# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 1 Introduction
[PERSON] _et al._ (2018) studied the tool vibration and surface roughness of AISI 4340 steel using the Taguchi method. The Taguchi design L18 was used to analyze the experimental results and showed that significant relationship between the surface roughness and vibration, and lower surface roughness were found during the turning process with tempered AISI 4340. Similarly, [PERSON] et al. [PERSON] _et al._ (2018) used the L27 Taguchi method for investigating the effect of WEDM process parameters on surface roughness, MRR, and cutting rate. The results showed that the surface roughness was affected by pulse-on time and servo voltage, while the cutting rate and MRR were affected by pulse-on/off time. [PERSON] et al. [PERSON] _et al._ (2018, 2019) used the L16 Taguchi method to investigate the turning process and EDM process for machining deep cryogenically treated AISI 4140 steel. [PERSON] et al. [PERSON] _et al._ (2018) used response surface methodology (RSM) for modeling and analysis of the machining parameters to obtain best surface roughness on electrical discharge machining (EDM). [PERSON] et al. [PERSON] _et al._ (2018) proposed the RSM method to optimize machining parameters to achieve maximum material removal rate (MRR). Several parameters such as pulse-on time, pulse-off time, and current were chosen and investigated. The study showed a significant increase in MRR when current and pulse-on time increased. On the contrary, MRR decreased when pulse-off time increased. [PERSON] et al. [PERSON] _et al._ (2018) proposed a combination of gray relational analysis (GRA) and principal component analysis (PCA) to identify the optimal combination of machining parameters on the WEDM process of hardfacing materials. [PERSON] et al. [PERSON] _et al._ (2018) used gray relational analysis for optimizing the WEDM process parameters. Peak current, pulse-on time, and wire feed rate were chosen as input parameters, with kerf width and surface roughness as the output response. [PERSON] et al. [PERSON] _et al._ (2018) developed a multiresponse optimization technique to optimize WEDM process parameters for surface roughness, material removal rate, and wire consumption. This study proposed a systematic system that can optimize the machining parameters in order to reduce the corner error during corner machining and shorten the machining time. Experiments with different settings of machine parameters were conducted. In this study, statistical analysis using the Box-Behnken response surface methodology (RSM) was carried out to investigate the relationship between each parameter and corner machining error. Additionally, the multiparameter interaction and the correlation to corner machining error was studied. Box-Behnken Design is specially designed to fit a second-order model and only needs three levels for each factor which avoids the extreme axial (star) points as in the central composite design, so it is more practical. In addition, the Box-Behnken design requires a smaller number of experiments. The mathematical model was developed using the RSM to establish the relationship between independent parameters, the surface response corner error, and machining time. Furthermore, an algorithm was developed to optimize the parameter to obtain smaller corner error and faster machining time. In addition, a friendly human-machine interface that can predict the corner error and machining time,and also optimize the parameters to obtain smaller corner error and faster machining time, was developed using C# programming language.