Multi-objective and many objective design of plastic injection molding process

Alvarado‐Iniesta, Alejandro; Cuate, Oliver; Schütze, Oliver

doi:10.1007/s00170-019-03432-8

Cited by 29 publications

(12 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the last example, we consider a many objective problem that arises in the design of plastic injection molding (PIM) processes. More precisely, as case study, we use the model of the design of a particular plastic gear as reported in Reference [56]. The problem can be written as…”

Section: Plastic Injection Moldingmentioning

confidence: 99%

See 1 more Smart Citation

Pareto Explorer for Finding the Knee for Many Objective Optimization Problems

Cuate

Schütze

2020

Mathematics

Self Cite

View full text Add to dashboard Cite

Optimization problems where several objectives have to be considered concurrently arise in many applications. Since decision-making processes are getting more and more complex, there is a recent trend to consider more and more objectives in such problems, known as many objective optimization problems (MaOPs). For such problems, it is not possible any more to compute finite size approximations that suitably represent the entire solution set. If no users preferences are at hand, so-called knee points are promising candidates since they represent at least locally the best trade-off solutions among the considered objective values. In this paper, we extend the global/local exploration tool Pareto Explorer (PE) for the detection of such solutions. More precisely, starting from an initial solution, the goal of the modified PE is to compute a path of evenly spread solutions from this point along the Pareto front leading to a knee of the MaOP. The knee solution, as well as all other points from this path, are of potential interest for the underlying decision-making process. The benefit of the approach is demonstrated in several examples.

show abstract

Section: Plastic Injection Moldingmentioning

confidence: 99%

“…where we have taken the minimal and maximal value according to each objective out of a given sample set. As the initial point, we have chosen x 0 := (230.00, 5.00, 94.73, 13.87) T , which is taken from Reference [56]. For PE, we used the step sizes τ 1 = 0.5 and τ 2 := 0.05.…”

Section: Plastic Injection Moldingmentioning

confidence: 99%

Pareto Explorer for Finding the Knee for Many Objective Optimization Problems

Cuate

Schütze

2020

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Alvarado-Iniesta et al (2017) integrated the NSGA-II algorithm with machine learning and multicriteria decision-making techniques to optimize the dimension of the plastic product, cycle time, and packing pressure in the injection molding process. Alvarado-Iniesta et al (2019), solved a many-objectives design problem associated with plastic gear using an NSGA-III algorithm to optimize seven objectives in a plastic injection molding process.…”

Section: Introductionmentioning

confidence: 99%

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

et al. 2021

View full text Add to dashboard Cite

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with nondominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance (IGD). To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

show abstract

“…e authors confirm through numerical and experimental results the validity of the proposed procedure. In [23] Alvarado et al proposed a multi-and many-objective optimization problem with 7 objectives including warpage, volumetric shrinkage, sink marks, Von Mises stress, shear stress, cycle time, and clamping force. Four process parameters, which included melt temperature, packing time packing pressure, and cooling time, were optimized.…”

Section: Introductionmentioning

confidence: 99%

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

Mukras

2020

Advances in Polymer Technology

View full text Add to dashboard Cite

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.

show abstract

Multi-objective and many objective design of plastic injection molding process

Cited by 29 publications

References 50 publications

Pareto Explorer for Finding the Knee for Many Objective Optimization Problems

Pareto Explorer for Finding the Knee for Many Objective Optimization Problems

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

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

Contact Info

Product

Resources

About