A systematic review of additive manufacturing-based remanufacturing techniques for component repair and restoration

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

https://www.mdpi.com/1424-8220/23/13/5970/pdf?version=1687940310

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Recently, the commercialization of hybrid machine tools that combine directed energy deposition (DED) additive manufacturing (AM) technology and subtractive manufacturing (SM) has rapidly progressed. This technology trend has been developed to meet the market demand for processing quality (precision) and productivity, and to produce products such as lightweight parts, and complex and special functional parts, as well as to facilitate mold production and part repair. Compared to SM technology, metal AM technology has limitations in terms of surface quality and shape accuracy. Therefore, post-processing is necessary for the AM output. Laser-assisted machining (LAM) is an innovative hybrid technique in which surface quality and productivity can be improved by enhancing the machinability of difficult-to-cut materials. LAM studies have mainly been performed on titanium alloys, nickel based alloys and ceramic materials. However, except for ceramics, no high-strength material studies have been conducted to analyze the LAM process machining characteristics of workpieces fabricated by AM process. Therefore, in this study, LAM is applied to post-processing of output fabricated by AM of Ti-6Al-4V. DED device was developed using metal powder feeder and a laser. The Ti-6Al-4V workpieces were successfully fabricated through many tests. The cutting depth for LAM was selected through thermal analysis for LAM of the fabricated workpiece. Compared to the case of traditional machining without preheating, machining characteristics in LAM experiments were analyzed and property testing was performed.

Laser-Assisted Machining of Ti-6Al-4V Fabricated by DED Additive Manufacturing

Structural integrity issues of additively manufactured railway components: Progress and challenges

Neural network-based build time estimation for additive manufacturing: a performance comparison

Development of Preprocessor for the Exchange of Equipment and Materials’ Specifications Sheet Data Based on ISO 15926

Development of A Postprocessor for Exchanging of Equipment and Materials Specification-sheet Data Based on ISO 15926

Additive manufacturing via 3D printing can enable the efficient and cost-effective replacement of damaged parts that can be produced at any manufacturing location and even when the parts are discontinued but their 3D CAD models are available. In addition, damaged portions of a part can be reconstructed by utilizing 3D printing. However, using a 3D printer requires a skilled operator with knowledge of this technology and other technical aspects. Hence, in this research, a user-friendly maintenance framework has proposed for any operator to repair partially damaged parts using 3D printing without requiring expert technical support. The framework includes a parts catalog with information necessary for 3D printing, a search module for automatically identifying damaged parts without prior knowledge about the part, and a shape comparison module for validation of the repaired part through damage detection and error measurement. Design and implementation of a maintenance support system based on the proposed framework are explained and the result of conducted experiment with a damaged ball from a valve to verify its performance is presented.

Maintenance Framework for Repairing Partially Damaged Parts Using 3D Printing

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Development of Preprocessor for the Exchange of Equipment and Materials’ Specifications Sheet Data Based on ISO 15926

Development of A Postprocessor for Exchanging of Equipment and Materials Specification-sheet Data Based on ISO 15926

Maintenance Framework for Repairing Partially Damaged Parts Using 3D Printing