As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

/pdf/digital-twin-based-sustainable-intelligent-manufacturing-a-32gjodfp5s.pdf

Digital twin-based sustainable intelligent manufacturing: a review

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

https://www.mdpi.com/1424-8220/19/14/3170/pdf

Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network.

Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time

Product carbon footprint across sustainable supply chain

Sustainable port cities with coupling coordination and environmental efficiency

Underactuated robotics is an emerging research direction in the field of robotics. The control input of the underactuated robot is less than the degree of freedom of the system. It has the advantag...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419862164

Yongjia Liu

Papers

Underactuated robotics: A review:

Product carbon footprint across sustainable supply chain