An integrated vendor–buyer cooperative inventory model in an imperfect production process with shortage backordering

In this paper, we develop an integrated inventory model for vendor–buyer coordination under an imperfect production process. The proportion of defective items in each production lot is assumed to be stochastic and follows a known probability density function. The vendor inspects the items while they are being produced and delivers good-quality items to the buyer in small lots over multiple shipments. We assume that shortages are permitted and are completely backordered. The expected annual integrated total cost is derived and a solution procedure is provided to find the optimal solution. Numerical examples show that the integrated model gives an impressive cost reduction in comparison to an independent decision by the buyer. The results also show that if back orders are permitted, the expected integrated annual cost is less than that when back orders are not permitted. Therefore, if customers are willing to wait for the next shipment when a shortage occurs, it is profitable for the company to allow back orders although the delay incurs a penalty cost.     

An integrated vendor–buyer inventory model with imperfect items and planned back orders

In this paper, we develop a mathematical model to determine an integrated vendor–buyer inventory policy, where the vendor’s production process is imperfect and produces a certain number of defective items with a known probability density function. The vendor prepares for the repeating flow of orders of size $ {Q_{\mathrm{P}}}=nQ $ from the buyer by producing items in batches of size Q _P and planning to have each batch delivered to the buyers in n deliveries, each with a lot of Q units. Once the buyer receives the items, a 100 % screening process is conducted. We assume the screening process and demand take place simultaneously. We also assume that shortages are allowed and are completely back ordered. The objective is to minimize the total joint annual costs incurred by the vendor and the buyer. The expected annual integrated total cost is derived and a solution procedure is provided to find the optimal solution. Numerical examples show that the integrated model gives an impressive cost reduction in comparison to an independent decision by the buyer. The results also show that even though there is a cost associated with each back order, it is profitable for the company to have planned back orders if customers are willing to wait for the next delivery when a shortage occurs.     

Integrated vendor–buyer cooperative inventory model with controllable lead time, ordering cost reduction, and service-level constraint

In this study, an integrated vendor–buyer inventory model is proposed. The vendor produces a product in a batch environment and delivers it to a buyer facing normally distributed demand. In the current study, several assumptions are used that the combination of these assumptions has not been studied in previous papers. Here is the list of these assumptions: service-level constraint instead of shortage cost is used; ordering cost reduction is allowed, and decreasing lead time by a cost dependent on two factors including shortened lead time and ordering quantity is considered. An algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the results of the model. Also, the effects of assumptions used in this paper are evaluated and discussed.     

Decision making in production on the basis of structure–strategy theory

The formulation of adequate requirements in manufacturing is outlined on the basis of structure–strategy theory.     

In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals

In this work, an attempt has been made to use vibration signals for in-process prediction of surface roughness during turning of Ti–6Al–4V alloy. The investigation was carried out in two stages. In the first stage, only acceleration amplitude of tool vibrations in axial, radial and tangential directions were used to develop multiple regression models for prediction of surface roughness. The first and second order regression models thus developed were not found accurate enough (maximum percentage error close to 24%). In the second stage, initially a correlation analysis was performed to determine the degree of association of cutting speed, feed rate, and depth of cut and the acceleration amplitude of vibrations in axial, radial, and tangential directions with surface roughness. Subsequently, based on this analysis, feed rate and depth of cut were included as input parameters aside from the acceleration amplitude of vibrations in radial and tangential directions to develop a refined first order multiple regression model for surface roughness prediction. This model provided good prediction accuracy (maximum percentage error 7.45%) of surface roughness. Finally, an artificial neural network model was developed as it can be readily integrated into a computer integrated manufacturing environment.     

Design of a fractional order PID controller using GBMO algorithm for load–frequency control with governor saturation consideration

Load–frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load–frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load–frequency control system in confronting with model parameters variations.     

Generation,validation and application of dynamic load profiles in flow measurement using cavitating Herschel–Venturi nozzles

Today, utility meters for water are tested for measurement behavior at stable operating conditions at specified flow rates as part of the approval process. The measurement error that occurs during start and stop or when changing between flow rates may not be taken into account. In addition, there are new technologies whose measuring behavior under real-world conditions is only known to a limited extend. To take these facts into account, a new method has been developed and tested to determine the measurement behavior of water meters under dynamic load profiles as they occur in the real application. For this purpose, a test rig for flow rate measurement was extended by a cavitation nozzle apparatus and the generation of dynamic load profiles was validated. For the cavitation nozzles used, possible factors influencing the flow rate, such as temperature and purity of the water as well as the upstream pressure were investigated. Using different types of domestic water meters, the applicability of the dynamic test procedure was demonstrated and the measurement behavior of the meters was characterised.     

Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using Nelder–Mead and genetic algorithm methods

Machining of composite materials is an important and current topic in modern researches on manufacturing processes. Determination of optimal cutting parameters is one of the most important elements in the machinability study of composites. Optimization has significant practical importance particularly for operating the machineries. In order to increase the accuracy of drill holes, the tool must be in good condition always as much as possible. To achieve good condition of tool, the optimization of machining parameters like drill bit diameter, spindle speed, and feed rate are mandatory. The objective of this paper is to study the effect of these process parameters on thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites. The optimal settings of the parameters were determined through experiments planned, conducted, and analyzed using the Box–Behnken design, Nelder–Mead, and genetic algorithm methods. This paper also aimed to increase the cutting condition of tool, i.e., minimization of tool wear by applying the optimized input parameters using Nelder–Mead and genetic algorithm techniques.     

Study of performance and emission characteristics of IC engines by using diesel–water emulsion

Due to the shortage of petroleum products and its increasing cost, efforts are on to develop alternate fuels, especially diesel oil, for partial or full replacement. Also, internal combustion engines generate undesirable emissions during combustion process. The emissions exhausted in to the surroundings pollute the atmosphere and causes several problems. The emissions of concern are: unburnt hydrocarbons, oxides of carbon, and oxides of nitrogen (NO_X). Advanced diesel fuel formulations offer significant emission reductions to new and older in-use engines every time the fuel tank is filled. The addition of water to diesel fuel lowers particulate emissions by serving as diluents to the key combustion intermediates that lead to particulate formation. The incorporation of water also reduces NO_X emissions by lowering the peak combustion temperatures through high heat of vaporization. When using water blend diesel, the engine fuel system recognizes the liquid as diesel fuel because the water droplet is encapsulated within a diesel fuel. In this experiment, we have used single cylinder four-stroke engine and the water-blend diesel emulsion is used and the diesel emission test, emulsion emission test, and various gases has been analyzed; smoke meter test is also conducted for various rate of loads. The test results from the engine fuelled with water-blend diesel showed reduction in emissions as compared to that of engine fuelled with conventional diesel. The better emissions in the CI engine using water-blend diesel is due to the incorporation of water which reduces NO_X emissions by lowering the peak combustion temperatures. Water-blend fuel enhances fuel atomization by micro-explosion. The addition of water to diesel fuel lowers particulate emissions by serving as diluents to the key combustion intermediates that lead to particulate formation     

Identification and quantification of β-caryophyllene in copaiba oil using Raman spectroscopy

Copaiba oleoresin presents several compounds with known biologic activity and physiologic effects, including analgesic and insecticide properties. Among them are the terpenoids (mainly diterpenes and sesquiterpenes) with β-caryophyllene, the main representative of the terpenoids and considered to be a chemical marker. This study employed Raman spectroscopy and principal component analysis (PCA) techniques to identify and quantify the β-caryophyllene marker in copaiba oil samples purchased from popular markets in Brazil. A dispersive Raman spectrometer (830?nm, 250?mW, 2?cm^?1 spectral resolution) was used. Results showed the identification of the main Raman peaks from the β-caryophyllene in copaiba oil samples (main peaks at 507, 771, 1442, 1638, and 1673?cm^?1). The loading vector 2 (PC2) extracted the spectral information from β-caryophyllene in the samples and the eigenvalue 2 (score 2) allowed the estimation of the concentration of this marker in commercial samples, with the concentrations from 15 to 34%. Raman spectroscopy combined with PCA may be considered to be a potential analytical tool for the quality control of Copaifera oil samples by quantifying β-caryophyllene using its unique spectral information.     

Mapping of mechanical properties of WC–Co using nanoindentation

High-resolution measurements of mechanical properties are of immense importance in metallurgy. Measuring the intrinsic properties of each phase separately in multiphase materials gives information that is valuable for the development of new materials and for modelling. In this work, nanoindentation has been used to reveal mechanical properties of different phases in WC–Co with very high resolution. The resolution limits of the equipment and this material as well as various techniques to accomplish the measurements were evaluated. By making indents only 0.1 μm apart and using very low loads (1 mN) it was possible to distinguish between the two different phases, WC and Co. Maps created from the measured properties, hardness and Young’s modulus, were in excellent agreement with SEM images of the same area. Furthermore, it was also possible to detect an increased hardness of the Co binder phase by a factor of four as compared to the bulk hardness of Co. This work verifies experimentally what several authors have proposed earlier based on modelling. This revised version was published online in August 2006 with corrections to the Cover Date.     

Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas–liquid two-phase flows

In this study, a simple detection system comprised of one ⁶⁰Co source and just one NaI detector was investigated in order to identify flow regime and measure void fraction in gas–liquid two phase flows. For this purpose, 3 main flow regimes of two-phase flows including stratified, homogenous and annular with void fractions in the range of 5–95% were simulated by Monte-Carlo N Particle (MCNP) code. At first step, 3 features (count under full energy peaks of 1.173 and 1.333 MeV, and count under Compton continuum) were extracted from registered gamma spectrum. These 3 extracted features were used as inputs of artificial neural network (ANNs). A primary network was trained for identifying the flow regimes, but after testing many different structures, it was found that just two regimes of stratified and annular could be completely identified from each other. After identifying the mentioned two flow regimes by the first ANN, two specific ANNs were also implemented for predicting the void fraction. Using the proposed method in this work, void fraction percentages were predicted with a mean relative error (MRE) of less than only 0.42%. Using fewer detectors is of advantage in industrial nuclear gauges, because of reducing economical expenses and also simplicity of working with these systems.     

Multiple quality optimization in laser cutting of difficult-to-laser-cut material using grey–fuzzy methodology

This paper investigates the laser cutting performance of 1 mm Duralumin sheet with the aim to improve quality of cut by simultaneously optimising multiple performances such as cut edge surface roughness, kerf taper and kerf width. The experimental data obtained by Taguchi methodology-based L₂₇ orthogonal array experimentation have been used in the hybrid approach optimization of grey relational analysis and fuzzy logic theory. The predicted optimum results have been verified by conducting confirmation experiments. The verification results show an overall improvement of 19 % in multiple quality characteristics. The effects of significant factors on quality characteristics have also been discussed.     

Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.     

Analysis of dry sliding friction and wear behaviour of AA6351–SiC–B4C composites using grey relational analysis

Abstract

This paper describes the multifactor based experiments that are applied to investigate the dry sliding wear system of aluminium matrix alloy (AA6351) with 5 wt-% silicon carbide (SiC), 5 wt-% and 10 wt-% of boron carbide (B₄C) reinforced metal matrix composites (MMCs). Stir casting route was adopted to prepare the composites and the tribological experiments were carried out on pin-on-disc type wear machine. The effects of parameters like applied load, sliding velocity, wt-% of B₄C on the dry sliding wear and frictional coefficient of aluminium MMCs using grey relational analysis (GRA) are reported. The orthogonal array with L9 layout and analysis of variance were used to investigate the influence of the parameters. It is observed that the dry sliding friction and wear behaviour of the composites are influenced by the applied load, sliding velocity and wt-% of B₄C with a contribution of 60·82%, 21·72% and 14·28% respectively. The optimal design parameters were found by grey relational grade and a good agreement was observed for 95% level of confidence.     

Role of Third Bodies in Friction and Wear of Cold-Sprayed Ti and Ti–TiC Composite Coatings

Titanium (Ti) and Ti-based alloy wear performance is often poor unless coating or lubricants are used. An alternative is to use hard phase reinforcement. Cold spray is a relatively new method to deposit composite coatings, where here we report the deposition of a Ti–TiC coating and its sliding wear behavior. Mixtures of mechanically blended Ti–TiC with various TiC content were injected into a de Laval nozzle and sprayed onto substrates. Two composite coatings and a pure Ti coating are reported here, where the as-sprayed compositions of the composites were 13.8 and 33.4 vol% TiC. Reciprocating dry sliding wear was performed using a custom-built in situ tribometer. All tests were conducted with a sliding speed of 3 mm/s and at a normal load of 0.5 N. Using a transparent sapphire hemisphere of 6.25 mm as counterface, dynamic behavior of third bodies was directly observed. It was found that adhesive transfer of Ti was the primary wear mechanism for the Ti coating, with oxidative and abrasive wear also occurring for longer sliding cycles. The superior wear resistance of the composite coatings compared to Ti was related to dual function of TiC particles, where they reinforced the Ti matrix and facilitated the formation of a stable and protective tribofilms. The tribofilms contained carbonaceous material that provided easier shear and lower friction. The formation of these tribofilms was highly dependent on the TiC particles, which contained excess carbon compared to the equilibrium composition. Higher TiC content was more effective in quickly developing and sustaining the tribofilms.     

Investigating the Machinability of Al–Si–Cu cast alloy containing bismuth and antimony using coated carbide insert

Surface roughness and cutting force are two key measures that describe machined surface integrity and power requirement evaluation, respectively. This investigation presents the effect of melt treatment with addition of bismuth and antimony on machinability when turning Al–11%Si–2%Cu alloy. The experiments are carried out under oblique dry cutting conditions using a PVD TIN-coated insert at three cutting speeds of 70, 130 and 250 m/min, feed rates of 0.05, 0.1, 0.15 mm/rev, and 0.05 mm constant depth of cut. It was found that the Bi-containing workpiece possess the best surface roughness value and lowest cutting force due to formation of pure Bi which plays an important role as a lubricant in turning process, while Sb-containing workpiece produced the highest cutting force and highest surface roughness value. Additionally, change of silicon morphology from flake-like to lamellar structure changed value of cutting force and surface roughness during turning.     

Dielectric and Optical Properties of Strontium–Barium Niobate Films in the Range of 0.2–1.3 THz

The knowledge of optical and dielectric properties of ferroelectric films, in particular, strontium–barium niobate films, in the terahertz spectral range is needed to use these films as a basis of active elements and structures for detection and control of terahertz radiation. The properties of strontium–barium niobate films with x = 0.5 grown on oriented sapphire substrates with a deposited electrode are studied by the method of terahertz time-domain spectroscopy in the spectral range of 0.2–1.3 THz. It is found that strontium–barium niobate films can be used to develop devices for detection and control of terahertz radiation.