Study on Cost Estimate at Product Design Stage Based on Factor Analysis and Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 1781-1785 ◽  
Author(s):  
Yi Zheng ◽  
Ming Hua Wang ◽  
Tao Yang
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Product Design ◽  
Bp Neural Network ◽  
Concurrent Engineering ◽  
Cost Control ◽  
New Products ◽  
Cost Estimate ◽  
Design Stage ◽  
The Cost

The cost estimate plays an important role in cost control and developing new products at the design stage. To improve the accuracy of cost estimate, we extract the feature parameters using the theory of concurrent engineering and factor analysis. Then we propose DCEM that is the model of cost estimate based on factor analysis and BP neural network. The model not only simplifies the input of BP neural network, but also avoids the coupling among the input parameters. The result shows that the model’s performance is stable and it can estimate the cost more accurate at the early product design stage.

An Improved BP Neural Network Algorithm Based on Factor Analysis

2010 ◽  
Vol 5 (4) ◽  
pp. 103-108 ◽  
Author(s):  
Shifei Ding ◽  
Weikuan Jia ◽  
Chunyang Su ◽  
Xiaoliang Liu ◽  
Jinrong Chen
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Bp Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals The Cost of Learning from Failures and Mistakes in Product Design: Reviewing the Literature

2019 ◽  
Vol 1 (1) ◽  
pp. 1653-1662
Author(s):  
Ali Shafqat ◽  
Josef Oehmen ◽  
Torgeir Welo ◽  
Pelle Willumsen
Keyword(s):  
Product Development ◽  
Product Design ◽  
Low Cost ◽  
New Products ◽  
Design Phase ◽  
Learning Methods ◽  
Lack Of Information ◽  
The Cost ◽  
Future Work ◽  
Conceptual Foundations

AbstractIn the design phase of product development (PD) process, most new products face significant uncertainties and risks. Uncertainty is typically associated with a lack of information, while learning is a process that acquires information. Therefore, learning fast and at low cost decreases the uncertainty and increases the efficiency of the product design phase. This paper investigates the concept of the cost of learning in PD's design phase. Reviewing the literature, we conceptualize the cost of learning and review the learning methods considering three aspects in the design phase of the PD process: (1) costs associated with learning from mistakes and failures, (2) learning methods and (3) categories of learners. This paper thus provides the conceptual foundations for future work to increase the efficiency of the PD process by reducing the cost of learning from mistakes and failures.

Prediction of Cutting Tool Life Based on ACO-BP Meural Network

2013 ◽  
Vol 655-657 ◽  
pp. 1714-1717 ◽  
Author(s):  
Tie Liu Wang ◽  
Xian Ming Chen ◽  
Shui Bin Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Tool Life ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Back Propagation ◽  
Global Search ◽  
Machining Parameters ◽  
The Cost ◽  
Search Capability

For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.

Evaluation Model for Supply Chain Performance Based on BP Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 2502-2507
Author(s):  
Da Li Jiang ◽  
Xin Guang Zhao ◽  
Liang Tao Peng
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Customer Service ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Correlation Coefficients ◽  
Supply Chain Performance ◽  
Development Capacity ◽  
The Index System ◽  
The Cost

The performance of a supply chain can be evaluated according to its customer service, the cost condition, cooperation and development capacity. This paper proposed the index system of supply chain and evaluation model based on BP Neural Network. The experiment proved this model can be immune from the inherent subjectivity and uncertainty of traditional methods in choice of weight and Correlation coefficients.

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