Multi-sensor measurement and data fusion technology for manufacturing process monitoring: a literature review

Abstract

Due to the rapid development of precision manufacturing technology, much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness, thereby improving the efficiency and precision of manufacturing. In a multisensor system, each sensor independently measures certain parameters. Then, the system uses a relevant signal-processing algorithm to combine all of the independent measurements into a comprehensive set of measurement results. The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems. The architecture of multisensor measurement systems is reviewed, and some implementations in manufacturing systems are presented. In addition to the multisensor measurement system, related data fusion methods and algorithms are summarized. Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

“…Literature [15] and so on generate a series of candidate samples through particle filter or sliding window and score these candidate samples to get the final tracking result. Literature [16] uses a sliding window to obtain a series of candidate samples and then uses a convolutional neural network to evaluate the maximum likelihood estimation of the samples to obtain the final tracking result. Literature [17] obtained candidate samples by scattering particles and obtained the scores of these particle samples through the twin network, and the final tracking result was the highest among the particle scores.…”

Force-Vision Sensor Fusion Improves Learning-Based Approach for Self-Closing Door Pulling

“…It has been widely studied in object recognition and autonomous navigation, and it has boarder applications in Internet of Things (IoT), automotives, drones, computer vision, virtual reality, and healthcare domains due to its advantages of richer semantic and higher resolution on observation, better confidence in certainty and accuracy of the data, and more comprehensive knowledge of the environment [11]. There are three fundamental ways of fusing sensor data: 1) complementary: combining the data from each sensor which provides data about different aspects or attributes of the environment; 2) competitive: fusing the data from several sensors which measure the same or similar attributes of the environment; 3) cooperative: deriving information of the environment from the data from two or more independent sensors in the system [12], [18]. Dasarathy et al classified sensor fusion architectures into three levels depending on the input/output characteristics [19], namely data-level fusion, feature-level fusion, and decision-level fusion.…”

“…In addition, combining the data from vision and force sensors will result in a more confident decision on the robot's motion control, just like the fact that a human relies not only on vision but also on hand sensation of force and touch when opening a door. Such a combination will benefit the robot control in terms of both safety and performance, because multiple sensors with data fusion technology can not only extract more features of the environment but also enhance understanding of the sensed environment [11], [12]. Based on this thinking, we propose a Deep Neural Network (DNN)-based force-vision sensor fusion method for enhancing a wheeled vehicle's learning to pull a self-closing door without using a general-purpose robotic arm.…”

A Review of Post-Processing Technologies in Additive Manufacturing

“…The layer-by-layer forming technology used in the manufacture of the material releases the forming stress when each layer condenses into its final form. Certainly, there are many other advantages of AM technology, such as the ability to achieve a variety of multi-material composite manufacturing [33][34][35], high processing efficiency, and fabrication of various complex structures [36][37][38][39][40][41][42][43][44].…”