40 publications

References 17 publications

Intelligent Injection Molding on Sensing, Optimization, and Control

Intelligent Injection Molding on Sensing, Optimization, and Control

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

Cost-Effectiveness of Direct Metal Laser Sintered Maraging Steel Inserts for Plastic Injection Moulding Process

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