scholarly journals Optimization of Polymer Processing: A Review (Part II- Molding Technologies)

2021 ◽  
Author(s):  
António Gaspar-Cunha ◽  
Janusz Sikora ◽  
José A. Covas
Keyword(s):  
Optimization Problems ◽  
Polymer Processing ◽  
Optimization Techniques ◽  
Function Optimization ◽  
Optimization Approach ◽  
Processing Technologies ◽  
Twin Screw Extruders ◽  
Extrusion Dies ◽  
Twin Screw ◽  
Processing Optimization

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Also, within the field of artificial intelligence, several approaches can be reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies in single and twin screw extruders, extrusion dies and calibrators. This second part focus on injection molding, blow molding and thermoforming technologies.

scholarly journals Optimization of Polymer Processing: A Review (Part I—Extrusion)

Materials ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 384
Author(s):  
António Gaspar-Cunha ◽  
José A. Covas ◽  
Janusz Sikora
Keyword(s):  
State Of The Art ◽  
Polymer Processing ◽  
Optimization Techniques ◽  
Practical Situation ◽  
Twin Screw Extruders ◽  
Extrusion Dies ◽  
Twin Screw ◽  
Processing Techniques ◽  
Screw Extruders ◽  
Modelling Approach

Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related to the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems and define the best procedure for each particular practical situation. For this purpose, the state of the art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focuses on extrusion, namely single and twin-screw extruders, extrusion dies, and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing with a very good performance and without the need of demanding computation requirements.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

scholarly journals Non-Convex Feasibility Robust Optimization Via Scenario Generation and Local Refinement

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case

Abstract Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. The proposed approach is based on an integration of two techniques: (i) a sampling-based scenario generation scheme and (ii) a local robust optimization approach. An analysis of the computational cost of this integrated approach is performed to provide worst-case bounds on its computational cost. The proposed approach is applied to several non-convex engineering test problems and compared against two existing robust optimization approaches. The results show that the proposed approach can efficiently find a robust optimal solution across the test problems, even when existing methods for non-convex robust optimization are unable to find a robust optimal solution. A scalable test problem is solved by the approach, demonstrating that its computational cost scales with problem size as predicted by an analysis of the worst-case computational cost bounds.

Fuzzy Optimization of Complex Systems Using Approximation Concepts

1997 ◽  
Vol 50 (11S) ◽  
pp. S97-S104 ◽  
Author(s):  
Hector A. Jensen ◽  
Abdon E. Sepulveda
Keyword(s):  
Original Problem ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Optimization Techniques ◽  
System Response ◽  
Optimization Approach ◽  
Design Variables ◽  
Intermediate Response ◽  
Intermediate Variables ◽  
Approximation Concepts

This paper presents a methodology for the efficient solution of fuzzy optimization problems. Design variables, as well as system parameters are modeled as fuzzy numbers characterized by membership functions. An optimization approach based on approximation concepts is introduced. High quality approximations for system response functions are constructed using the concepts of intermediate response quantities and intermediate variables. These approximations are used to replace the solution of the original problem by a sequence of approximate problems. Optimization techniques for non-differentiable problems which arise in fuzzy optimization are used to solve the approximate optimization problems. Example problems are presented to illustrate the ideas set forth.

An Agent Based Optimization Approach to Manufacturing Process Planning

2004 ◽  
Vol 126 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

Numerical Simulation of Mixing Performance of Intermeshing Co-Rotating Tri-Screw and Twin-Screw Extruders

2012 ◽  
Vol 468-471 ◽  
pp. 2211-2214 ◽  
Author(s):  
G. Wang ◽  
X.Z. Zhu ◽  
Chun Yi Sun
Keyword(s):  
Shear Rate ◽  
Polymer Melt ◽  
Polymer Processing ◽  
Mixing Efficiency ◽  
Twin Screw Extruder ◽  
Screw Extruder ◽  
Twin Screw Extruders ◽  
Mixing Performance ◽  
Twin Screw ◽  
Dispersive Mixing

Parallel arranged tri-screw extruder (PATSE) is a new machine of polymer processing and first manufactured in recent years in China. Compared with the traditional twin-screw extruder, PATSE adds a screw, and added an intermeshing region. It is well known that material going though intermeshing region will acquire higher shear rate and stretching rate, which is beneficial to mixing processing. In order to know the mixing performance in cross-section for PATSE, polymer melt flow field simulation and mixing simulation were conducted on PATSE with 2D model and a Carreau flow model to evaluate velocity profiles, particle trajectories, max shear rate, max stretching rate, dispersive mixing, distributive mixing, segregation scale, length of stretch, mixing efficiency with the commercial CFD package Polyflow and compared with those of twin screw extruder (TSE). The results show that PATSE has better mixing performance than TSE.

Experiment Driven Local Optimization (EDLO) With an Application to Additive Manufacturing

2017 ◽  
Author(s):  
J. M. Hamel
Keyword(s):  
Additive Manufacturing ◽  
Objective Function ◽  
Design Optimization ◽  
Optimization Problems ◽  
Numerical Test ◽  
Local Optimization ◽  
Optimization Techniques ◽  
Decision Makers ◽  
Optimization Approach ◽  
Physical Experiments

The rise of the use of additive manufacturing processes by engineering and research enterprises has greatly increased opportunities for decision makers to quickly evaluate complex geometries and components throughout the design process. This capability makes it possible to explore design trade-off for which computational or analytical models are not readily available, or are impractical to obtain given available time and resources. However, this often means that decision makers are forced to rely primarily on physical experiments for design data, and this greatly limits opportunities for the application of design optimization techniques. To meet this challenge, a new so-called “online” surrogate based optimization approach, called Experiment Driven Local Optimization (EDLO), has been developed. This approach is focused specifically on using approximation techniques and real-time online surrogate training to solve design optimization problems where the system objective function must be evaluated using physical experiments. As a result, this approach is ideally suited to design problems focused on components that are fabricated using additive manufacturing. This approach is capable of mitigating the effects of experimental uncertainty (or noise) in a design objective function, does not require close coordination between the objective function evaluations and the optimizer, and requires as few physical experiments as possible. These capabilities have been demonstrated through the use of several numerical test problems and also through a 3D printed compliant mechanism design problem. The results produced by the EDLO approach are encouraging and show that this new technique has the potential to move decision makers working with physical experiments and additive manufacturing away from engineering intuition and towards the greater potential of design optimization techniques.

scholarly journals Optimization of Polymer Processing: A Review (Part I- Extrusion)

2021 ◽  
Author(s):  
António Gaspar-Cunha ◽  
Janusz Sikora ◽  
José A. Covas
Keyword(s):  
Optimization Algorithm ◽  
State Of The Art ◽  
Polymer Processing ◽  
Optimization Techniques ◽  
Extensive Review ◽  
Review Of The Literature ◽  
Practical Situation ◽  
Extrusion Dies ◽  
Processing Techniques ◽  
Modelling Approach

Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related with the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems, and define the best procedure for each particular practical situation. For this purpose, a state-of-the-art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focus on extrusion, namely extruders, extrusion dies and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing, with a very good performance and without the need of demanding computation requirements.

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