Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites
Highlights
► The environmental impacts of machining Al alloy- SiC composite have been analysed. ► A new and novel model and methodology for selecting optimum cutting conditions for machining, based on minimum energy requirements and maximum tool life is presented. ► Reduction in amount of scrap due to increased tool life will save the environment.
Introduction
The manufacturing sector is a key industry that relies on the use of energy in driving value during manufacturing processes. Mechanical machining is widely used in most manufacturing industries hence represents a major demand for energy. Despite decades of optimizing of machining operations based on cost and productivity, optimizing energy use had not received significant attention. There is an abundant amount of research work done on the machining process but environmental issues of machining processes have rarely been given much attention. The energy required for the machining process is drawn from the electrical grid. The energy (electricity) is generated from different power sources like thermal, nuclear and hydraulic. Saving in electrical energy will result less emission of harmful gases, if a thermal or nuclear source has been used for generation of electricity and saving of precious water resources, if hydraulic route has been used for generation of electricity.
The first step toward reducing power consumption and maximize the life of tools in machining is to analyze the impact of machining parameters on power consumption and tool life. Reducing the energy consumption of machine tools can significantly improve the environmental performance of manufacturing processes and systems. Furthermore, given that machining processes are used in manufacturing many consumer products, improving the energy efficiency of machining-based manufacturing systems could yield significant reduction in the environmental impact of consumer products.
This research work presents a new and novel model and methodology for selecting optimum cutting conditions for machining, based on minimum energy requirements. The energy savings associated with using such methodologies are quantified and found to be very significant. This work makes a distinct and important contribution to the machining science for reducing the energy there by resulting in minimizing harmful emissions.
Section snippets
Literature review
Hauschild et al. (2005) report suggests that the deficiency in the evaluation of the life cycle and the process involved in product's manufacturing to provide substantial consumption amounts of energy and other resources and, as a result, have a measurable impact on the environment.
The work of Kara and Li (2011) presents an empirical approach to develop unit process energy consumption models for material removal processes. For the selected machine tools, the derived models provide a reliable
Material
The chemical composition of 7075 Al alloy was analyzed by electron probe microscopic analysis (EPMA). It is as shown in Table 1.
7075 Al alloy–15 wt% SiC (particle size 20–40 μm) composites are fabricated by stir casting process under, controlled conditions.
Computer numerical control (CNC) turning machine
The basic objective behind the use of CNC turning machine is the reduction in the cost of production and the improvement in product quality. Machining by CNC turning can be done to very precise limits, which normally is very difficult by a
Design of experiments
Designs of experiments are considered as very useful strategy for deriving clear and accurate conclusions from the experimental observations. In this phase of experimentation a design of experimentation technique versus Response Surface Methodology has been used for studying the influence of four process parameters (cutting speed, feed, depth of cut and nose radius) on two different responses in machining of 7075 Al alloy SiC composites. Face centered central composite design is preferred in
Response surface methodology (RSM)
Box and Wilson (1951) have proposed response surface methodology for the optimization of experiments. According to, Myers and Montgomery (1995) the RSM is an empirical modeling approach for the determination of relationship between various process parameters and responses with the various desired criteria and searching the significance of these process parameters on the coupled response It is a sequential experimentation strategy for building and optimizing the model.
Planning of experimentation
Turning of AA7075–15 wt% SiC Particles (20–40 μm) composite is carried out by tungsten carbide inserts on CNC turning machine as per the plan of experiments tabulated in Table 5.
Adequate model is selected for these responses. Analysis of variance (ANOVA) is performed in order to statistically analyze the results. Significant process parameters are identified. Interaction effects of process parameters were studied.
Results
In experimental investigations, the results depend, to a large extent, on the way in which data is collected. The most preferred method of experimentation utilized by the researchers is face centered central composite design. This measures the response of every possible combination of factors and the factor levels. These responses are analyzed to provide information about every main effect and every interaction effect. Values of responses viz power consumption and tool life is measured. These
Macro-scale observation and micro-scale mechanisms for power consumption and tool life
The power consumption increases as cutting speed increase. This is quite obvious because as cutting speed increases, the material removal rate also increases forcing the system to spend more power. As cutting speed increases, the heat generated at the tool workpiece interface also increases. With a significant portion of heat generated entering in to the workpiece material, this increased heat generated leads to softening of the aluminum matrix. In Al alloy/SiC machining, significant amount of
Conclusions
Turning experiments were conducted based on response surface methodology for AA7075–15 wt% SiC (20–40 μm) composite using 6615 grade tungsten carbide cutting tool. The experimentally collected data were subjected to ANOVA and desirability function analysis for optimization of machining parameters. From this analysis, the following conclusions are drawn for power consumption and tool life:
- 1.
The 3D surface plots of Response Surface Methodology (RSM) for AA7075–15 wt% SiC (20–40 μm) composite
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