The research trend in additive manufacturing (AM) has evolved over the past 30 years, from patents, advances in the design, and layer-by-layer materials, to technologies. However, this evolution is faced with some barriers, such as the implementation of additive manufacturing (AM) in operations, its productivity limitations, and economic and social sustainability. These barriers need to be overcome in order to realize the full potential of AM. The objective of this study is to analyze the bibliometric data on these barriers through a systematic review in two study areas: business model innovation and sustainability in AM from Industry 4.0 perspective. Using the most common keywords in these two study areas, we performed a search on the Web of Science (WoS) and Scopus databases and filtered the results using some inclusion and exclusion criteria. A bibliometric analysis was performed for authorship productivity, journals, the most common keywords, and the identified research clusters in the study areas. For the bibliometric analysis, the BIBEXCEL software was used to extract the relevant information, and Bibliometrix was used to determine the research trend over the past few years. Finally, a literature review was performed to identify future trends in the study areas. The analysis showed evidence of the relationship between the study areas from a bibliometric perspective and areas related to AM as an enabler for Industry 4.0.

Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future.

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters.