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The author proposed a friction plunge micro-welding (FPMW) method and applied it to column grid array packaging to realize the connection of copper columns without precision molds assisted positioning. The purpose of this paper is to study the flow behavior of the solder undergoing frictional thermo-mechanical action during the FPMW and to determine the source of the solders in the micro-zones with different microstructure characteristics near the solder/Cu column friction interface.

Three kinds of Sn58Bi/SAC305 and SAC305/Pb90Sn composite solder samples were designed to study the flow behavior of the solder during FPMW using Bi and Pb as tracer elements.

The results show that most of the solders in the position occupied by the copper column was softened and plasticized during the welding process and was extruded to side of the copper column, flowing axially, circumferentially and radially along a trajectory similar to a conical spiral line. Under the drive of the tangential friction force and the radial hold-tight force, the extruded out visco-plastic solders fully mixed with the visco-plastic solders on the sides of the copper column, and bonded with the solders that deformed plastically on the periphery, so that a stir zone and a dynamic recrystallization zone finally evolved. The outside plastically deformed solders evolved into a thermo-mechanical affected zone.

The flow behavior of the solder during the FPMW was determined, as well as the source of the solders in micro-zones with different microstructure characteristics.

This paper aims to discuss how acrylic resin influences the smoke generation of intumescent flame retardant coatings.

Thermal decomposition kinetics is used in this study to simulate the burning process. The thermal decomposition of acrylic resin can be identified in the intumescent coatings through the multi-peak fitting of derivative thermogravimetric (DTG) curves. The dormant influence of acrylic resin, combined with the smoke density, is calculated.

Multiple peaks fitting method of DTG curves helps estimate the decomposition process of acrylic resin in flame retardant coating. Combining DTG data with the smoking curve, smoking generation of acrylic resin during the combustion could be evaluated. The decomposition conversion rate of acrylic resin is 21.13 per cent. Acrylic resin generates 34.64 per cent of the total amount of smoke produced during the combustion of intumescent flame retardant coatings.

All the other intumescent flame retardant coating systems could be studied using the same approach as that used in this work to achieve an improved understanding of the smoke generation process during combustion.

The method developed here provided a simple and practical solution to analyse the decomposition and smoking generation of acrylic resin in the coating mixtures. It also can be used to analyse any thermal decomposition process of any mixed compounds.

The analysis method to evaluate resin’s smoking generation of coating’s total generation is novel, and it could be applied in all kinds of coatings and mixtures to estimate the smoking generation of one composition.

It is a challenge to print a model with the size that is larger than the working volume of a three-dimensional (3D) printer. The purpose of this paper is to present a feasible approach to divide a large model into small printing parts to fit the volume of a printer and then assemble these parts into the final model.

The proposed approach is based on the skeletonization and the minima rule. The skeleton of a printing model is first extracted using the mesh contraction and the principal component analysis. The 3D model is then partitioned preliminarily into many smaller parts using the space sweep method and the minima rule. The preliminary partition is finally optimized using the greedy algorithm.

The skeleton of a 3D model can effectively represent a simplified version of the geometry of the 3D model. Using a model’s skeleton to partition the model is an efficient way. As it is generally desirable to have segmentations at concave creases and seams, the cutting position should be located in the concave region. The proposed approach can partition large models effectively to well retain the integrity of meaningful parts.

The proposed approach is new in the rapid prototyping field using the model skeletonization and the minima rule. Based on the authors’ knowledge, there is no method that concerns the integrity of meaningful parts for partitioning. The proposed method can achieve satisfactory results by the integrity of meaningful parts and assemblability for most 3D models.

This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

The construction industry contributes to economic development by providing physical equipment and infrastructures. However, it also generates some undesirable outputs such as waste and environmental pollution, especially in developing countries. Due to the importance of the green supply chain management (GSCM) philosophy, for solving these problems, the current study aims to evaluate the drivers of GSCM adoption in the construction industry of Iran.

This research uses a descriptive and practical methodology. The participated experts in the study include senior managers of the construction department in Rasht municipality who had relevant academic education and suitable experiences in urban and industrial construction. The experts took part in both qualitative and quantitative phases of the research, namely verification of the drivers extracted from literature and ranking them in ascending order. In the quantitative phase, Step-Wise Weight Assessment Ratio Analysis (SWARA) as a new multi-criterion decision-making (MCDM) method is used to evaluate the drivers of GSCM adoption using MATLAB software.

The results show that environmental management systems, green product design and innovational capability with weights of 0.347, 0.218 and 0.143 are the most significant sub-drivers, respectively. The less important factor is an investment in environmental technology.

This study evaluated the motivational factors of GSCM in the construction industry. The findings help governments, companies and green supply chain (GSC) managers to improve their knowledge about GSCM and make the best decisions to decrease environmental pollution.

Over the last two decades, green supply chain management (GSCM) has enabled businesses to operate in an environmentally friendly manner. The present review examines 234 research articles and proposes a methodical literature review on GSCM, focusing on the aspects of sustainable development.

The work examines conceptual, analytical, empirical and non-empirical research articles, analyzing at all levels of the organization, such as firm, dyad, supply chain and network. The objective of the review is to provide insights into the state and scope of existing research in the domain of GSCM, to identify the prevalence of GSCM and to consolidate the trend of future research. The literature review follows a systematic methodology for analyzing the literature.

The findings can support researchers in identifying research areas with significant impact and streamline research on GSCM in the future. Practitioners can utilize this structured classification to strategize their green initiatives in their firms.

The work contributes to providing literature that explores a detailed review in GSCM. The proposed literature review captures critical aspects in the domain of GSCM and offers future research directions.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.