Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool.

Machining activities in recent times have shifted their focus towards tool life and tool wear. Cutting tools have been utilized on a daily basis and play a vital role in manufacturing industries. Prolonged and incessant operation of the cutting tool can lead to wear and tear of the component, thereby compromising the dimensional accuracy. The condition of a tool is estimated based upon the surface quality of the machined component, condition of the machine, and the rate of production. Maintaining the tool health plays a vital role in enhancing the productivity of manufacturing industries. Numerous efforts were experimented by the researchers to maintain the tool health condition. The drawbacks of conventional diagnostic techniques include requirement of high level of human intelligence and professional expertise on the field, which led the researchers to develop intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the condition of single point cutting tool. This article proposes the use of transfer learning technology to detect the condition of single point cutting tool. First, the vibration signals were collected from the cutting tool and plots were made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the single point cutting tool. In this work, the pretrained networks such as VGG-16, AlexNet, ResNet-50, and GoogLeNet were employed to identify the state of the cutting tool. In the pretrained networks, the effect of hyperparameters such as batch size, solver, learning rate, and train-test split ratio was studied, and the best performing network was suggested for tool condition monitoring.

Misfire Detection in Spark Ignition Engine Using Transfer Learning.

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.