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.

Additive manufacturing (AM) technology has rapidly evolved with research advances related to AM processes, materials, and designs. The advantages of AM over conventional techniques include an augmented capability to produce parts with complex geometries, operational flexibility, and reduced production time. However, AM processes also face critical issues, such as poor surface quality and inadequate mechanical properties. Therefore, several post-processing technologies are applied to improve the surface quality of the additively manufactured parts. This work aims to document post-processing technologies and their applications concerning different AM processes. Various types of post-process treatments are reviewed and their integrations with AM process are discussed.

Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.

The additive manufacturing (AM) technique has received considerable industrial attention, as it is capable of producing complex functional parts in the aerospace and defense industry. Selective laser melting (SLM) technology is a relatively mature AM process that can manufacture complex structures both directly and efficiently. However, the quality of SLM parts is affected by many factors, resulting in a lack of repeatability and stability of this method. Therefore, several common and advanced in situ monitoring as well as defect detection methods are utilized to improve the quality and stability of SLM processes. This article aims at documenting the various defects that occurred in SLM processes and their influences on the final parts. Various types of in situ monitoring and defect detection methods and their applications are reviewed, and their integrations with the SLM processes are also discussed.