Context—The maintenance of aero engines is intricate, time-consuming, costly and has significant functional and safety implications. Engine blades and vanes are the most rejected parts during engine maintenance. Consequently, there is an ongoing need for more effective and efficient inspection processes. Purpose—This paper defines engine blade defects, assigns root-causes, shows causal links and cascade effects and provides a taxonomy system. Approach—Defect types were identified from the literature and maintenance manuals, categorisations were devised and an ontology was created. Results—Defect was categorised into Surface Damage, Wear, Material Separation and Material Deformation. A second categorisation identified potential causes of Impact, Environmental causes, Operational causes, Poor maintenance, Poor manufacturing and Fatigue. These two categorisations were integrated with an ontology. Originality—The work provides a single comprehensive illustrated list of engine blade defects, and a standardised defect terminology, which currently does not exist in the aviation industry. It proposes a taxonomy for both engine blade defects and root-causes, and shows that these may be related using an ontology.

Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface.

Purpose: Lean principles are applied in manufacturing and service industries extensively, but its successful application in the printing industry has been poor. The purpose of this paper is to explore the factors that impede the uptake of lean in the printing industry. Design/methodology/approach: Qualitative case study research methods were used to explore success factors and barriers to implementing lean in printing by interviewing two managers and three employees at each of two printing firms that had implemented lean to different degrees. Findings: A number of critical success factors and barriers to implementing lean are identified. Success factors include practitioners’ understanding of lean philosophy, principles and methods, management leadership and commitment, upfront training in lean and effective communication. Originality/value: The work contributes to empirical research on implementing lean in the printing industry. This research identified critical success and failure factors in implementing lean in this industry. Management knowledge, commitment, and support emerged as key factors. A theoretical framework is provided that proposes relationships of causality for the successful implementation of lean in this industry.