Hybrid NSGA-II for an imperfect production system considering product quality and returns under two warranty policies

2019 ◽  
Vol 75 ◽  
pp. 333-348 ◽  
Author(s):  
Ata Allah Taleizadeh ◽  
Pooya Pourrezaie Khaligh ◽  
Ilkyeong Moon
Keyword(s):  
Product Quality ◽  
Production System ◽  
Nsga Ii ◽  
Imperfect Production ◽  
Warranty Policies

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction

2018 ◽  
Vol 66 (4) ◽  
pp. 344-355 ◽  
Author(s):  
Iris Weiß ◽  
Birgit Vogel-Heuser
Keyword(s):  
Data Mining ◽  
Product Quality ◽  
Statistical Methods ◽  
Analysis Of Variance ◽  
Production System ◽  
Production Systems ◽  
High Potential ◽  
Quality Prediction ◽  
Automated Production ◽  
Effective Use

AbstractData mining in automated production systems provide high potential to increase the Overall Equipment Effectiveness. Nevertheless, data of such machines/plants include specific characteristics regarding the variance and distribution of the dataset. For modelling product quality prediction, these characteristics have to be analysed to interpret the results correctly. Therefore, an approach for the analysis of variance and distribution of datasets is proposed. The evaluation of this approach validates the developed guidelines, which identify the reasons for inconsistent prediction results based on two different datasets of the same production system.

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