In-process surface roughness monitoring based on workpiece surface temperature for turning operations

As the machining process has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance and controlling the required surface quality. The focus of present study is to predict and monitor the surface roughness (Ra) in-process using workpiece surface temperature (T) of a turning workpiece, supported by the cutting tool vibration represented by (RMS). Thus, an in-process surface roughness monitoring and control system during the machining process were developed via temperature sensing in order to achieve a good trade-off between cost and performance, with a high reliability and a reduced computing time, and using sensors that do not disturb the machining process. Response surface method (RSM) and analysis of variance (ANOVA) are used to get the relationship between different response variables (surface roughness, workpiece surface temperature, and cutting tool vibration) and the input parameters (cutting speed, feed rate, and depth of cut). The experimental results showed that workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance. Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment. An effective monitoring method presented in this research using Grey relational analysis (GRA) to identify the surface roughness utilizes the grey relational coefficient (GRC) and grey relational grades (GRG), using in-process measured multi performance responses. An artificial intelligence approach developed then for cutting parameters identification using multi adaptive network based fuzzy inference system (MANFIS). Two models architectures were presented, multi-input-multi-output (MIMO) ANFIS and single-input-multi-output (SIMO) ANFIS. The results show that the developed MANFIS models can be used successfully for machinability data selection once the desired surface roughness entered to the system. The present study also, takes into account the degree of the influence of the workpiece diameter and overhung length and their interaction with the cutting parameters on the surface roughness. The diameter was identified to be the most influence on the surface roughness followed by the length and there is a significant interaction between them and the feed rate and depth of cut. The experimental results show that the workpiece surface roughness improved with bigger diameter and shorter length of the workpiece.

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