Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites

Bhushan, Rajesh Kumar

doi:10.1016/j.jclepro.2012.08.008

Cited by 324 publications

(141 citation statements)

References 19 publications

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“…Here, a data item selection problem occurs because even one data element can contain various sub-elements that influence P machine and k and thus all sub-elements are not possible to be accommodated. For example, even an insert contains various sub-elements that make the variety of P cutting such as insert material, tool nose radius, flank angle, and cutting edge length [16,33,35] and thus including all the sub-elements would be quite difficult to identify MC data items and then generate predictive models in terms of the MC data items identified. To avoid the complexity of the data selection problem, "machine tool model", "cutting tool type", "insert material", "workpiece material", "machining operation", "cooling type", and "path trajectory", which are dominant data items influencing P machine and k and closed to nominal or enumeration data types, are chosen as a set of MC data items in the present work.…”

Section: Categorization Of Process Feature Datamentioning

confidence: 99%

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

et al. 2018

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Abstract:In the metal cutting industry, power consumption is an important metric in the analysis of energy efficiency since it relates to energy consumption of machine tools. Much of the research has developed predictive models that correlate process planning decisions with power consumption through theoretical and/or experimental modeling approaches. These models are created by using the theory of metal cutting mechanics and Design of Experiments. However, these models may lose their ability to predict results correctly outside the required assumptions and limited experimental conditions. Thus, they cannot accurately reflect a diversity of machining configurations; i.e., selections of machine tool, workpiece, cutting tool, coolant option, and machining operation for producing a part, which a machining shop has operated. This paper proposes a predictive modeling approach based on historical data collected from machine tool operations. The proposed approach can create multiple predictive models for power consumption, which can be applicable to the diverse machining configurations. It can create fine-grained models predictable up to the level of a numerical control program. It uses standard-based data interfaces such as STEP-NC and MTConnect to implement interoperable and comprehensive data representations. This paper also presents a case study to demonstrate the feasibility and effectiveness of the proposed approach.

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Section: Categorization Of Process Feature Datamentioning

confidence: 99%

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

et al. 2018

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“…There are good theoretical computations available for cutting energy, but they are difficult to perform due to the difficulties in the calculation of all the parameters involved in the theoretical formulas (Kalpakjian, 1984). The empirical method is, therefore, still widely used for the reliable prediction of cutting forces and energies (Bhushan, 2013;Ding et al, 2010). Empirical models possess simple and easy-to-get characteristics as well as provide high prediction accuracy.…”

Section: Energy Consumption Of Materials Removalmentioning

confidence: 99%

Experimental study on energy consumption of computer numerical control machine tools

Tang

Jia

et al. 2016

Journal of Cleaner Production

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Machining processes are responsible for substantial environmental impacts due to their great energy consumption. Accurately characterizing the energy consumption of machining processes is a starting point to increase manufacturing energy efficiency and reduce their associated environmental impacts. The energy calculation of machining processes depends on the availability of energy supply data of machine tools. However, the energy supply can vary greatly among different types of machine tools so that it is difficult to obtain the energy data theoretically. The aim of this research was to investigate the energy characteristics and obtain the power models of computer numerical control (CNC) machine tools through an experimental study. Four CNC lathes, two CNC milling machines and one machining center were selected for experiments. Power consumption of non-cutting motions and material removal were measured and compared for the selected machine 2 tools. Here, non-cutting motions include standby, cutting fluid spraying, spindle rotation and feeding operations of machine tools. Material removal includes turning and milling. Results show that the power consumption of non-cutting motions and milling is dependent on machine tools while the power consumption of turning is almost independent from the machine tools. The results imply that the energy saving potential of machining processes is tremendous.

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“…The surface roughness of the turned parts and how their roughness is affected by the wear of the cutting tool was studied. The interaction of the wear on the tool (notch wear, flank wear, crater wear, among others) and the cutting parameters used in the process can be critical to the machined work surface finish [15] and [16] and may give unsatisfactory results.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

2018

SV-JME

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Manufacturing processes are aimed at transforming materials into goods, generating wealth [1]. Camposeco-Negrete [2] states that cost and quality are the main goals of manufacturing companies. To improve quality in this type of process, several authors have studied the turning process using mathematical strategies in order to contribute to the efficiency of these processes, such as: the Taguchi method The primary input parameters in the turning process, i.e., cutting speed, feed rate and depth of cut [7], are directly responsible for the quality and productivity characteristics of the process, such as the amount of material removed, tool wear, and finishing of the product [8].Furthermore, the finishing of the machined parts can be evaluated according to the surface roughness, which are irregularities presented on the surface of the parts, characterized by grooves made by the tool during the machining process [9] of the cutting parameters (ranging from a single parameter per experiment) in the quality responses, such as tool life and surface roughness. This paper makes use of only one roughness parameter (arithmetic average roughness, R a ), considering the calculation of its metrics of the roughness characteristics (R a , R y , R q , R z and R t ). The average arithmetic roughness (R a ) is the arithmetic mean of the absolute values of the ordinates of the effective (measured) profile in relation to the midline in a sample length (Fig. 1). In addition, R a is the most used parameter for general quality control [10].The steel used in the turning process of this study was AISI 12L14 carbon steel (used in studies such as: Peruchi et al.

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Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites

Cited by 324 publications

References 19 publications

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

Experimental study on energy consumption of computer numerical control machine tools

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

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