# Opportunities for Industry 4.0 to Support Remanufacturing
## 4 Case Illustrations
### Smart Repair Cell
The scanned  
Figure 2: Smart remanufacturing cell-process flow. output is stored in the shared database, usually in point-cloud form. Secondly, the scanned data is compared with the original digital design of the part (computer-aided model--CAD), also called golden sample/nominal data, to obtain the geometry difference, using reverse engineering software. Thirdly, on-machine probing or a scanning method is carried out to measure the information on the machine coordinate system (MCS) and to align the repaired part with respect to the machine datum. Lastly, via the developed machine-to-machine communication software platform, geometry differences and the MCS are fed into computer-aided manufacturing (CAM) software for the adaptive tool-path generation. The tool path generated based on this aligned datum is stored in the shared database layer as a numerical control (NC) file. Following the additive repair processes, when a part is ready for final machining, the NC file is retrieved from the database to be used for machining. Various machining processes, such as 5-axis milling, turning, grinding, abrasive polishing, etc., are carried out to achieve the final dimensions on the repaired component. Machining-related information is also stored in the database as part of the product's digital \"life-story\". The real-time monitoring of the machining data plays a key role in defining the surface integrity of the machined parts. In the subsequent case study, the effect of sensor integration on machine tools and its impact on Industry 4.0 is discussed in detail. In addition, thanks to the advancement of machine tool technology, both additive and subtractive processes can be performed with one single machine. Hybrid machines--combining both laser metal deposition blown powder and 5-axis milling in one machine--are now commercially available for use in production. This provides the benefit of single-machine and single-work setup, hence improving the volumetric accuracy of the final repaired component. In addition, the cost of multiple machines, corresponding part transfer automation, and the space requirement are reduced substantially. Smart repair cells are designed to be an effective way of repairing each component adaptively with utmost accuracy and tolerance due to seamless information flow across the processes. Currently, a smart repair cell concept is simulated at ARTC using various scenarios such as number of machine tools, robotic automation vs manual part transfer, and volume of cores. Based on the simulated results, the best operational efficiency is calculated to select the right hardware combinations. Know-how in reverse engineering, adaptive toolpath development, and individual repair processes have already been embedded into the smart repair cell. Ongoing work at ARTC is focused on building up the database based on number of cores, real-time machine tool monitoring, and the final remanufactured part conformance data, which will eventually help machine learning for automatic repair decision making. It should be noted that the above-mentioned remanufacturing cell focuses mainly on the adaptive repair process. Disassembly, cleaning, re-assembly, and, most critically, the NDT process for analyzing issues related to adhesion of the repaired material, change of material structure, porosity, inclusions, etc. are not addressed in this paper.