Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining

doi:10.1016/j.jclepro.2014.07.073

Journal of Cleaner Production

Volume 83, 15 November 2014, Pages 151-164

https://doi.org/10.1016/j.jclepro.2014.07.073 Get rights and content

Highlights

•
Multi-objective predictive and optimization model for determination of machining parameters.
•
Two sustainable machining performance measures, viz. power consumption and surface roughness, are simultaneously optimized.
•
The power consumed during the machining is as important as product quality.

Abstract

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time.

Introduction

The 1980s have witnessed a fundamental change in the way governments and development agencies think about environment and development. The two are no longer regarded as mutually exclusive. It has been recognized that a healthy environment is essential for a healthy economy. Energy and materials are the two primary inputs required for the growth of any economy and these are obtained by exploiting the natural resources like fossil fuels and material ores. The industrial sector accounts for about one-half of the world's total energy consumption and the consumption of energy by this sector has almost doubled over the last 60 years (Fang et al., 2011). The consumption of critical raw materials (such as steel, aluminium, copper, nickel, zinc, wood, etc) for industrial use has increased worldwide. The rapid growth in manufacturing has created many economic, environmental and social problems from global warming to local waste disposal (Sangwan, 2011). There is a strong need, particularly, in emerging and developing economies to improve manufacturing performance so that there is less industrial pollution, and less material & energy consumption. Energy efficiency and product quality have become important benchmarks for assessing any industry. Machine tools have efficiency less than 30% (He et al., 2012) and more than 99% of the environmental impacts are due to the consumption of electrical energy used by the machine tools in discrete part machining processes like turning and milling (Li et al., 2011). Sustainability performance of machining processes can be achieved by reducing the power consumption (Camposeco-Negrete, 2013). If the energy consumption is reduced, the environmental impact generated from power production is diminished (Pusavec et al., 2010). However, sustainability performance may be reduced artificially by increasing the surface roughness as lower surface finish requires lesser power and resources to finish the machining. However, this may lead to more rejects, rework and time. Therefore, an optimum combination of power and surface finish is desired for sustainability performance of the machining process. A lot of research on the modelling and optimization of machining parameters for surface roughness, tool wear, forces, etc has been done during last 100 years after the well known formula relating tool life to cutting speed was given by Taylor (1907). However, a little research has been done to optimize the energy efficiency of machine tools. Moreover, in the past, metal cutting operations have been mainly optimized based on economical and technological considerations without the environmental dimension (Yan and Li, 2013). Reduction in power consumption will improve the environmental impact of machine tools and manufacturing processes.

Machine tools require power during machining, build-up to machining, post machining and in idling condition to drive motors and auxiliary equipments. However, the design of a machine tool is based on the peak power requirement during machining of material which is very high as compared to non-peak power requirement of the machine tool. This leads to higher inefficiency of energy in machine tools. The optimization of machining parameters for minimum power requirement is expected to lead to the application of lower rated motors, drives and auxiliary equipments and hence save power not only during machining but as well as during build-up to machining, post machining and idling condition. In addition to the machining parameters, the power requirement during machining also depends upon workpiece properties and cutting tool properties. In this study the work material is steel and cutting tool material is uncoated tungsten carbide. This combination is the most widely used combination in the industry and any reduction in power consumption is expected to lead to high saving of power in absolute numbers. No doubt, steel is one of the widely researched materials in machining for more than last half century, but there is a renewed interest in application of steel because of its sustainability – 100% recyclable and almost indefinite life cycle. AISI 1045 steel is one of the steel grades, widely used in different industries (construction, transport, automotive, power, etc.). In order to maximize sustainability performance, the materials that are both in abundant supply and have the potential for recycling/re-use with no significant environmental effect should be used (Pusavec et al., 2010). Energy requirement for steel recycling is less than one third of aluminium recycling.

There is a close interdependence among productivity, quality and power consumption of a machine tool. The surface roughness is widely used index of product quality in terms of various parameters such as aesthetics, corrosion resistance, subsequent processing advantages, tribological considerations, fatigue life improvement, precision fit of critical mating surfaces, etc. But the achievement of a predefined surface roughness below certain limit generally increases power consumption exponentially and decreases the productivity. The capability of a machine tool to produce a desired surface roughness with minimum power consumption depends on machining parameters, cutting phenomenon, workpiece properties, and cutting tool properties, etc. The first step towards reducing the power consumption and surface roughness in machining is to analyse the impact of machining parameters on power consumption and surface roughness. This paper aims at optimizing the power consumption and surface roughness simultaneously. Optimization of machining parameters through experimentation is a not only tedious but costly also, therefore, this paper presents a predictive mathematical model to optimize the power consumption and surface roughness simultaneously. The multi-objective predictive model has been developed using the grey relational analysis coupled with principal component analysis. The response surface methodology has been used to optimize the machining parameters to minimize the multi-objective function.

Section snippets

Literature review

Process models have often targeted the prediction of fundamental variables such as stresses, strains, strain rate, temperature, etc but to be useful for industry these variables must be correlated to performance measures and product quality (accuracy, dimensional tolerances, finish, etc) (Arrazola et al., 2013). Recent review papers on machining show that the most widely machining performances considered by the researchers are surface roughness followed by machining/production cost and material

Research methodology

The research carried out for this paper can be broadly divided into three phases – experimental planning; multi-objective predictive model formulation; and machining parameters optimization – followed by result confirmation using experimental studies as shown in Fig. 1.

In the first phase experimental plan was developed to select the machine tool, cutting tools, machining material, machining parameters and their levels, and performance characteristics (power consumption and surface roughness).

Selecting machining parameters and performance characteristics

The turning experiments were carried out in dry cutting conditions using a centre lathe, which has a maximum spindle speed of 2300 rpm and a spindle power of 5.5 kW. For increasing rigidity of the machining system, workpiece material was held between chuck and tailstock and the tool overhang was 20 mm. The choice of machining parameters was made by taking into account the capacity/limiting cutting conditions of the lathe, tool manufacturer's catalogue and the values taken by researchers in the

Calculating the grey relation co-efficient using GRA

The experimental results for the Ra and P are listed in Table 2. Preprocessing sequence (Table 3) was computed using Eq. (4) as both surface roughness and power consumption fit ‘the-smaller-the-better’ methodology. $x_{0}^{*} (k)$ shows the value for reference sequence and $x_{i}^{*} (k)$ for comparability sequence. The deviation sequence is computed using: $Δ_{01} (R_{a}) = | x_{0}^{*} (R_{a}) - x_{i}^{*} (R_{a}) | = | 1.0000 - 0.7780 | = 0.2220$ $Δ_{01} (P) = | x_{0}^{*} (P) - x_{i}^{*} (P) | = | 1.0000 - 1.0000 | = 0.0000$

Therefore the value of deviation sequence for comparability

Analysis of variance

The relative importance among the machining parameters (v, f, d) for the multiple performance characteristics (Ra and P) needs to be investigated so that the optimal parameters can be decided effectively. The analysis of variance (ANOVA) has been applied to investigate the developed model and the effect of machining parameters on the multi-objective function. Table 7 shows ANOVA results for the linear [v, f, d,] quadratic [v ², f ², d²] and interactive [(v × f), (v × d), (f × d)] factors.

Confirmation experiments

The confirmation experiments were conducted on the optimal machining parameters (v = 127.63 m/min, f = 0.12 mm/rev and d = 0.50 mm) predicted using the developed model. The result of the confirmation runs for the power consumption and surface roughness are listed in Table 8. It can be observed that the optimal machining parameters predicted by the developed model will lead to lower power consumption and better surface roughness as shown in Table 8.

The influence of machining parameters on power consumption and surface roughness

Machine tools consume power to provide the relative movement to the cutting tool with respect to the workpiece and rotation of spindle. The three machining parameters, viz. cutting speed, feed and depth of cut determine the material removal rate. As the feed and depth of cut increases, the undeformed chip section increases and hence the force required to remove this area also increases which forces the machine tool to consume more power. The surface roughness also increases with increase in

Conclusions

This paper presents a multi-objective predictive model for the minimization of power consumption and surface roughness during the machining of AISI 1045 steel. It has been observed that the predictive model provides optimum machining parameters. The predictive model has been found statistically significant using ANOVA. The results of the proposed model provide an improvement of 6.59% reduction in power consumption and 2.65% improvement in surface roughness over the best experimental run. It has

References (34)

A. Aggarwal et al.
Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique—a comparative analysis
J. Mater. Process. Technol.
(2008)
P.J. Arrazola et al.
Recent advances in modelling of metal machining processes
CIRP Ann. – Manuf. Technol.
(2013)
R.K. Bhushan
Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites
J. Clean. Prod.
(2013)
M.C. Cakir et al.
Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material
J. Mater. Process. Technol.
(2009)
G. Campatelli et al.
Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel
J. Clean. Prod.
(2014)
C. Camposeco-Negrete
Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA
J. Clean. Prod.
(2013)
M.H. Cetin et al.
Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method
J. Clean. Prod.
(2011)
M. Emami et al.
Investigating the minimum quantity lubrication in grinding of Al₂O₃ engineering ceramic
J. Clean. Prod.
(2014)
K. Fang et al.
A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction
J. Manuf. Syst.
(2011)
D. Fratila et al.
Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg 3
J. Clean. Prod.
(2011)

I. Hanafi et al.

Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools

J. Clean. Prod.

(2012)

Y. He et al.

A modeling method of task-oriented energy consumption for machining manufacturing system

J. Clean. Prod.

(2012)

H.S. Lu et al.

Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling

J. Mater. Process. Technol.

(2009)

S.T. Newman et al.

Energy efficient process planning for CNC machining

CIRP J. Manuf. Sci. Technol.

(2012)

F. Pusavec et al.

Transitioning to sustainable production – part I: application on machining technologies

J. Clean. Prod.

(2010)

M. Sarıkaya et al.

Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL

J. Clean. Prod.

(2014)

C.-J. Tzeng et al.

Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis

J. Mater. Process. Technol.

(2009)

Cited by (242)

AI-based optimisation of total machining performance: A review
2024, CIRP Journal of Manufacturing Science and Technology
Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP.
Unique cellular microstructure-enabled hybrid additive and subtractive manufacturing of aluminium alloy mirror with high strength
2023, Journal of Materials Processing Technology
The integrated design and manufacturing of aerospace metal optical mirrors can be challenging for rapid manufacturing with traditional techniques while maintaining mechanical integrity and meeting mass limitations. Hybridized additive/ultra-precision machining process is a promising approach to fabricate optical surfaces on AlSi10Mg alloy to meet the optical demands and mechanical performances. However, the fundamental investigation of ultra-precision machining of optical surfaces on additively manufactured AlSi10Mg alloy is still lacking, delaying the application of this technology. In this study, the microstructure, mechanical properties, and machinability of AlSi10Mg alloys produced by laser powder bed fusion (LPBF) were investigated, including the effect of heat treatment. A superior surface finish was achievable on the as-built alloy due to the unique fine cellular microstructure that aided the cutting process. Surfaces with roughness of ∼4 nm Ra and excellent mechanical properties with tensile strength of up to ∼491 MPa were obtained. In contrast, the large silicon particles that formed during heat treatment led to the deterioration of machined surface quality with an achievable roughness of ∼9 nm Ra along with the reduction in tensile strength to ∼283.5 MPa. Subsurface TEM analysis on the machined as-built sample indicated that the nano-scale silicon particles in the cellular microstructure boundary of the alloy and the semi-coherent relationship between silicon particles and the aluminum matrix avoided the generation of machined surface damages. New insights into the relationships between the additive manufacturing process, microstructure, and machinability are detailed to highlight the potential of hybrid manufacturing technology for high-strength and high-surface-quality parts.
Development of a cyber physical production system framework for 3D printing analytics[Formula presented]
2023, Applied Soft Computing
3D printing technology is considered one of the emerging areas to deal with global sustainability challenges and to facilitate the Industry 4.0 adoption. However, 3D printing technology is still immature due to several limitations and negative perceptions about its quality and performance. The goal of this paper is to propose a cyber physical production system (CPPS) framework for a 3D printer to (i) monitor the process, parameters, and carbon footprint, (ii) predict the nozzle’s remaining useful life (RUL), and (iii) prescribe optimum 3D printing parameters for minimizing carbon footprint and printing time, simultaneously at the targeted surface quality. Experiments were designed based on Taguchi L-27 orthogonal array to investigate the relationship between printing parameters and performance characteristics. The usefulness of the proposed framework has been demonstrated for a 3D printer to predict the remaining useful life of the printer nozzle (prognostic model), and to find an optimal combination of printing parameters for the simultaneous optimization of sustainability and productivity at the targeted surface quality (prescriptive model). Layer height was found to have a statically significant impact on the specific carbon footprint followed by scale and bed temperature. Layer height is the only statically significant contributor to the surface roughness of 3D printed parts. The scale and layer height followed by infill have significant effect on the printing time. The significance of the present work lies in enhancing the performance of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. The usefulness of the proposed CPPS framework is demonstrated as a decision support tool for a 3D printer real-time monitoring, visualization, and control. The proposed CPPS framework and its application for prognostic and prescriptive analytics is generic in nature, and is transferable and applicable to other FDM 3D printers, irrespective of brand and size.
Characterising surface roughness of Ti-6Al-4V alloy machined using coated and uncoated carbide tools with variable nose radius by machine learning
2023, Engineering Applications of Artificial Intelligence
A rich literature explores how cutting parameters influence surface roughness in machining different materials using two- or three-dimensional visualisations. Research on Ti-6Al-4V alloy, an important but difficult-to-machine material, is relatively nascent. This research conducts real-life turning operations for Ti-6Al-4V alloy using coated and uncoated carbide inserts with different nose radii and subsequently applies ML techniques to simulate and characterise surface roughness (Ra) for varying cutting parameters — cutting speed (v), feed rate (f), depth of cut (d). Additionally, it presents an innovative visualisation of all three machining parameters’ influences on the surface roughness for Ti-6Al-4V alloy. The results identify Support Vector Machine (SVM) with RBF kernel as a highly potential ML approach to characterise the impact compared to SVM with linear kernel, Random Forest, Deep Learning, and ensemble approaches. The results also demonstrate, for a low feed rate and cutting speed, the surface roughness is better irrespective of the depth of cut and cutting tool materials. For coated carbide with a 0.40 mm nose radius and feed rate of 0.10 mm/rev, for example, Ra values stay within the range of 0.68 to 1.16 m $μ$ for all experimented cutting speeds ranging from 50 m/min to 250 m/min. A similar outcome occurs for other cutting tools. The surface roughness tends to be higher for a higher feed rate. However, the Ra values can decrease even for a high feed rate if the cutting speed exceeds 200 m/min. Overall, the visualisation indicates a nonlinear relationship between machining parameters and surface roughness.
In pursuit of sustainability in machining thin walled α‑titanium tubes: An industry supported study
2023, Sustainable Materials and Technologies
Cryogenic machining has emerged as a major sustainable machining techniques especially for difficult-to-cut alloys, including titanium, Inconel alloys in terms of its impact on cost savings for high-value manufacturing projects reduced energy consumption, enhanced worker safety, and eliminating the infrastructure and disposal needed for flood coolants. However, further study is needed to quantify the sustainable elements of machining processes to have a clearer vision for industries to transition from traditional techniques to such advanced techniques. In the currently known literature, the topic of high-speed machining of titanium tubes with thin walls is yet to be explored under advanced cooling technique. The studies on the machinability indicators including tool wear, power consumption etc. are the backbone to the assessment of sustainability aspect of machining. The quantification of sustainability indicators in terms of machining costs and carbon emissions will provide better insights to the industries to maximise their profits and have least negative impacts on the environment. Thus, this study focuses on analyzing sustainability of the machining processes in terms of economic and environmental factors while turning thin walled α‑titanium tubes under different cutting environments which include dry, wet and cryogenic machining under two different high cutting speeds of 150 m/min and 200 m/min. The economical aspect comprises the assessment of the different machining costs occurring during the machining. The environmental aspect includes the assessment of carbon emissions occurring during the machining operation. The backbone of the sustainability analysis is the machinability analysis which involves the investigation of power consumption and cutting tool-wear. A comprehensive study of tool wear and total power consumption has been presented to get a better perspective in the machinability of thin walled α‑titanium tubes while working in the cryogenic environment in comparison with dry and wet environments. The total machining cost is relatively less (approximately 27%) under cryogenic environment in comparison with a wet environment making it a more economical process. The total emissions of carbon are also found to be lowered (up to 9%) while machining under cryogenic environment in comparison with a wet environment making it a more environmentally friendly process. The cutting tool wear is found to be relatively low (up to 38.68% and 72.24%) under cryogenic environment in comparison with wet and dry environments respectively. Power consumption was compared for the three different environments to have detailed analysis in terms of productivity and sustainability. A significant reduction in total power consumption (up to 19.25%) was observed under cryogenic environment in comparison with wet environment.
Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning
2023, Robotics and Computer-Integrated Manufacturing
Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric optimisation, significantly contributing to sustainable manufacturing.

View all citing articles on Scopus

View full text

Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining

Highlights

Abstract

Introduction

Section snippets

Literature review

Research methodology

Selecting machining parameters and performance characteristics

Calculating the grey relation co-efficient using GRA

Analysis of variance

Confirmation experiments

The influence of machining parameters on power consumption and surface roughness

Conclusions

J. Mater. Process. Technol.

CIRP Ann. – Manuf. Technol.

J. Clean. Prod.

J. Mater. Process. Technol.

J. Clean. Prod.

J. Clean. Prod.

J. Clean. Prod.

J. Clean. Prod.

J. Manuf. Syst.

J. Clean. Prod.

J. Clean. Prod.

J. Clean. Prod.

J. Mater. Process. Technol.

CIRP J. Manuf. Sci. Technol.

J. Clean. Prod.

J. Clean. Prod.

J. Mater. Process. Technol.