Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.
The present work concerns an experimental study of turning with coated cermet tools with TiCN-TiN coating layer of AISI 52100 bearing steel. The main objectives are firstly focused on the effect of cutting parameters and coating material on the performances of cutting tools. Secondly, to perform a Multi-objective optimization for minimizing surface roughness (Ra) and maximizing material removal rate by desirability approach. A mathematical model was developed based on the Response Surface Methodology (RSM). ANOVA method was used to quantify the cutting parameters effects on the machining surface quality and the material removal rate. The results analysis shows that the feed rate has the most effect on the surface quality. The effect of coating layers on the surface quality is also studied. It is observed that a lower surface roughness is obtained when using PVD (TiCN-TiN) coated insert when compared with uncoated tool. The values of root mean square deviation and coefficient of correlation between the theoretical and experimental data are also given in this work where the maximum calculated error is 2.65 %.
This paper describes a comparison of surface roughness between ceramics and cubic boron nitride (CBN7020) cutting tools when machining of AISI H11 hot work steels treated at 50 HRC. Plan is designed according to Taguchi’s L18 (21×32) orthogonal array. The response surface methodology (RSM) and analysis of variance (ANOVA) were used to check the validity of multiple linear regression models and to determine the effects, contribution, significance and optimal machine settings of process parameters, namely, cutting speed, feed rate and depth of cut on machining parameters on the Ra and Rt. The results of this research work showed that, the feed rate was found to be a dominant factor on the surface roughness, followed by the cutting speed, lastly the depth of cut. The CBN7020 cutting tool showed the better performance than that of ceramic based cutting tool. In addition, the combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness.
Nickel based super alloys are excellent for several applications and mainly in structural components submitted to high temperatures owing to their high strength to weight ratio, good corrosion resistance and metallurgical stability such as in cases of jet engine and gas turbine components. The current work presents the experimental investigations of the cutting parameters effects (cutting speed, depth of cut and feed rate) on the surface roughness, cutting force components, productivity and power consumption during dry conditions in straight turning using coated carbide tool. The mathematical models for output parameters have been developed using Box-Behnken design with 15 runs and Box-Cox transformation was used for improving normality. The results of the analysis have shown that the surface finish was statistically sensitive to the feed rate and cutting speed with the contribution of 43.58% and 23.85% respectively, while depth of cut had the greatest effect on the evolution of cutting force components with the contribution of 79.87% for feed force, 66.92% for radial force and 66.26% for tangential force. Multi-objective optimization procedure allowed minimizing roughness Ra, cutting forces and power consumption and maximizing material removal rate using desirability approach.
The present research work investigated the machining of AISI304 austenitic stainless steel in terms of machining force evolution, power consumption, specific cutting force and surface roughness where a factorial experiment design and analysis of variance technique were used and several factors were evaluated for their effects on each level. The case of dry turning process was studied based on design of experiments in order to obtain empirical equations characterizing material machinability according to cutting conditions such as cutting speed, feed rate and depth of cut and the latter ones were put in relationship with the machining output variables (Ra, Fc, Kc and Pc) through the response surface methodology (RSM). Results revealed that feed rate was the most preponderant factor affecting surface roughness (71.04%). However, the depth of cut affects considerably cutting force and cutting power by (60.74% and 67.11%), respectively. In addition, the specific cutting force was found affected significantly by cutting speed with a contribution of 41.43%. The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of machining parameters (104.54 m/min, 0.08 mm/rev and 0.295 mm).
A reverse supply chain is viewed as a process from consumption point to recovery point and the management of domestic waste is considered as a specific and complex reverse supply chain. This important sector represents a high challenging problem for our cities, constrained by financial, social, health and environmental considerations. This paper proposes multicriteria decision aid to help choose an efficient domestic waste management strategy. In fact, Multicriteria decision making techniques are considered as a key option to solve this type of problems, giving a solution that represents a good compromise between different preferences. The adopted approach consists in outranking a set of candidate management plans using a method based on partial aggregation criteria. This model is applied on a real case study of an Algerian city and to validate the obtained results, a deep sensitivity analysis is carried out, giving the most appropriate plans.
Skill management is a key factor in improving effectiveness of industrial companies, notably their maintenance services. The problem considered in this paper concerns scheduling of maintenance tasks under resource (maintenance teams) constraints. This problem is generally known as unrelated parallel machine scheduling. We consider the problem with a both objectives of minimizing total weighted tardiness (TWT) and number of tardiness tasks. Our interest is focused particularly on solving this problem under skill constraints, which each resource has a skill level. So, we propose a new efficient heuristic to obtain an approximate solution for this NP-hard problem and demonstrate his effectiveness through computational experiments. This heuristic is designed for implementation in a static maintenance scheduling problem (with unequal release dates, processing times and resource skills), while minimizing objective functions aforementioned.
This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD – coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.
In this paper, a method for severity fault diagnosis of ball bearings is presented. The method is based on wavelet packet transform (WPT), statistical parameters, principal component analysis (PCA) and support vector machine (SVM). The key to bearing faults diagnosis is features extraction. Hence, the proposed technique consists of preprocessing the bearing fault vibration signal using statistical parameters and energy obtained through the application of Db8- WPT at the third level of decomposition. After feature extraction from vibration signal, PCA is employed for dimensionality reduction. Finally, particle swarm optimization with passive congregation-based support vector machine is used to classify seven kinds of bearing faults. The classification results indicate the effectiveness of the proposed method for severity faults diagnosis in ball bearings.
This work illustrates the manufacturing of the honeycomb hexagonal topology structures by the kirigami technical, and the compressive testing of this specimen. The cellular configuration is simulated using a series of finite element models representing fullscale. The models are benchmarked against experimental results from pure compression tests. Finite element models of the honeycomb assemblies under compressive loading have been developed using nonlinear shell elements from an ANSYS code. Good agreement is observed between numerical nonlinear simulations and the experimental results.