Nanofluid/ultrasonic atomization minimal quantity lubrication (MQL) was used in an SKD11 steel micro-milling process with ultrasonic dispersion. The nanofluid atomizer substantially improved nanofluid reunification and delivery to the cutting zone. The micro-milling cutting force and temperature were used to calculate the signal-to-noise ratio (S/N). Normalization of the S/N in a grey relational analysis obtained a grey correlation coefficient, which was used in a fuzzy analysis to obtain a multiple performance characteristic indexes (MPCI). The MPCI was maximized by optimizing the process parameters for multiple quality characteristics and then comparing the optimized parameters with those in the MQL system. Experimental results indicate that the nanofluid parameters containing the density of nanofluid, feed rate, and distance of nozzle are the most influential control factors, while 3.617 Newton and 117.1 degrees Celsius, respectively, are the optimal combination of parameters. Different lubricating methods were also compared in terms of effects on micro-milling cutting force, micro-milling temperature, micro-milling tool wear, and surface burr. Experiments showed that nanofluid (MWCNTs)/ultrasonic atomization MQL obtained the best results.

Background: Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images. Results: A Resnet101-9 ensemble model was developed for classifying ALL in microscopic images. The proposed Resnet101-9 ensemble model combined the use of the nine trained Resnet-101 models with a majority voting strategy. Each trained Resnet-101 model integrated the well-known pre-trained Resnet-101 model and its algorithm hyperparameters by using transfer learning method to classify ALL in microscopic images. The best combination of algorithm hyperparameters for the pre-trained Resnet-101 model was determined by Taguchi experimental method. The microscopic images used for training of the pre-trained Resnet-101 model and for performance tests of the trained Resnet-101 model were obtained from the C-NMC dataset. In experimental tests of performance, the Resnet101-9 ensemble model achieved an accuracy of 85.11% and an F₁-score of 88.94 in classifying ALL in microscopic images. The accuracy of the Resnet101-9 ensemble model was superior to that of the nine trained Resnet-101 individual models. All other performance measures (i.e., precision, recall, and specificity) for the Resnet101-9 ensemble model exceeded those for the nine trained Resnet-101 individual models. Conclusion: Compared to the nine trained Resnet-101 individual models, the Resnet101-9 ensemble model had superior accuracy in classifying ALL in microscopic images obtained from the C-NMC dataset.

This paper presents a multiobjective evolutionary approach that can solve integrated airline scheduling and rescheduling problems under conditions of disruption. The integrated problem simultaneously considers both aircraft routing and crew pairing to meet several objectives under real-world constraints and disturbance events. Because of their high complexity, we formulated integrated problems as combinational optimization problems and used the NSGA-II variant method combined with a repair strategy as the solver. To verify and validate the proposed approach, real-world flight data were used to build study cases. In the experiment, we first studied the convergence of the algorithm by using the repair method. We then reviewed real-world plans and evaluated the improvement obtained using the proposed integrated approach. Finally, a disruption was simulated to study rescheduling capability. Experimental results showed that the proposed approach yields better schedules than real-world expert-made plans and that Pareto solutions after the disruption can, under safety and legal constraints, be successfully explored in rescheduling problems.

This study solved the robotic task sequence planning problem of scheduling joint-space tours so that each task point visit is made according to expected manufacturing criteria. Multiple solutions for robotic inverse kinematic (RIK) problems were obtained for two task sequencing problems: 1) preset manufacturing sequence planning (Preset-MSP) and 2) optimal manufacturing sequence planning (Opt-MSP). First, a real-coded twin-space crowding evolutionary algorithm (TC-EA) was developed and used to explore multiple RIK joint configurations for each task point. Then, a heuristic bidirectional reference mechanism (BRM) was developed and used for efficiently solving Preset-MSP problems. By integrating BRM, the proposed discrete-coded TC-EA also efficiently solved Opt-MSP problems. To validate the proposed approach, multiple multimodal benchmark functions and task sequencing test cases were used to compare the solving capability of the proposed TC-EA and other evolutionary multimodal solvers. The experimental results showed that the proposed methods obtained better or at least comparable solutions for all test problems. For decision makers, the proposed methods have practical applications for exploring and comparing multiple robotic manufacturing plans.

A genetic algorithm and Taguchi method were used to optimize parameters for the residual-current device (RCD) snubber circuit of a DC-DC flyback converter. The most suitable algorithm was determined by using test functions to compare performance in three multi-objective optimization methods: non-dominated sort genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and multi-objective differential evolution algorithm (MODE). Comparisons of coverage rate, distance between non-dominant solutions, and maximum walking distance showed that NSGA-II was superior to both MOPSO and MODE. Therefore, NSGA-II was used to obtain parameter values for the RCD snubber circuit. However, practical application of the parameter values was limited because the values could not meet the specifications required for real-world circuits. Thus, the parameter values obtained by NSGA-II were used in further factor-level experiments performed by Taguchi method. The experimental results indicated that, compared to previous design methods, the proposed NSGA-II and Taguchi method obtains better parameter values for the RCD snubber circuit.