# Opportunities for Industry 4.0 to Support Remanufacturing
## 4 Case Illustrations
### Sensorized Machines for Intelligent Machining  
Machine tools could become increasingly smart by perceiving their own states and the state of the surrounding environment, which is considered as \"intelligent machining\". Key enablers for this capability include smart sensors embedded into machine systems, a data acquisition system which can transmit the processing data quickly without data loss, and also the intelligent central system which receives and analyzes the data. Such an intelligent machining system is of great importance for evaluating the system's health and to enable prediction of a possible breakdown or malfunction before it happens. Currently, advanced condition-based maintenance is triggered when an asset reaches a predefined unacceptable level detected by the sensoring system. These thresholds usually originate from experts' experiences (machine operators) or manufacturers' recommendations. However, when multiple complicating factors coexist, false alarms might be triggered. To resolve such complications, sensors and advanced computer systems will be used in tandem during intelligent machining to monitor the system health. Both historical and real-time sensor data will be used in the evaluation of the machine condition via artificial intelligence techniques to substantially increase the prediction accuracy and the quality of the produced part. This work is now being carried out by a research group in ARTC. A large number of sensing devices, including vibration sensors, current sensors, temperature sensors, and acoustic emission sensors, have been installed into various critical subsystems of a computer numerical control (CNC) machine, such as spindles, linear guides, ball screws, cutting tools, etc. All the sensors are linked to three data acquisition (DAQ) systems. Each DAQ system performs data acquisition and storage. The data are used for analyzing the correlation between machine health and the quality of the product selected to be machined. Sensor data are captured and recorded in the database during the process of machining the product, which is, in our case, a carburized steel shaft. As modern machines are quite robust, it may take months or years before detectable changes in machine operation and performance can be observed. Sensors can register small changes indicating the beginning of degradation in machine components or subsystems. However, it is not guaranteed that such changes will affect the quality of machined products. Therefore, it is difficult to determine the remaining useful life of a whole machine system or subsystem based on one-time measurement without access to historical data. In order to obtain the critical and more relevant machine data, different levels of failure modes, such as tool wear, tailstock misalignment, and spindle unbalance, were simulated. At the same time, changes in sensor data collected during experiments to simulate different levels of machine failure modes, as well as the measurement of part quality, including surface roughness, waviness, dimensions, and out-of-roundness, have been captured and documented in order to search for correlations between the sensor signals, deterioration of machine subsystems, and quality of machined products, as shown in Figure 3. To date, a total of 190 GB of data have been collected during the machining of 45 shafts under the different simulated failure modes. Work is ongoing to analyze these data and to generate a model for machine failure prediction. The purpose of developing intelligent machining capability is to be able to monitor machine condition and part quality, to predict the need for maintenance, and to avoid costly failure and the need for part rework. In the meanwhile, data captured will also form part of a product's \"digital life-story\", which will be of great use for supporting product end-of-life decision making and analyzing the cause of product failure.