PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

H., Z.

doi:10.1016/j.cie.2010.01.004

Cited by 64 publications

(36 citation statements)

References 24 publications

(25 reference statements)

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“…However, despite of the technological importance of plastic parts few works have been reported about the quantitative evaluation of surface moulds (Chan et al 2008;Sun et al 2001). On the other hand, several authors affirm that the product cost in the concept design stage accounts for only 6 % of the total cost, but determines 70-80 % of product cost and 80 % of quality (Che 2010). In this context, the present paper proposes a new methodology to improve the surface evaluation of plastic moulds.…”

Section: Introductionmentioning

confidence: 91%

Artificial intelligence based system to improve the inspection of plastic mould surfaces

et al. 2014

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Plastic industry is today in a constant growth, demanding several products from other segments, which includes the plastic moulds, used mainly in the injection moulding process. This paper presents a methodology for the surface evaluation of plastic moulds, aiming the automation of the polishing surface analysis. Provided that this type of analysis by traditional procedures can be slow and expensive, the development of automatic system could lead to considerable improvements regarding the speed and reliability of information. The starting point of the evaluation procedure is the image generated by the laser light scattered over the sample mould surface that could be captured and analysed by image processing and artificial intelligence techniques. The results showed that the proposed system is able to mapping and classifying several damages over the polished surface and could be an alternative to reduce efficiently the costs and the spending time in mould surface inspection tasks.

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Section: Introductionmentioning

confidence: 91%

Artificial intelligence based system to improve the inspection of plastic mould surfaces

et al. 2014

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“…To ensure the assembly performance, the downstream process will be adjusted by considering the relationship between the process and the upstream process. In view of the BP neural network has the characteristics of strong self-learning, selforganizing and self-adaptive and can approximate any nonlinear function with any precision and has better fault tolerance [32][33][34][35][36]. So, this paper proposed the model of AQCTO and APP based on the BP neural network.…”

Section: The Methods Of Aqac Based On Bp Neural Networkmentioning

confidence: 97%

“…The connection weights and threshold of BP neural network's each layer will be optimized by PSO algorithm. Compared with the traditional BP neural network, the method is a better way to solve the problems of the slow convergence rate, easiness into local optimal solution and the low generalization ability [34][35][36]. It is important to significantly improve the accuracy and speed of the models for online quality control.…”

Section: The Methods Of Aqac Based On Bp Neural Networkmentioning

confidence: 99%

Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

Xiao-qiao

Liu

et al. 2015

Computers in Industry

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“…Articles which are the most significant for this research are related to product complexity [9] to [11], and the implementation of ANN in the mold production estimation process [9] and [12]. All these approaches give quite accurate estimates only when used for very specific types of products.…”

Section: The Project Estimation Processmentioning

confidence: 99%

Neural Network-Based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing

Florjanič¹,

Govekar²,

Kuzman³

2013

SV-JME

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The mold making industry is project driven, and as such it has to cope with the characteristics of an individual production process. One of the major sources of risk in project management is the inaccurate forecast of project costs, demand, and other impacts [1]. In the mold production process it is crucial to minimize uncertainty in the early project estimation phase. The estimation phase is commonly a human expert driven activity which is sensitive to the expert's bias. This bias can lead to an underestimation of project resources when the estimator is overconfident, or to over-estimation of project resources when the estimator does not have sufficient confidence that all aspects of the project can be properly covered. Both scenarios have a negative impact on future business. In the case of underestimation, the project will bring economic loss, and in the case of overestimation, it will most likely be assigned to a competitive supplier. The estimator's key competence is to properly collect and evaluate all significant information for making the project estimation successful. The contradiction lies in the fact that the estimator should spend minimal time necessary on estimation activity since usually less than 10% of all offers turn into orders in the mold making industry, as stated in [2] to [4].Estimations in the mold manufacturing business still rely heavily on intuitive methods, which are subjective and prone to reliability and repeatability problems. A solution for these problems is addressed in this article, with the development of a supported expert driven project estimation process.In the project estimation process the volume of manufacturing hours represents one of the most important pieces of information. It reflects the majority of costs in the final project price, and it most significantly shapes the project schedule. The research objective is to develop an ANN-supported, expert driven project estimation process to improve the estimation of the volume of manufacturing hours in the mold production. In addition to the development of a reliable estimation model, it is also very important to properly position the supporting model in the expert driven estimation process. Therefore, in addition to model building, the problem of proper position of the supporting model will be addressed in the paper.Following these aims, first an overview of estimation process is given. Then, the solution for the problem of proper placement of an estimation support model is addressed. Furthermore, the proposed ANN-based model for estimation of the volume of manufacturing hours is presented. Finally, the results of ANN modelling are presented and discussed. THE PROJECT ESTIMATION PROCESSA major challenge of the project estimation process in general is to achieve sufficient project estimation reliability within minimal time consumption for this operation. Estimation reliability is directly related to the amount and quality of the data available at the moment the estimation process takes place. As shown in Fig. 1, the availabi...

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PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

Cited by 64 publications

References 24 publications

Artificial intelligence based system to improve the inspection of plastic mould surfaces

Artificial intelligence based system to improve the inspection of plastic mould surfaces

Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

Neural Network-Based Model for Supporting the Expert Driven Project Estimation Process in Mold Manufacturing

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