Characterization of tool wear in friction drilling

Friction drilling is a non-traditional hole-making process, where the rotating conical tool between the thin workpiece produces a heat due to penetration to soften the workpiece and form a hole. It creates a bushing without generates the chip. Tool wear in friction drilling is crucial because it affects the tolerances that are achievable. In this study, the tool wear characteristics of friction drilling on low carbon steel were experimentally investigated using tungsten carbide tool. Tool wear characteristics were quantified by measuring the changes in tool shape and weight reduction. The energy dispersive spectrometry was utilized to analyze the element containing on the tool surface, and the observation of wear was made using optical microscope and scanning electron microscope. The results indicated that the conical tungsten carbide tool is durable and can be used up to 1000 holes. The changes of tool shape and weight reduction were concentrated at the tool center and conical regions. It confirmed that the abrasive wear revealed at the same regions with circular grooves. The adhesive wear was observed at the tool center and conical regions, and oxidation wear was identified with a dark burned appearance at the tool surface

Characterization of tool wear in friction drilling

Friction drilling is a non-traditional hole-making process, where the rotating conical tool between the thin workpiece produces a heat due to penetration to soften the workpiece and form a hole. It creates a bushing without generates the chip. Tool wear in friction drilling is crucial because it affects the tolerances that are achievable. In this study, the tool wear characteristics of friction drilling on low carbon steel were experimentally investigated using tungsten carbide tool. Tool wear characteristics were quantified by measuring the changes in tool shape and weight reduction. The energy dispersive spectrometry was utilized to analyze the element containing on the tool surface, and the observation of wear was made using optical microscope and scanning electron microscope. The results indicated that the conical tungsten carbide tool is durable and can be used up to 1000 holes. The changes of tool shape and weight reduction were concentrated at the tool center and conical regions. It confirmed that the abrasive wear revealed at the same regions with circular grooves. The adhesive wear was observed at the tool center and conical regions, and oxidation wear was identified with a dark burned appearance at the tool surface

Tool condition monitoring of friction drilling process using adaptive neuro-fuzzy inference system

Friction drilling is a new progressive hole-making method. The interaction of the rotating conical tool and the thin workpiece produces heat allowing penetration of the tool and soften the work-piece forming a hole and bush in one process. The process is environmentally friendly since bush formation creates no material wastage and requires no coolant fluid during the machining process. However, performing the machining process with a worn tool can increase the friction between the tool-workpiece and the late replacement of the worn tool may cause an unpredictable machine breakdown. The focus of the present study is to develop an AI-based expert system for tool condition monitoring (TCM) in the friction drilling process. Thus, the TCM was developed by detecting the machining signals through signal processing and pattern recognition. Subsequently, the tool condition was predicted by the artificial intelligence (AI) approach. A tungsten carbide tool was used in this experiment of friction drilling on medium carbon steel AISI 1045. As preliminary experiments, to determine optimal processing parameters in the friction drilling process by considering multi-performance characteristics (i.e., bush length and roundness error), an effective grey relational analysis (GRA) approach has been used. Tool wear characteristics were quantified of friction drilling by analyzing the changes in tool shape and weight reduction. TCM in the friction drilling process was developed based on the vibration signal collected through accelerometer sensors of the machining signals through a low-pass filter. Three approaches AI-model such as Artificial Neural Network (ANN), Fuzzy Logic (FL), and Adaptive Neuro-Fuzzy Inference System (ANFIS) used to boost the efficiency of the prediction system to anticipate the state of the tool in terms of the tool length and angle. The outcomes of the established models were compared in terms of prediction accuracy to find the best performing model. Therefore, real-time condition monitoring took part to verify the TCM system for the friction drilling process. The GRA obtained 3000 rpm of spindle speed and 50 mm/min of feed rate the best combination of processing to achieve a greater bush length and lower roundness error. The tool wear characteristic can be confirmed that the abrasive wear revealed in the conical region with circular grooves. The adhesive wear was observed at the tool centre and conical regions, and oxidation wear was identified with a dark burned appearance at the tool surface. The development of the AI-model model shows excellent performance, which the R-squared correlation shows the ANFIS model was 97.2% and 97.1% for tool length, and the angle at the training phase seen an increase to 98.4% and 98.2% in the testing phase. It was verified in the real-time TCM experiments that the ANFIS-based expert system was successfully developed and utilized in monitoring the tool condition by categorizing the level of condition into three distinct categories, i.e., good, half-life, and worn-out conditions

Evaluation of tool wear and machining performance by analyzing vibration signal in friction drilling

Tool condition plays an important role in machining performance. In machining processes, multiple phenomenon occurs during material cutting. To improve their robustness, the reliability pattern recognition techniques have been implemented in tool condition monitoring systems. This study demonstrates a tool condition monitoring approach in a friction drilling operation based on the vibration signal collected through accelerometer sensors. The experiment has been carried out on a CNC milling machine. In this present work, an optimal parameter in friction drilling has been used on medium carbon steel AISI 1045. The signals were collected by accelerometer sensors and Low-pass-filter was utilized to filter the raw data. Pattern recognition was identified and categorized into one of three clusters which are; tool at good, half-life and worn-out conditions. The results found that the vibration amplitude is directly proportional to tool wear and friction which support the nature of tool wear in drilling process. The hole size reduction on the workpiece can also be seen clearly with the increasing vibration on the process due to the tool wear. With the effectiveness of pattern recognition, the damages of the machine tool can be avoided to control the product quality consistently

Multi-objective optimization in friction drilling of AISI1045 steel using grey relational analysis

The friction drilling process has the great prospective to vitalize green manufacturing in hole-making process. The optimization of process performance in friction drilling can be realized through an appropriate selection on process parameters. However, some recognized techniques can successfully optimize only on a single-performance characteristic, and optimization on multi-performance characteristics can be difficult and challenging to investigate due to its complexity in the analysis. In this present work, a parametric optimization in friction drilling of medium carbon steel AISI 1045 using L25 orthogonal array design of experiments has been experimentally investigated. The grey relational analysis (GRA) has been utilized to determine optimum process parameters in friction drilling process by considering multi-performance characteristic, namely bush length and roundness error. The GRA results confirm that the best combination of process parameter is obtained as spindle speed 3000 rpm and feed rate 50 mm/min. It has been found that the spindle speed is the more significantly affected than feed rate to obtain a greater bush length and lower roundness error through response table. The confirmation test results show that the GRA succeeds in optimizing the process parameters in friction drilling process. The study revealed the multi-performance characteristic can be enhanced by selecting the proper process parameters