Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’

Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process

Modeling the surface roughness and cutting force for turning

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.

A neural-network-based methodology for the prediction of surface roughness in a turning process

Due to the numerous interacting machining variables involved in finish turning, it is extremely difficult to assess the total machining performance (TMP) encompassing surface finish, tool-wear rate, dimensional accuracy, cutting power and chip breakability. This paper presents the development of a new methodology for assessing each aspect of the TMP in finish turning of steels as follows. First, a machining reference database is established from the representative machining experiments to provide a primary quantitative standard. Second, a knowledge poo) is developed based on the extended experiments and the present knowledge of the major influencing factors on the TMP. Then, a fuzzy-set method is introduced to quantify the effects of these factors. Finally, a series of fuzzy-set models are developed to give quantitative assessments of the TMP for any given set of input conditions, including work material properties, tool geometries, chip-breaker types and cutting conditions.

X. D. Fang

Papers

Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters