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Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.

As manufacturing companies are becoming more global, dynamic, and competitive, contradictory demands intensify. Flexibility is a key enabler for meeting the challenges of a global market if offered at mass production price and quality. Many companies have adopted Flexible Manufacturing System (FMS) together with new technologies. Nevertheless, despite the drastic increase in industrial robots adoption, industrial robot applications continue today as they were designed 50 years ago. To obtain a flexible and reliable production system, it takes more than technology as quality depends on equipment and manufacturing processes. Non-adaptive industrial robots autonomy may be disrupted by the geometrical deformations of the fixtures. This paper presents a comprehensive case study of adopting a robotic in-line quality inspection in an automotive Original Equipment Manufacturer (OEM) to aid the robot-fixture collision problem. The purpose is to examine errors that occur in production processes and how quality inspection can mitigate such errors. Empirical data collection was carried out in the form of (i) interviews, (ii) participant observations, (iii) documents, and (iv) video recording of robot cells. Results show that contrary to the case company beliefs, the manufacturing system does not follow the FMS standards; thus, to harmonise resources design and manufacturing processes, adding a robotic in-line quality inspection station is not enough. First, the robotic in-line quality inspection should follow a “preventive” control strategy to avoid deviated fixturing from entering the robot line. Second, the managers should address the beliefs of operators and their activities in solving the robot-fixture collision problem. Moreover third, the robot gripper design needs to be updated to an appropriate one.

In the realm of Industry 4.0, measurement systems play an important role in adapting industrial robots to dynamic environments. Real-time control techniques such as Measurement Assisted Assembly (MAA) can exploit the digital measurements for operation process corrections. Likewise, the propagation of defects can be avoided with in-line measurement conditions. The purpose of this paper is to first understand the capability of robotic in-line measurement assisted assembly in the industrial case of peg-in-hole assembly and second, record the encountered challenges and their enablers. A proof of concept - formed by two 6DoF industrial robots, an in-line Linear Laser (LL), and an on-machine force sensor - have been designed and tested in a lab environment. The experimental results show that robotic in-line measurement assisted assembly can be performed within the tight tolerances of (i) 0, 071 ° to 0, 154° angular deviation between X and Y axes, (ii) applying minimum (near 0) Newton forces in X and Y axes when performing the peg-in-hole robotic assembly of two parts with only 50μm clearance, and (iii) within the company's cycle time. Further, for the effectiveness and practicality of robotic measurement assisted assembly systems, we recorded the encountered challenges and key enablers. 

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

The Zero-Defect Manufacturing (ZDM) movement has received increasing interest from practitioners and academics. However, despite the academic development of the field, the adoption of ZDM enablers such as robotic in-line quality inspection applications has not increased as expected. This article explores the state of adoption of robotic in-line quality inspection at five global Swedish manufacturing companies. Results show that contrary to the case companies' beliefs, more people- and process-oriented challenges have been encountered compared with technological ones. Future work will focus on developing system design guidelines for robotic in-line quality inspection systems in the realm of ZDM.