Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.     

New version of continuous–discrete cubature Kalman filtering for nonlinear continuous–discrete systems

For nonlinear continuous–discrete systems, this paper elaborates a new accurate implementation of continuous–discrete cubature Kalman filter (CD-CKF). As the main contribution of this work, the new Kalman prediction stage begins by integrating the nonlinear continuous model for all the cubature sample vectors; the prior estimate state and covariance prediction are based on the weighted statistics of these integrated cubature sample vectors and the Gauss–Legendre approximation scheme. The new square root form CD-CKF is also derived and accurately implemented by combining with the modified variable stepsize NIRK. As the advantages of proposed approach, the complicated and error-prone processes of solving covariance differential equation or calculating derivatives are avoided, while the positive semi-definiteness of prior error covariance are numerically guaranteed. Simulations of traffic control scenarios further confirm the new approach’s superior filtering performance in both reliability and accuracy.     

Binder jetting additive manufacturing of aluminum nitride components

In this work, we report on the novel fabrication of aluminum nitride (AlN) components using Binder Jetting (BJT) additive manufacturing (AM). The AlN constructs were subjected to post-fabrication thermal treatment by hot isostatic pressing (HIPing) for 8 hours at a pressure of 206 MPa and temperature of 1900 °C. This treatment resulted in a 60.1% relative density maximum densification for AlN. The BJT printed AlN specimens were analyzed using various characterization techniques. The purity, microstructure, and polycrystallinity of the AlN phase formed were confirmed by techniques that included x-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), and high-resolution transmission electron microscopy (HRTEM). Second harmonic generation (SHG) microscopy showed polarization dependence and second harmonic signal at 470 nm, indicating the potential to produce thermal and optical-mechanical devices. Mechanical properties obtained by nanoindentation resulted in an elastic modulus of ~251 GPa when measured in fully dense, contiguous crystalline regions, corresponding to an apparent, porous bulk stiffness of ~90 GPa for the final, 60.1 % dense products. Finally, the laser flash method (LFM) was used to measure the thermal conductivity of the material as a function of temperature resulting in values from 4.82 W/mK to 3.17 W/mK for the temperature range from 23 °C to 500 °C, respectively.     

Post-combustion of mazut with CO2 capture using aspen hysys

There is a wide range of resources for CO₂ emissions. The net amount of CO₂ emissions in the cement industry due to the consumption of fossil fuels and the chemical processes of cement production under heating raw materials is reported to be in the range of 15–25%; this industry, among all the industries and after the power plants and refineries, is the largest CO₂ gas producer throughout the world. Using CO₂ capture and storage (CCS), it can reduce greenhouse gas emissions in a short time. In this study, the technical feasibility study of recycling CO₂ in Abyek Cement Company, with a cement production capacity of about 12,500 tons per day in two production lines, has been studied as one of the largest cement industries throughout the world. Fuel oil (Mazut) is used as the primary fuel for furnaces in this industry. Affected by combustion, the emissions emitted from the five-stage preheater contain 5/24% vol% of CO₂, 7.6% H₂O, 4.8% vol% of O₂, and 63.1% N₂.     

Interlayer bonding between thermoplastic composites and metals by in-situ polymerization technique

Fiber-metal laminates (FMLs) offer the superior characteristics of polymer composites (i.e., light weight, high strength and stiffness) with the ductility and fracture strength of metals. The bond strength between the two dissimilar materials, composite and metal, dictates the properties and performance of the FMLs. The bonding becomes more critical when the polymer matrix is thermoplastic and hydrophobic in nature. This work employed a novel bonding technique between thermoplastic composites and a metal layer using six different combinations of organic coatings. The flexural, and interlaminar shear strength of the thermoplastic fiber metal laminates (TP-FMLs) were examined to investigate the bond strengths in the different cases along with fracture characteristics revealed from the tested samples using scanning electron microscopy. The viscoelastic performance of the fabricated TP-FMLs were also investigated using the dynamic mechanical thermal analysis method.     

Matching of technological forecasting technique to a technology using fuzzy multi-attribute decision-making methods: Case study from the aerospace industry

Given the accelerating pace of technological advances and environmental changes, technology-based companies are required to predict and understand future events in their environments. However, there is a wide range of forecasting methods creating confusion on which method to use. This paper demonstrates the selection of an appropriate technique for technology forecasting in the Iran Aviation Industries Organization (IAIO). To this end, a review of the literature was first reviewed to extract the proper criteria for selecting a forecasting method. Next, the SWARA and fuzzy MUTLIMOORA methods were used to evaluate and prioritize a total of twelve forecasting methods proposed for the case study. The results suggested that the Delphi method for technology forecasting in the IAIO. Scenario writing and the relevance tree are the next proper alternatives that can be used.     

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.