### Quality 4.0  
Besides cost, time and flexibility, quality is one crucial dimension of manufacturing attributes in terms of products and processes [19]. Its objective is to assure that the service or final product meets the specifications and satisfies the customers' requirements. Total quality management (TQM) is the current highest level of quality in an organization context, which holistically considers internal and external customers' needs, cost of quality and system development to organize and assist quality improvement. Quality control (QC) is a part of TQM, playing an essential role in fulfilling technical specifications with inspection applying techniques such as statistical process control (SPS), which is statistical sampling to manage the in-line quality on the shop-floor manufacturing level [20]. Contrastingly, Quality assurance (QA) concentrates more on the pre-manufacturing phases, such as planning, design, prototyping, etc., to ensure the achievement of quality requirements for manufacturing products. The international standard ISO 9001:2015 describes the specific standards of the quality management system [21]. Many organizations have employed various methods and approaches to improve quality performance such as TQM, Lean Six-Sigma, Failure mode and effect analysis (FMEA), quality function deployment (QFD) and benchmarking [22]. Furthermore, certain behaviors in the factories, such as process management, customer focus, involvement in the quality of supply and small group activity, are required for the successful application of quality management [23]. Industry 4.0 is a new industrial digitization paradigm that may be seen at all levels of modern industry. Quality 4.0 can be considered an integral part of Industry 4.0 when the status of quality and industry 4.0 are combined. It is the digitalization of TQM or the application of Industry 4.0 technology to improve quality. The value propositions for Quality 4.0 include the augmentation or improvement of human intelligence; the enhancement of productivity and quality for decision-making; the improvement of transparency and traceability; human-centered learning; change prediction and management [24; 25; 26]. In a holistic view of a smart factory, Big Data, in conjunction with CPS, can be applied to manage the data understanding. Big data analytics play a critical role in supporting early failure detection during the manufacturing process, providing valuable insight into factory management such as productivity enhancement [27]. IoT provides a superior global vision for the industrial network (including intelligent sensors and humans) as well as the ability to take real-time actions based on data comprehension [28]. Then it comes to AI, which is currently used to perform visual inspections of products towards quality control evaluation. One of the most critical issues in manufacturing is the ability to visually assess product quality [29]. AI methods (Machine learning techniques) proved their advancement in assisting inspections based on data analysis. This latter is frequently taken as images collected from sensors/cameras inside manufacturing environments. Finally, AR technologies can be applied to facilitate the inspection process with an immersive experience by superimposing digital information onto the working environment [30]. At the time of conducting this study, most enabling technologies of Industry 4.0, especially AR technology, have reached a mature point that could enhance the transformation of quality 4.0. This means that a systematic literature review about AR applied in the quality sector is essential and crucial not only for the digital transformation in quality 4.0 but also for the long-term integration of AR technology in quality sector. All relevant AR-assisted quality control solutions in the manufacturing context are considered for this SLR to observe how the cutting edge AR technology has been applied and evolved in quality sector.