Abstract: In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.
Abstract: The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.
Abstract: Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang.
Abstract: Chatter vibrations, occurring during cutting process,
cause vibration between the cutting tool and workpiece, which
deteriorates surface roughness and reduces tool life. The purpose of
this study is to investigate the influence of cutting parameters and
tool construction on surface roughness and vibration in turning of
aluminum alloy AA2024. A new design of cutting tool is proposed,
which is filled up with epoxy granite in order to improve damping
capacity of the tool. Experiments were performed at the lathe using
carbide cutting insert coated with TiC and two different cutting tools
made of AISI 5140 steel. Taguchi L9 orthogonal array was applied to
design of experiment and to optimize cutting conditions. By the help
of signal-to-noise ratio and analysis of variance the optimal cutting
condition and the effect of the cutting parameters on surface
roughness and vibration were determined. Effectiveness of Taguchi
method was verified by confirmation test. It was revealed that new
cutting tool with epoxy granite has reduced vibration and surface
roughness due to high damping properties of epoxy granite in
toolholder.