Search results

The purpose of this paper is to investigate the influence of turning parameters such as cutting speed, feed rate and depth of cut on tool flank wear and machined surface quality of AISI 304 stainless steel during environment friendly turning under nanofluid minimum quantity lubrication (NMQL) conditions using PVD-coated carbide cutting inserts.

Turning experiments are conducted as per the central composite rotatable design under the response surface methodology. ANOVA and regression analysis are employed to examine significant cutting parameters and develop mathematical models for VB (tool flank wear) and R_a (surface roughness). Multi-response desirability optimization approach is used to investigate optimum turning parameters for simultaneously minimizing VB and R_a.

Optimal input turning parameters are observed as follows: cutting speed: 168.06 m/min., feed rate: 0.06 mm/rev. and depth of cut: 0.25 mm with predicted optimal output response factors: VB: 106.864 µm and R_a: 0.571 µm at the 0.753 desirability level. ANOVA test reveals depth of cut and cutting speed-feed rate interaction as statistically significant factors influencing tool flank wear, whereas cutting speed is a dominating factor affecting surface roughness. Confirmation tests show 5.70 and 3.71 percent error between predicted and experimental examined values of VB and R_a, respectively.

AISI 304 is a highly consumed grade of stainless steel in aerospace components, chemical equipment, nuclear industry, pressure vessels, food processing equipment, paper industry, etc. However, AISI 304 stainless steel is considered as a difficult-to-cut material because of its high strength, rapid work hardening and low heat conductivity. This leads to lesser tool life and poor surface finish. Consequently, the optimization of machining parameters is necessary to minimize tool wear and surface roughness. The results obtained in this research can be used as turning database for the above-mentioned industries for attaining a better machined surface quality and tool performance under environment friendly machining conditions.

Turning of AISI 304 stainless steel under NMQL conditions results in environment friendly machining process by maintaining a dry, healthy, clean and pollution free working area.

Machining of AISI 304 stainless steel under vegetable oil-based NMQL conditions has not been investigated previously.

This paper aims to focus on the application of multi-attribute decision-making methods (MADMs) to ascertain the optimal machining parameters while turning Ti-6Al-4V alloy under minimum quantity lubrication (MQL) conditions using Jatropha-curcas oil (JCO) bio-based lubricant.

The experiments were designed and performed using Taguchi L27 design of experiments methodology. A total of 27 experiments were performed under MQL conditions using textured carbide cutting tools on which different MADMs like Analytic hierarchy process (AHP), Technique for order preference by similarity to ideal solution (TOPSIS) and Simple additive weighting (SAW) were implemented in an empirical manner to extract optimize machining parameters for turning of Ti-6Al-4V alloy under set of constrained conditions.

The results evaluated through MADMs exhibit the optimized set of machining parameters (cutting speed V_c = 80 m/min, feed rate f = 0.05 mm/rev. and depth of cut a_p = 0.10 mm) for minimizing the average surface roughness (Ra), maximum flank wear (Vbmax), tangential cutting force (Fc) and cutting temperature (T). Further, analysis of variance (ANOVA) and traditional desirability function approach was applied and results of TOPSIS and SAW methods having optimal setting of parameters were compared as well as confirmation experiments were conducted to verify the results. A SEM analysis at lowest and highest cutting speeds was performed to investigate the tool wear patterns. At the highest speed, large cutting temperature generated, thereby resulted in chipping as well as notching and fracturing of the textured insert.

The research paper attempted in exploring the optimized machining parameters during turning of difficult-to-cut titanium alloy (Ti-6AL-4V) with textured carbide cutting tool under MQL environment through combined approach of MADMs techniques. Ti-6Al-4V alloy has been extensively used in important aerospace components like fuselage, hydraulic tubing, bulk head, wing spar, landing gear, as well as bio-medical applications.

This study aims to use nanofluid-based minimum quantity lubrication (NMQL) technique to minimize the use of cutting fluids in machining of Inconel-625 and Stainless Steel 304 (SS-304) (Ni-Cr alloys).

Machining of Ni-Cr-based alloys is very challenging as these exhibit lower thermal conductivity and rapid work hardening. So, these cannot be machined dry, and a suitable cutting fluid has to be used. To improve the thermal conductivity of cutting fluid, multi-walled carbon nanotubes (MWCNTs) were added to the soybean oil and used with MQL. This study attempts to compare tool wear of coated carbide inserts during face milling of Inconel-625 and SS-304 under dry, flooded and NMQL conditions. The machining performance of both materials, i.e. Inconel-625 and SS-304, has been compared on the basis of tool wear behavior evaluated using scanning electron microscopy-energy dispersive spectroscopy.

The results indicate higher tool wear and lower tool life during machining of Inconel-625 as compared to SS-304. Machining of Inconel-625 exhibited non-consistent tool wear behavior. The tool failure modes experienced during dry machining are discrete fracture, cracks, etc., which are completely eliminated with the use of NMQL machining. In addition, less adhesion wear and abrasion marks are noticed as compared to dry and flooded machining, thereby enhancing the tool life.

Inconel-625 and SS-304 have specific applications in aircraft and aerospace industry, where sculptured surfaces of the turbine blades are machined. The results of current investigation will provide a rich data base for effective machining of both materials under variety of machining conditions.

The literature review indicated that majority of research work on MQL machining has been carried out to explore machining of Ni-Cr alloys such as Inconel 718, Inconel 800, AISI4340, AISI316, AISI1040, AISI430, titanium alloys, hardened steel alloys and Al alloys. Few researchers have explored the suitability of nanofluids and vegetable oil-based cutting fluids in metal cutting operation. However, no literature is available on face milling using nanoparticle-based MQL during machining Inconel-625 and SS-304. Therefore, experimental investigation was conducted to examine the machining performance of NMQL during face milling of Inconel-625 and SS-304 by using soybean oil (vegetable oil) with MWCNTs to achieve ecofriendly machining.

Drawing upon resource-based view (RBV) and attribution theoretical lenses, this chapter provides a paradigm for examining the interplay among environmental investment towards green innovation, environmental disclosure as well as firm performance using the structural equation modelling (SEM) methodology. This chapter demonstrate a growing environmental awareness among stakeholders of the relevance of environmental performance to share value. It is also suggested that the mediating power of environmental disclosure between environmental investment and firm value as well as incremental goodwill is crucial. The findings of this chapter provide critical implications for several stakeholders that if environmental performance is hypothesised to affect the firm's value, companies may take proactive measures to avert potential environmental-related violations. Besides, investors may trade based on the evidence as to how firm value and its goodwill from acquisition will be affected by news of its environmental performance.

The purpose of this paper is to make out a complete solution to logistic outsourcing problem in a medium‐scale organization by using consistent fuzzy preference relation (CFPR) and vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) method.

The initial approach to this research was to develop a comprehensive framework for logistic outsourcing problem and selection of most appropriate third party logistic (3PL) provider.

It has been found that the organization should outsource logistic activities. The alternatives (3PL providers) have also been ranked and the fifth 3PL provider has been termed as best third party logistic provider.

The parameters selected for this study and developed framework are applicable only to a medium‐scale organization manufacturing automobile parts in northern India.

This is probably the first time that an attempt has been made to apply the two‐phase methodology approach, using CFPR and VIKOR, to analyze a multi‐criteria logistic outsourcing problem. A case is provided which demonstrates how to solve logistic outsourcing, a multi‐criteria decision‐making problem.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

The purposes of this study were to formulate a hierarchical table of factors that influence adoption of an inter-organization system (IOS) by enterprises and to apply multi-criteria decision-making (MCDM) tools to find the weights of these factors and to objectively identify the critical success factors (CSFs) for the adoption of IOSs by small- and medium-sized enterprises (SMEs).

This study first used a literature review to collect the factors that affect an enterprise’s adoption of an IOS and then constructed a three-level hierarchical table of these factors, based on a technology – organization – environment framework. Fuzzy analytic hierarchy processing was used, based on the returned questionnaires, to determine the weights of the factors. The concept of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) acceptable advantage was used to objectively identify the CSFs of SMEs that have adopted an IOS.

This study identifies six CSFs of SMEs that have adopted an IOS: industry knowledge and experience, the degree of application of information technology within the industry, system safety, the organizational infrastructure, customer relationships and ease of use. In addition, four findings are proposed.

The work has studied, in depth, the factors that influence the adoption of an IOS by SMEs and identified four practice implications that provide a useful guideline for SMEs when they plan to adopt an IOS.

The identification of CSFs is also an MCDM problem. However, very few previous articles have used MCDM tools to identify the CSFs. This study adopted MCDM tools to objectively identify these CSFs and determine their appropriate weights. The results can help the managers of SMEs allocate their resources, according to the weighting of these CSFs, when they are making plans to adopt an IOS.