In a demand context of mass customization, shifting towards the mass personalization of products, assembly operations face the trade-off between highly productive automated systems and flexible manual operators. Novel digital technologies—conceptualized as Industry 4.0—suggest the possibility of simultaneously achieving superior productivity and flexibility. This article aims to address how Industry 4.0 technologies could improve the productivity, flexibility and quality of assembly operations. A systematic literature review was carried out, including 234 peer-reviewed articles from 2010–2020. As a result, the analysis was structured addressing four sets of research questions regarding (1) assembly for mass customization; (2) Industry 4.0 and performance evaluation; (3) Lean production as a starting point for smart factories, and (4) the implications of Industry 4.0 for people in assembly operations. It was found that mass customization brings great complexity that needs to be addressed at different levels from a holistic point of view; that Industry 4.0 offers powerful tools to achieve superior productivity and flexibility in assembly; that Lean is a great starting point for implementing such changes; and that people need to be considered central to Assembly 4.0. Developing methodologies for implementing Industry 4.0 to achieve specific business goals remains an open research topic.

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.

Nowadays, errors during the manufacturing process of high value components are not acceptable in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power need complex and accurate components that demand close measurements and fast feedback into their manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They provide the possibility to measure the workpiece in-machine during or after its manufacture, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable enough for process control or product validation. The scientific objective is to determine the uncertainty on a machine tool measurement and, therefore, convert it into a machine integrated traceable measuring process. For that purpose, an error budget should consider error sources such as the machine tools, components under measurement and the interactions between both of them. This paper reviews all those uncertainty sources, being mainly focused on those related to the machine tool, either on the process of geometric error assessment of the machine or on the technology employed to probe the measurand.