陕西国防工业职业技术学院（Shaanxi Institute of Technology）简称陕西国防学院，坐落于世界历史文化名城西安近郊的户县，是一所由陕西省人民政府举办的全日制公办普通高校。1958年建校以来，学院先后隶属于原第五机械工业部、中华人民共和国兵器工业部、中国兵器工业总公司，2001年划转到陕西省人民政府。学院立足陕西，面向全国，为国家国防军工企业培养和输送了数万名高级技能技术型人才。2017年8月，学院入选为教育部第二批现代学徒制试点单位。学院为“国家示范性高等职业院校建设计划”骨干高职院校首批立项建设单位、国家国防科技工业高职教育实训基地、中央财政支持建设实训基地、全国数控技术人才培训基地、全国紧缺人才培训基地、教育部高职高专人才培养工作水平评估优秀院校，陕西省六所具有高职院校自主招生资格单位之一。学院有南北两个校区，校园面积1003亩、建筑面积31.1万平方米，固定资产总值4亿元，校内教学设备仪器数量达到了5825台(套)，总值8197万元。设有13个教学院部，开设化工、机械、电子和电气等8类48高职个专业，各类在校学生12000人，教职工602人。（2015年12月数据）2019年12月，被教育部、财政部列入第三类高水平专业群建设单位（B档）。
We propose a unified, training-free dynamic comfort model that simultaneously accounts for individual characteristics, group dynamics and scenario context for safe and comfortable robot navigation in dense crowds. First, a posture-driven, fuzzy-tuned asymmetric Gaussian framework reshapes personal comfort space in real time without prior training, using only gender, age and skeleton pose. Second, a speed-social rules coupling mechanism integrates robot velocity, task urgency and proxemic conventions (left-pass, hand-over, narrow-pass priority) into an analytic factor, eliminating the high-speed/low-intrusion paradox. Third, by embedding the online-estimated Common Attention Region (CAR) into an Extended Gaussian Mixture Model (EGMM), we derive a training-free, continuous potential that jointly models individual-group-robot proxemics for corridor passage, handover and overtaking tasks, and is drop-in compatible with $\mathbf{A}^{*}$, RRT, DWA and MPC planners. On real crowd datasets using A*, the proposed costmap increases individual comfort score by 47.2% and group comfort score by 41.7% while reducing comfort-constraint violations and collision probability by $\mathbf{1 5. 6 {\%}}$, validating socially compliant navigation in dense human-robot shared environments.
Digital twin-based real-time quality control in gear machining lacks closed-loop feedback, classification without visual recognition, and low-latency I/O synchronization. This paper proposes a digital twin-driven intelligent quality control framework built upon a four-layer cyber–physical architecture comprising three tightly coupled subsystems: a parent-object assignment mechanism that enables precise workpiece type tracking and routing through hierarchical container queries, eliminating the need for computationally expensive visual type classification while maintaining near-perfect tracking accuracy; a rhythm-adaptive multi-robot behavioral control scheme that adjusts production cadence via a global rhythm coefficient without altering spatial trajectories; and a lightweight in-memory key-value store-based I/O synchronization mechanism that achieves architecturally bounded signal update latency (communication over a dedicated localhost TCP/IP path) well below the critical process cycle. Validation on a physical gear hub production line with six operations and three robots demonstrates a single-piece inspection cycle of 11.5 s, collision-free operation, and average error rates of 0.4% for inline quality inspection. The framework provides a low-cost, low-latency, and formally grounded solution for real-time quality control in gear machining workshops.
Abstract This study investigates the machining process of the wedge block, a critical component in aluminum foil winding devices. To address the issues associated with traditional methods—such as the cumbersome setup, low efficiency, and high energy consumption of the process lap method and the blanking method—an innovative special Fixture Method based on robotic automation is proposed. This approach utilizes a designed 90° right-angle inclined groove positioning module to achieve form-based positioning and rapid clamping of the workpiece. By integrating robotic handling and blue-light scanning inspection, an automated machining unit is established. A small-batch comparative experiment (20 samples per method) was conducted to systematically evaluate the performance of the three processes in terms of machining time, dimensional accuracy, surface roughness, and energy consumption. The results indicate that the automated special Fixture Method reduces the average single-piece processing time to only 21.3 minutes, representing a reduction of approximately 62.7% compared to the process lap method (57 minutes) and 36.6% compared to the blanking method (33 minutes). Both key dimensional tolerances and surface roughness meet engineering requirements, while energy consumption is reduced by 29.8% relative to the process lap method. The study demonstrates that the proposed automated special Fixture Method significantly enhances the efficiency and sustainability of small-batch production of complex-shaped parts without compromising machining quality, providing an effective solution for process optimization of similar components.
