Artificial Neural Network Based Machining Operation Selection for Prismatic Components

Computer-aided process planning systems are used to assist human planners in producing better process plans. New artificial intelligence techniques play a significant role in CAPP. CAPP research includes neural network approaches, knowledge-based techniques, Petri nets, agent-based, fuzzy set theory, genetic algorithm, Standard for the Exchange of Product model data (STEP)-Compliant CAPP, and Internet-based techniques. This study deals with the application of the Artificial Neural Network techniques (ANN) in CAPP because of their learning ability and massive potential toward dynamic planning.  This study focuses on the usage of artificial neural networks machining operation selection and sequences of operations for prismatic components. The intelligent CAPP system suggests the best machining operation and its sequences for the prismatic components using tolerances, material requirements, and surface finish details. The process planning of machining features in part is the starting point. An enormous amount of knowledge is required for part feature process planning, like selecting proper material, size, stock, dimensional tolerance, and surface finish. In this work, various prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face are taken and details like material, size, stock, dimensional tolerance and surface finish are properly normalized and given as input to neural networks to find the required sequence of machining operation. LevenbergMarquidt algorithm was used to train the networks and was found very effective in operation sequence selection. A sample prismatic component with nine features have been analyzed and found to be more productive. Levenberg Marquidt  algorithm is then compared with the conjugant space algorithm, and it is found that the former produces less error in outputs compared to them later

Application of Regression and Fuzzy Logic Method for Prediction of Tool Life

AbstractThis paper presents a model for predicting tool life when end milling IS2062 steel using P30 uncoated carbide tipped tool under various cutting conditions. A tool life model is developed from regression model obtained by using results of the experiments conducted based on Taguchi's approach. A second model is developed based on fuzzy logic method for predicting tool life. The results obtained from fuzzy method are compared with regression model. The results of the fuzzy model is found to be more closer to experimental value

Internet of Things Based Cutting Tool Status Monitoring in a Computer Numerical Control Milling Machine

The Internet of Things (IoT) in the manufacturing industry shares machine data in real time. The majority of industrial data can be gathered and processed from machines and other remote IoT devices in a production system through data streaming. As sensors become smaller and more affordable, the Internet of things will attract more attention. It is possible to employ a wide range of sensors for monitoring, and with efficient open-source software, the status of the operation can be evaluated effectively. Our work aims at providing instructions for sending data (cutting tool status time) from Particle Photon devices connected to CNC milling machines to open-source software called ThingSpeak. This task is accomplished by integrating the Particle Photon device with the infrared sensor to the CNC milling machine. Three axis CNC vertical milling machine is used to manufacture the sample component. The status of the cutting tool, in this case the cutting time for each machining feature is monitored. Using infrared technology, the sensor detects whether the cutting tool is present. Particle photons measure machining time and communicate it to ThingSpeak. The ThingSpeak library will interpret the cloud information and displays it. With ThingSpeak software, we define the fields for various cutting tools and track every tool in real time. The time recorded using ThingSpeak software is found to be near to the time, which is monitored manually by the user. A major step in computer assisted process planning in manufacturing is monitoring the machining time for each cutting tool and it is successfully implemented in this research work