The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low‐cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human–machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist‐designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.

Superhydrophobic surfaces have shown versatile applications in waterproofing, self-cleaning, drag reduction, selective absorption, etc. The most convenient and universally applicable approach to forming superhydrophobic surfaces is by coating; however, currently, superhydrophobic, smart coatings with flexibility and multiple functions for wearable sensing electronics are not yet reported. Here, a highly flexible multifunctional smart coating is fabricated by spray-coating multiwalled carbon nanotubes dispersed in a thermoplastic elastomer solution, followed by treatment with ethanol. The coatings not only endow various substrate materials with superhydrophobic surfaces, but can also respond to stretching, bending, and torsion-a property useful for flexible sensor applications. The coatings show superior sensitivity (gauge factor of 5.4-80), high resolution (1° of bending), a fast response time (<8 ms), a stable response over 5000 stretching-relaxing cycles, and wide sensing ranges (stretching: over 76%, bending: 0°-140°, torsion: 0-350 rad m ). Moreover, multifunctional coatings with thicknesses of only 1 µm can be directly applied to clothing for full-range and real-time detection of human motions, which also show extreme repellency to water, acid, and alkali, which helps the sensors to work under wet and corrosive conditions.

A highly flexible porous ionic membrane (PIM) is fabricated from a polyvinyl alcohol/KOH polymer gel electrolyte, showing well‐defined 3D porous structure. The conductance of the PIM changes more than 70 times as the relative humidity (RH) increases from 10.89% to 81.75% with fast and reversible response at room temperature. In addition, the PIM‐based sensor is insensitive to temperature (0–95 °C) and pressure (0–6.8 kPa) change, which indicates that it can be used as highly selective flexible humidity sensor. A noncontact switch system containing PIM‐based sensor is assembled, and results show that the switch responds favorably to RH change caused by an approaching finger. Moreover, an attachable smart label using PIM‐based sensor is explored to measure the water contents of human skin, which shows a great linear relationship between the sensitivity of the sensor and the facial water contents measured by a commercial reference device.

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As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.

Product-service system (PSS) is an effective solution for service-oriented manufacturing. In the life cycle of PSS, evaluation decision of PSS alternatives is of great significance for subsequent implementation. Supported by the big data of stakeholder comments, a PSS evaluation decision technique is explored. Based on the multi-stakeholder comments of PSS evaluation decision's influence factors, the index system considering the environmental effect is constructed through analyzing and summarizing the co-occurrence matrix and semantic network diagram of high-frequency words. To determine the index value of PSS alternative, the stakeholders' vague opinions expressed by trapezoidal fuzzy number are fused. At last, PSS alternatives are evaluated by Kullback-Leibler divergence (KLD) modified TOPSIS. The case of PSS evaluation decision for a printer company shows that the explored technique is effective.

The design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently. Environmental and social factors should be systematically integrated into the life cycle of intelligent manufacturing. In view of this, a green performance evaluation methodology of intelligent manufacturing driven by digital twin is proposed in this paper. Digital twin framework, which constructs the bidirectional mapping and real-time data interaction between physical entity and digital model, provides the green performance evaluation with a total factor virtual image of the whole life cycle to meet the monitoring and simulation requirements of the evaluation information source and demand. Driven by the digital twin framework, a novel hybrid MCDM model based on fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II is proposed as the methodology for the green performance evaluation of intelligent manufacturing. The model is tested and validated on a study of the green performance evaluation of remote operation and maintenance service project evaluation for an air conditioning enterprise. Testing demonstrates that the proposed hybrid model driven by digital twin can enable a stable and reasonable evaluation result. A sensitivity analysis was carried out by means of 27 scenarios, the results of which showed a high degree of stability.

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