Simulating surface tension of Sn-based lead free solder using an artificial neural network. Issue 4 (5th September 2016)
- Record Type:
- Journal Article
- Title:
- Simulating surface tension of Sn-based lead free solder using an artificial neural network. Issue 4 (5th September 2016)
- Main Title:
- Simulating surface tension of Sn-based lead free solder using an artificial neural network
- Authors:
- Wu, Min
Su, Xiangyu - Abstract:
- Abstract : Purpose: Because of the complexity of relationship between surface tension and its decisive factors, such as temperature, concentration, electronic density, molar atomic volume and electro-negativity, a reasonable predicting model of surface tension of Sn-based solder alloys has not been developed yet. The paper aims to address the surface tension issue that has to be considered if the new lead free solder will be applied for electronics. Design/methodology/approach: Using an artificial neural network (ANN) model with back-propagation (BP) algorithm, the surface tension for Sn-based binary solder alloys was simulated, and the comparison between the simulating results and data from experiments and literatures was analyzed as well. In addition, the relationship between surface tension and its decisive factors would be discussed based on the ANN and orthogonal design methods. Findings: It is shown that the predicting model of surface tension of Sn-based solder alloys is constructed according to the BP–ANN theory, and the predicted value from the BP–ANN is in excellent agreement with the experimental results. The surface tension of Sn-based solders is determined by five factors, i.e. temperature, concentration, electronic density, molar atomic volume and electro-negativity. Among of the factors, molar atomic volume is major factor, and the order of degree of influence on surface tension is molar atomic volume > electro-negativity > electronic > density > concentrationAbstract : Purpose: Because of the complexity of relationship between surface tension and its decisive factors, such as temperature, concentration, electronic density, molar atomic volume and electro-negativity, a reasonable predicting model of surface tension of Sn-based solder alloys has not been developed yet. The paper aims to address the surface tension issue that has to be considered if the new lead free solder will be applied for electronics. Design/methodology/approach: Using an artificial neural network (ANN) model with back-propagation (BP) algorithm, the surface tension for Sn-based binary solder alloys was simulated, and the comparison between the simulating results and data from experiments and literatures was analyzed as well. In addition, the relationship between surface tension and its decisive factors would be discussed based on the ANN and orthogonal design methods. Findings: It is shown that the predicting model of surface tension of Sn-based solder alloys is constructed according to the BP–ANN theory, and the predicted value from the BP–ANN is in excellent agreement with the experimental results. The surface tension of Sn-based solders is determined by five factors, i.e. temperature, concentration, electronic density, molar atomic volume and electro-negativity. Among of the factors, molar atomic volume is major factor, and the order of degree of influence on surface tension is molar atomic volume > electro-negativity > electronic > density > concentration > temperature. Moreover, a simply reasonable equation is proposed to estimate the surface tension for Sn-based solders. Originality/value: The five decisive factors of surface tension for Sn-based binary solder alloys have been analyzed theoretically, and a reasonable model of surface tension for Sn-based binary solder alloys is proposed as well. It is shown that ANN theory will be applied well to simulate the surface tension of Sn-based lead free solder. … (more)
- Is Part Of:
- Soldering & surface mount technology. Volume 28:Issue 4(2016)
- Journal:
- Soldering & surface mount technology
- Issue:
- Volume 28:Issue 4(2016)
- Issue Display:
- Volume 28, Issue 4 (2016)
- Year:
- 2016
- Volume:
- 28
- Issue:
- 4
- Issue Sort Value:
- 2016-0028-0004-0000
- Page Start:
- 201
- Page End:
- 206
- Publication Date:
- 2016-09-05
- Subjects:
- Artificial neural network -- Sn -- BP -- Lead free solders -- Surface tension
Brazing -- Periodicals
Solder and soldering -- Periodicals
671.5605 - Journal URLs:
- http://www.emeraldinsight.com/journals.htm?issn=0954-0911 ↗
http://www.emeraldinsight.com/ ↗ - DOI:
- 10.1108/SSMT-01-2016-0002 ↗
- Languages:
- English
- ISSNs:
- 0954-0911
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8327.242650
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9.xml