Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting

In modern micromachining, there is a need to measure and monitor certain machining process parameters in process so as to detect tool wear in real time, to optimize the process parameters setup, and to render the machining process some level of smartness and intelligence. This paper presents the innovative design of a smart turning tool using two pieces of piezoelectric films to measure cutting and feed force in real time. The tool was tested on its performance through the calibration and cutting trials against the commercial dynamometer. The results show the smart turning tool has achieved the performance as designed

The performance of modified jatropha-based nanofluid during turning process

The industry's extensive use of petroleum-based metalworking fluid (MWF) harms the environment and humans. The production of bio-based MWF, especially from crude jatropha oil (CJO), has therefore taken numerous initiatives. This aimed to formulate newly modified jatropha oil (MJO) with the addition of 0.05wt.% hBN and 0.05wt.% MoS2 as the nanofluid for MWF. The performance of the nanofluids was determined through the turning process in terms of cutting temperature, workpiece surface roughness, tool life and tool wear of the tool lubricated by the nanofluids. The performance of the nanofluid samples was compared with the synthetic ester (SE). From the results, after conducted 100mm axial cutting length MJO+hBN+MoS2 recorded the lowest in cutting temperature and surface roughness compared to all samples. The result shows that MJO+hBN+MoS2 has longer tool life (6500mm) compared to SE (6000mm). Abrasion and adhesion were observed as the dominant tool wear mechanism. In conclusion, MJO+hBN+MoS2 shows better machining performance and has the potential to be an environmentally friendly metalworking fluid

CampProf: A Visual Performance Analysis Tool for Memory Bound GPU Kernels

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge these performance models, by modeling and analyzing a lesser known, but very severe performance pitfall, called 'Partition Camping', in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of memory-bound CUDA kernels by up to seven-times. No existing tool can detect the partition camping effect in CUDA kernels. We complement the existing tools by developing 'CampProf', a spreadsheet based, visual analysis tool, that detects the degree to which any memory-bound kernel suffers from partition camping. In addition, CampProf also predicts the kernel's performance at all execution configurations, if its performance parameters are known at any one of them. To demonstrate the utility of CampProf, we analyze three different applications using our tool, and demonstrate how it can be used to discover partition camping. We also demonstrate how CampProf can be used to monitor the performance improvements in the kernels, as the partition camping effect is being removed. The performance model that drives CampProf was developed by applying multiple linear regression techniques over a set of specific micro-benchmarks that simulated the partition camping behavior. Our results show that the geometric mean of errors in our prediction model is within 12% of the actual execution times. In summary, CampProf is a new, accurate, and easy-to-use tool that can be used in conjunction with the existing tools to analyze and improve the overall performance of memory-bound CUDA kernels

Academics in control: supporting personal performance for teaching-related activities

Academics are under pressure because of entrepreneurial constraints, such as budgets and cost-oriented objectives, and educational demands, such as for more flexibility and for offering courses online or with online components. Based on the results of a series of studies of desired and actual performance, and evaluations of responses to a set of prototypes of a Personal Performance Support Tool, a final prototype version of the tool was developed to research the effects the tool can have on the performance and job satisfaction of academics, especially for their teaching-related activities

Part Form Errors Predicted from Machine Tool Performance Measurements

Machine tool performance testing, as defined by IS0 230 and ANSI B5.54 has been successfully used to maintain and improve the accuracy and repeatability of production-level machine tools. In this study, a controlled series of experiments have been used to test the efficacy of these performance tests in the prediction of part form errors. Results are shown for flatness, squareness, position, and profile tolerances. The experimental results suggest that standard machine tool performance tests can also be used to predict the “best-case” tolerances that can be achieved for particular part features

Filling the Gap: a Tool to Automate Parameter Estimation for Software Performance Models

© 2015 ACM.Software performance engineering heavily relies on application and resource models that enable the prediction of Quality-of-Service metrics. Critical to these models is the accuracy of their parameters, the value of which can change with the application and the resources where it is deployed. In this paper we introduce the Filling-the-gap (FG) tool, which automates the parameter estimation of application performance models. This tool implements a set of statistical routines to estimate the parameters of performance models, which are automatically executed using monitoring information kept in a local database