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This paper aims to develop an uncertain global supply chain network design (GSCND) model with rules of origin (RoOs) and limited import quotas, and to discuss the international factors’ effects on location decisions.

The authors establish an uncertain GSCND model with the international factors. The transportation costs and customers’ demands are characterized as random variables. To deal with the risk of uncertainty, the authors introduce the customers’ demand service level. A sample approximation approach (SAA) is used to deal with the service level constraint and turn the proposed model into a mixed integer programming. On the basis of the properties of the proposed model, a hybrid memetic algorithm (MA) is designed to solve it.

The authors find that the proposed MA is efficient to the real supply chain network design problem. Besides, the RoOs and limited import quotas can affect the optimal choices of plant and distribution center locations.

The authors propose an uncertain GSCND model with RoOs and limited import quotas. An MA with SAA is designed to solve the proposed model. The authors apply the proposed model into a real global supply chain of an apparel corporation in East Asia, and give some managerial insights.

Orientation determination is an essential planning task in additive manufacturing (AM) because it directly affects the part quality, build time, geometric tolerance, fabrication cost, etc. This paper aims to propose a negative feedback decision-making (NFDM) model to realize the personalized design of part orientation in AM process.

NFDM model is constructed by integrating two sub-models: proportional–integral–derivative (PID) negative feedback control model and technique for order preference by similarity to an ideal solution (TOPSIS) decision-making model. With NFDM model, a desired target is first specified by the user. Then, the TOPSIS decision model calculates the “score” for the current part orientation. TOPSIS decision model is modified for ease of control. Finally, the PID controller automatically rotates the part based on the error between the user-specified target and the calculated “score”. Part orientation adjustment is completed when the error is eliminated. Five factors are considered in NFDM model, namely, surface roughness, support structure volume, geometric tolerance, build time and fabrication cost.

The case studies of turbine fan and dragon head indicate that the TOPSIS model can be perfectly integrated with the PID controller. This work extends the proposed model to different AM processes and investigates the feasibility of combining different decision-making models with PID controller and the effects of including various evaluation criteria in the integrated model.

The proposed model innovatively takes the TOPSIS decision-making model and the PID control model as a whole. In this way, the uncontrollable TOPSIS model becomes controllable, so the proposed model can control the TOPSIS model to achieve the user-specified targets.

The specific iron losses of amorphous alloy material are extremely low compared with silicon steel material. The iron losses of motors may reduce by replacing the silicon steel core with an amorphous alloy core. However, one drawback of amorphous alloy material is that the specific iron losses will increase a lot after the motor manufacturing process. This paper aims to study the influences of interlaminar insulator solidifying and annealing on amorphous alloy material. The iron losses of motors made of amorphous alloy and baseline silicon steel sheets are compared and discussed.

This paper opted for an exploratory study using the experimental analysis and loss separation methods. Two amorphous alloy cores are produced and tested. The iron losses of motors made of amorphous alloy and silicon steel sheets are calculated and compared based on the measured specific iron losses. Three wound amorphous alloy core samples are made and measured. The iron losses are separated and compared by considering the manufacturing influences.

This paper provides empirical insights about what change is brought in amorphous alloy material after manufacturing. The results have shown that, for amorphous alloy cores without the annealing process, the loss increase caused by solidifying is mainly the eddy current loss, while it is mainly the hysteresis loss component for annealed amorphous alloy cores.

This paper presents for the first time the measured results of manufactured amorphous alloy cores. This paper fulfils the need to manufacture amorphous alloy motors properly for the producers.

The purpose of this paper is to investigate the effects of parameters on the liquid phase migration (LPM) during the freeze-form extrusion fabrication (FEF) process.

To carry out this study, three factors were systematically investigated using orthogonal design of experiments. These three parameters are the extrusion velocity, the extrusion interval time and the extrusion head length. An orthogonal array with nine test units was selected for the experiments. Range analysis and analysis of variance were used to analyze the data obtained by the orthogonal experiments to identify the order of significant factors on LPM.

It was found that the LPM decreased with the increase of extrusion velocity and increased with the lengthening of extrusion interval time and the length of the extrusion nozzle. The order of significant factors for the LPM were found to be extrusion velocity > extrusion nozzle length > extrusion interval time.

Using an orthogonal design of experiments and a statistical analysis method, the liquid content of extrudate can be predicted and appropriate process parameter values can be selected. This leads to the minimization of LPM during the FEF process. Also, this analysis method could be used to study the LPM in other paste extrusion processes.

This paper suggests that the factors have significant impact on LPM during FEF process. The following analysis in this paper is useful for FEF users when prediction of LPM is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other FEF-type processes. The research can also help to get better understanding of LPM during the FEF process.