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AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity.

The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity.

The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm³ and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm³. The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains.

The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications.

To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

This paper is to present a new failure model that can be applied to single‐sample failure data of a single system under testing.

The Bayesian method is used for the reliability evaluation. The weighted least squares method is used for determining the parameters of the reliability function.

The authors have observed the operation of a special computer numerical control (CNC) system for a period of over two years, and maintained a reliability database will all the collected failure data, from which the main source of failures can be identified.

Preliminary research results are very encouraging. However, more work will be necessary to validate the new failure model.

The determination of the parameters of the reliability function of a system under testing helps to identify its failure characteristics and potential quality problems.

It is hoped that the paper can help understand some of the challenges in modeling the failure behavior of special CNC systems.

The lack of reference architecture for Internet of Things (IoT) modeling impedes the successful design and implementation of an IoT-based production logistics and supply chain system (PLSCS). The authors present this study in two parts to address this research issue. Part A proposes a unified IoT modeling framework to model the dynamics of distributed IoT processes, IoT devices, and IoT objects. The models of the framework can be leveraged to support the implementation architecture of an IoT-based PLSCS. The second part (Part B) of this study extends the discussion of implementation architecture proposed in Part A. Part B presents an IoT-based cyber-physical system framework and evaluates its performance. The paper aims to discuss this issue.

This paper adopts a design research approach, using ontology, process analysis, and Petri net modeling scheme to support IoT system modeling.

The proposed IoT system-modeling approach reduces the complexity of system development and increases system portability for IoT-based PLSCS. The IoT design models generated from the modeling can also be transformed to implementation logic.

The proposed IoT system-modeling framework and the implementation architecture can be used to develop an IoT-based PLSCS in the real industrial setting. The proposed modeling methods can be applied to many discrete manufacturing industries.

The IoT modeling framework developed in this study is the first in this field which decomposes IoT system design into ontology-, process-, and object-modeling layers. A novel implementation architecture also proposed to transform above IoT system design models into implementation logic. The developed prototype system can track product and different parts of the same product along a manufacturing supply chain.

The paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system.

The system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management.

The proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants.

Collectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.

This paper aims to describe an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of wastewater treatment (WWT) technology for treating wastewater.

The proposed approach is based on Analytical Hierarchy Process (AHP) under fuzzy environment, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) and hierarchy Grey Relation Analysis (GRA) techniques. Two models are proposed to evaluate the best WWT. The first model, Fuzzy Analytical Hierarchy Process (FAHP) is integrated with Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) technique. The second model, FAHP is integrated with hierarchy Grey Relation Analysis (GRA) technique. The Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of criteria and then ranking of the WWT technology is determined by PROMETHEE and GRA.

An efficient pair‐wise comparison process and ranking of alternatives can be achieved for WWT technology selection through the integration of FAHP and PROMETHEE, FAHP and GRA.

The paper highlights a new insight into MCDM techniques to select an optimum WWT technology selection for the paper manufacturing industry.

The purpose of this paper is to analyze changes in consumers’ knowledge and acceptance of genetically modified (GM) foods over the past decade and identifies the determinants in the consumer attitudes toward GM foods in urban China.

The data used in this study were collected from 952 urban consumers in 2013 in 15 provinces. The ordinal logit model was chosen to identify the determinants in the consumers’ subjective knowledge and acceptance of GM foods.

Results show that the consumers’ awareness of GM foods, biotech knowledge, and subjective knowledge improved significantly, while the acceptance rate toward GM foods declined considerably from 2002 to 2013. Moreover, the consumers’ subjective knowledge of GM foods had a significantly negative impact on their acceptance rate of GM foods. Finally, the media coverage with the “event of Golden rice in 2012” as a proxy helped consumers in shaping their negative perceptions toward GM foods, suggesting that the media coverage was one of major factors in leading to the low acceptance rate of GM foods in urban China.

The findings of previous studies conducted in the early 2000s might not reflect current Chinese consumer attitudes because the public opinion toward GM foods in modern China has considerably changed. This study thus filled in the void by updating estimates on consumer attitudes toward GM foods and by underlining the factors that have led to the changes in consumer attitudes, using a mass survey covering Chinese urban consumers in 15 provinces in 2013.