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The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

The purpose of this paper is to investigate the surface integrity features, including surface roughness (SR), recast layer (RL), material migration, topography and wire wear pattern in rough and trim-cut wire electric discharge machine (WEDM) of hybrid composite (Al6061-90%/SiC-2.5%/TiB₂-7.5%).

Effects of four important factors, namely, rough-cut history (RCH), pulse on time (T_on), peak current (IP) and wire offset (WO) have been assessed on the responses of interest for trim-cut WEDM. Box–Behnken design (RSM) was used to formulate the experimentation plan. Quantitative indices of surface integrity, namely, SR and RL, and selected samples have been investigated for qualitative analysis, namely, surface topography, material migration and wire wear pattern.

T_on and IP are found to be most significant, whereas RCH and WO are found insignificant for SR. T_on and WO were found to be the most significant factors affecting RL. After trim cut, an RL of thickness 8.26 µm is observed if the initial rough cut has been accomplished at high discharge energy setting. Whereas the best value of RL thickness, i.e. 5.36 µm, can be realized with low level of RCH. A significant decrease in the presence of foreign materials is recorded, indicating its strong correlation with the discharge energy used during machining.

Investigation on surface integrity features for machining of hybrid composite through rough and trim-cut WEDM has been reported by only a limited number of researchers in the past. This study is attempted at fulfilling few vital gaps by addressing the issues such as evaluation of the efficacy of trim cutting under different discharge energy conditions (using RCH), analysis of wire wear pattern in both rough and trim-cut modes and investigation of the wire breakage phenomenon during machining.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

This research examined the influence of the service attribute components of a smart automated coffee vending machine on the enjoyment and choice behaviour of customers from the perspective of the Stimulus-Organism-Response paradigm.

To gain an improved understanding of the influential factors that can yield the desired study outcomes, this research employed sufficiency logic and necessity logic to provide insights and practical implications for research.

While this study identified “special benefits” as a sufficient factor to induce both enjoyment and choice behaviour, “interactive experience” and “ease of use” were found to be the fundamental factors for achieving these two desirable outcomes.

This research extends beyond the conventional approach of symmetric analysis by incorporating necessary condition analysis to explore the essential conditions necessary for enjoyment and choice behaviours during automated-vending-machine consumption. The smart feature, highlighted by the ‘interactive experience,’ is revealed as one of the necessary factors in fostering enjoyment and influencing consumer choice of beverages from smart automated vending machines.

The purpose of this paper is to introduce and analyze a dual-side-permanent-magnet Halbach array vernier (DSPMHV) machine and to propose methods for achieving high torque density.

Flux harmonics and torque characteristics are analyzed by using finite element analysis. First, a suitable pole-slot combination is selected by comparison. Second, field modulation processes of DSPMHV machine are analyzed to identify the reason for high torque density. And it is compared with dual-side-PM (DSPM) machine to analyze flux harmonic and verify the flux concentrating effect of the Halbach array.

The permanent magnet (PM) field of the DSPM machine is approximately equal to the superposition of stator-PM field and rotor-PM field, which is the reason for high torque density. And the Halbach array can reduce flux leakage and increase the amplitude of main flux harmonics, then further improves torque. Improvement of torque can be achieved by choosing right pole-slot combination, adopting DSPM machine structure, reducing flux leakage and adopting field modulation principle.

The DSPMHV machine with split-tooth is proposed in this paper by combining the Halbach array with DSPM structure. This paper analyzes the bidirectional field modulation process, the reason for high torque density of the DSPM machine is obtained. Comparison with the DSPM machine verifies the flux concentrating effect of Halbach array. To alleviate the magnetic saturation in part of stator teeth, this paper proposes an improved DSPMHV machine with shaped auxiliary magnet.

Utilizing electrical discharge machining (EDM) to process micro-holes in superalloys may lead to the formation of remelting layers and micro-cracks on the machined surface. This work proposes a method of composite processing of EDM and ultrasonic vibration drilling for machining precision micro-holes in complex positions of superalloys.

A six-axis computer numerical control (CNC) machine tool was developed, whose software control system adopted a real-time control architecture that integrates electrical discharge and ultrasonic vibration drilling. Among them, the CNC system software was developed based on Windows + RTX architecture, which could process the real-time processing state received by the hardware terminal and adjust the processing state. Based on the SoC (System on Chip) technology, an architecture for a pulse generator was developed. The circuit of the pulse generator was designed and implemented. Additionally, a composite mechanical system was engineered for both drilling and EDM. Two sets of control boards were designed for the hardware terminal. One set was the EDM discharge control board, which detected the discharge state and provided the pulse waveform for turning on the transistor. The other was a relay control card based on STM32, which could meet the switch between EDM and ultrasonic vibration, and used the Modbus protocol to communicate with the machining control software.

The mechanical structure of the designed composite machine tool can effectively avoid interference between the EDM spindle and the drilling spindle. The removal rate of the remelting layer on 1.5 mm single crystal superalloys after composite processing can reach over 90%. The average processing time per millimeter was 55 s, and the measured inner surface roughness of the hole was less than 1.6 µm, which realized the  micro-hole machining without remelting layer, heat affected zone and micro-cracks in the single crystal superalloy.

The test results proved that the key techniques developed in this paper were suite for micro-hole machining of special materials.

This paper aims to deal with intra and inter-cell layout problems in cellular manufacturing systems. The model is organized to minimize the total handling cost, i.e. intra and inter-cell handling costs in a continuous environment.

The research was conducted by developing a mixed integer mathematical model. Due to the complexity and NP-hard nature of the cellular manufacturing layout problem, which mostly originated from binary variables, a “graph-pair” representation is used for every machine set and cells each of which manipulates the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm is embedded in the resulting linear programming model after graph-pair construction.

Various numerical examples in both small and large sizes are implemented to verify the efficiency of the linear programming embedded genetic algorithm.

Considering the machine and cell layout problem simultaneously within the shop floor under a static environment enabled managers to use this concept to develop the models with high efficiency.

To address the issues of the insufficient output torque associated with the application of intensifying-flux permanent magnet (PM) machines in electric vehicles, this paper aims to propose an intensifying-flux hybrid excitation PM machine. It is possible to adjust the air gap magnetic field by adjusting the field current in the excitation winding, thereby increasing the torque output capability and speed range of the machine.

First, a novel intensifying-flux hybrid excited permanent magnet synchronous machine (IF-HEPMSM) is proposed on the basis of intensifying-flux permanent magnet synchronous machine (IF-PMSM) and an equivalent magnetic circuit model is established. Second, the tooth width and yoke thickness of the machine stator are optimized to ensure the overload capacity of the machine while effectively improving the wide flux regulation range. Furthermore, the electromagnetic characteristics of the IF-HEPMSM are investigated and compared with the IF-PMSM and conventional permanent magnet synchronous machine (PMSM) by using finite element simulations.

The i_d of IF-HEPMSM and IF-PMSM is greater than zero low-speed magnetizing current. And the flux-weakening current of the IF-HEPMSM is 18% and 3% smaller than of the conventional PMSM and IF-PMSM.

Aiming at the problems of IF-PMSM applied to electric vehicles, this paper proposes an IF-HEPMSM. The air gap magnetic field is adjusted by controlling the current of the excitation winding to improve the reliability of the machine. Therefore, the IF-HEPMSM combines the advantages of high-power density and high efficiency of the PMSM and the controllable magnetic field of the electro-excitation machine, which is of great engineering value when applied in the field of electric vehicles.

The proposed IF-HEPMSM offers better flux regulation capability with electromagnetic characteristics analysis and maps of dq-axis current as compared to IF-PMSM and conventional PMSM. Moreover, the improvement of the torque can make up for the shortcomings of the insufficient torque output capability of the IF-PMSM.

This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction.

This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength.

The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC.

Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter.

The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants.

Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.

The purpose of this study is to reduce the cogging torque in axial flux permanent magnet (AFPM) machine using optimal magnet shape.

This study analyzes different magnet shapes for AFPM machine performance enhancement. Three-dimensional (3D) finite element analysis is performed to see the effects of pole shaping on the cogging torque of the AFPM machine.

The magnetic pole shape has a significant effect on cogging torque and overall efficiency. The conventional model has the highest torque whereas the conventional skewing affected cogging torque positively and significantly reduced the cogging torque. The combination of skewing the pole along with face curving is more effective and decreases the cogging torque from 3.88 Nm to 1.5 Nm.

Rare-earth magnets are the most expensive and important part of AFPM machines. Shape and volume optimization of rare-earth magnets is crucial for the performance of AFPM machines. The research aims to analyze the different permanent magnet designs for performance improvement of the AFPM machine. Conventional flat top trapezoidal, curved-top and skewed-magnet shapes are analyzed and the performance of the AFPM machine is compared with different magnet shapes. Curved-top shape and skewed magnet significantly reduce the cogging torque. Furthermore, a combination of curved-top shape and skew magnet shape is proposed to reduce the cogging torque further and improve the AFPM machine’s overall performance. Newly proposed magnet profile gives skewed curve magnet shapes which reduce the cogging torque further. 3D finite element analysis has been used to analyze the single-sided AFPM with all four different magnet shapes. The research focuses on single-sided AFPM machines, but the results are also valid for double-sided AFPM machines and can be extended to other topologies of AFPM machines.