Cutting tool selection plays an important role in achieving reliable quality and high productivity work, and for controlling the total cost of manufacturing. However, it is complicated for process planners to choose the optimal cutting tool when faced with the choice of multiple cutting tools, multiple conflict criteria, and uncertain information. This paper presents an effective method for automatically selecting a cutting tool based on the machining feature characteristics. The optimal cutting tool type is first selected using a proposed multicriteria decision-making method with integrated fuzzy analytical hierarchy process (AHP). The inputs of this process are the feature dimensions, workpiece stability, feature quality, specific machining type, and tool access direction, which determine the cutting tool type priority after evaluating many criteria, such as the material removal capacity, tool cost, power requirement, and flexibility. Expert judgments on the criteria or attributes are collected to determine their weights. The cutting tool types are ranked in ascending order by priority. Then, the rule-based method is applied to determine other specific characteristics of the cutting tool. Cutting tool data are collected from world-leading cutting tool manufacturer, Sandvik, among others. An expert system is established, and an example is given to describe the method and its effectiveness.

The experiments of the surface grinding of Ti-6Al-4V grade 5 alloy (Ti-64) with a resin-bonded cubic Boron Nitride (cBN) grinding wheel are performed in this research to estimate the influence of cutting parameters named workpiece infeed speed, Depth of Cut (DOC), cooling condition on the grinding force, force ratio, and specific energy. A finite element simulation model of single-grain grinding of Ti-64 is also implemented in order to predict the values of grinding forces and temperature. The experimental results show that an increase of workpiece infeed speed creates higher intensified cutting forces than the DOC. The grinding experiments under wet conditions present slightly lower tangential forces, force ratio, and specific energy than those in dry grinding. The simulation outcomes exhibit that the relative deviation of simulated and experimental forces is in the range of 1-15%. The increase in feed rate considerably reduces grinding temperature, while enhancement of DOC elevates the heat generation in the cutting zone.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

This study investigates the factors governing the distribution of surface roughness in the finish grinding of complex freeform bone plates using a single-layer electroplated CBN ball end wheel on Ti6Al4V alloy. Eight independent workpieces were machined under fixed process parameters, four in the as-machined condition and four heat treated to 41 HRC, with each group ground sequentially by a dedicated wheel. Roughness was measured at twenty-one locations with three repeated measurements per location, and radial wheel wear was monitored across eight cross sections throughout the grinding sequence, yielding 504 observations. The spatial distribution of roughness proved reproducible, with intraclass correlation coefficients of 0.852 and 0.768 for the two groups, and was preserved across both material conditions. Wheel wear exhibited a two-stage behaviour and reduced roughness by 5.53 nm per micrometre of cumulative wear, consistent with the levelling of grain protrusion heights during run in. Heat treatment reduced mean roughness by 91.8 nm, corresponding to 24.0 percent, while regions of larger radius of curvature exhibited roughness higher by up to 77.5 nm. The measured roughness exceeded the geometric scallop limit by a factor of 16 to 103, indicating that the governing mechanism resides in the abrasive grit regime rather than in toolpath kinematics within the range of conditions examined.