Tungsten alloys are widely used in aerospace, military defense, the nuclear industry, medical applications, and other critical manufacturing fields due to their exceptional physical and chemical properties, including corrosion resistance, high hardness, high boiling point, low vapor pressure, and high strength. As the demand for tungsten alloys grows, there is a need for improved accuracy, quality, and surface integrity in their manufacturing and processing. Conventional Grinding (CG) methods for tungsten alloys faced challenges such as fast tool wear, brittle fracture, high grinding force, and high temperature. Rotary Ultrasonic Grinding (RUG) was developed to overcome these limitations, combining traditional and ultrasonic machining to enhance Material Removal Rates (MRR). This research involved grinding of tungsten alloy (W-Ni-Fe) simulations in Abaqus software and experiments on CNC machines, comparing RUG and CG carried out by Taguchi experimental design method to study the influences of spindle speed, grinding depth, feed rate, ultrasonic frequency, and amplitude on the grinding force generated in grinding of tungsten alloy. Results indicated that RUG reduces grinding force compared to CG. Increased grinding depth and feed rate raised the grinding force, while the grinding force was reduced by increasing ultrasonic amplitude. Finally, for optimal grinding force reduction in processing, a smaller grinding depth and feed rate, higher spindle speed, moderate ultrasonic frequency, and amplitude should be selected.

The leading enterprise-dominated collaborative production mode has become a major trend in large-scale manufacturing, which poses significant challenges to cross-enterprise resource kitting and efficient production collaboration among multiple suppliers. However, for kitting, current research mainly focuses on in-house material assembly kitting, largely overlooking cross-enterprise order production kitting. Traditional order reallocation methods often ignore kitting characteristics, thereby hindering dynamic and sustainable collaboration. To address these gaps, this study proposes a novel kitting-oriented collaborative order reallocation method. This method integrates kitting with order reallocation techniques, characterizes the large-scale collaborative production mode, and establishes a dynamic collaborative control framework. A kitting-oriented multi-objective order reallocation model is then developed, which explicitly incorporates kitting and cost objectives to balance economic efficiency with supply chain stability. An improved Non-Dominated Sorting Genetic Algorithm-II with Repair Mechanism (NSGA-II_RM) is designed, featuring novel chromosome repair and initial population generation operations. Finally, case studies and comprehensive comparative analyses are conducted to validate the feasibility and effectiveness of the proposed method. The results demonstrate its great potential in addressing dynamic cross-enterprise order reallocation and resource kitting problems, while balancing solution quality, computational efficiency, and scalability.