Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: II. with Hybrid GA-BP Method

2010 ◽  
Vol 154-155 ◽  
pp. 1091-1095
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Process Design ◽  
Hot Pressing ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Ceramic Tool ◽  
Die Materials ◽  
Die Material ◽  
Two Hybrid

In this paper, the two hybrid algorithms of back propagation artificial neural network and genetic algorithm were used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. Compared with the BP algorithm, the predicted results of hybrid algorithm indicated that the combination algorithm can offer a robust and efficient way for the fabrication process design of ceramic tool and die materials.

Risk Assessment for Remanufacturing System Based on Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1382-1385 ◽  
Author(s):  
Qiu Lian Wang ◽  
Cong Bo Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Model ◽  
The Internet ◽  
Risk Assessment Model ◽  
Key Indicators ◽  
The Relationship ◽  
Evaluating Method

To provide referenced risk assessment model for implementing remanufacturing program in enterprise, a set of evaluating indicators was proposed according to the characteristics of the remanufacturing program’s life cycle, which includes acquisition, assessment, disassembly, reproducing and reprocessing phases; And Back Propagation neural network (BPNN) was applied to measure the risk of the remanufacturing system as evaluating method; In addition, the influence of the evaluating indicators on the output was calculated by the Relationship Function between the networked weights, so the key indicators can be found out. The risk assessment model is trained by five samples obtained from the Internet, and is verified by the case of one machining tools company.

scholarly journals Neural network method for control valve cost estimation on the EPC project bidding

2018 ◽  
Vol 49 ◽  
pp. 02004 ◽  
Author(s):  
Gilang Almaghribi Sarkara Putra ◽  
Rendra Agus Triyono
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Control Valve ◽  
Back Propagation Neural Network ◽  
Neural Network Method ◽  
Price Quotation ◽  
Network Method ◽  
Alternative Choice ◽  
The Cost

Cost estimation on the bidding phase is a crucial stage that determines the success of the Engineering, Procurement and Construction (EPC) project. If the cost offered to the client is too high then it could not compete with the other bidder, but if the cost offered are too low it can reduce profit margins and result in losses for the EPC companies. This paper describe the use of Back Propagation Neural Network method to help determine cost estimation. This method is applied specifically to determine control valve cost estimation on the bidding phase so that the retrieved costs will be accurate. When there is no technical and price quotation from vendors as well as the narrowness of the bidding processing time, this method can be an alternative choice to determine the price based on previous vendor quotation. In the future, this method could be developed and applied for other instrumentation equipment such as transmitter, switch, analyzer, control system and others to achieve total cost estimation of instrumentation equipment in EPC bidding proposal.

High-Pressure Water Spray Research Based on Neural Network

2010 ◽  
Vol 29-32 ◽  
pp. 138-142
Author(s):  
Rui Li ◽  
Zi Ming Kou
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Influencing Factors ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Industrial Processes ◽  
Water Spray ◽  
Key Factors ◽  
Pressure Water ◽  
The Relationship

The spray cleaning method is important and universal in many industrial processes and other occasion. Because the size of the waterdrop is one of key factors for cleaning, this paper not only studied the relationship between the size of waterdrop and other influencing factors, but also researched the forecasted method for the size of waterdrop. In lab, by measuring the size of the waterdrop, jetted by one kind of nozzle, data were acquired and were used to train the Back Propagation Neural Network ( BPNN ). Through comparing those diameters, between measured in lab and calculated by BPNN after trained. It was acquired that the maximum errors was smaller than 1.62%, between the computed results and the factual measured ones. The experimental results showed that BPNN is an effective tool to predict the variation of the non-linear waterdrop diameter.

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