Integrated configurable equipment selection and line balancing for mass production with serial–parallel machining systems

Solving equipment selection and line balancing problems together allows better line configurations to be reached and avoids local optimal solutions. This article considers jointly these two decision problems for mass production lines with serial–parallel workplaces. This study was motivated by the design of production lines based on machines with rotary or mobile tables. Nevertheless, the results are more general and can be applied to assembly and production lines with similar structures. The designers’ objectives and the constraints are studied in order to suggest a relevant mathematical model and an efficient optimization approach to solve it. A real case study is used to validate the model and the developed approach.     

Harnessing value in knowledge management for performance in buyer–supplier collaboration

This study aims to investigate how different knowledge-management processes (i.e. knowledge acquisition and dissemination) affect the manufacturers’ performance in collaborative economic exchanges with their suppliers. Drawing upon the knowledge based view and transaction cost economics, this study proposes that knowledge-management processes are positively related to the performance of the manufacturers in a collaborative buyer–supplier relationship. It also posits that this link is stronger when the levels of supply-chain integration and relational stability are higher rather than lower. The findings of this study show strong support for these propositions. Theoretical and practical contributions of this study are also addressed.     

ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler

An artificial neural network (ANN) and genetic algorithm (GA) approach to predict NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas, is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature, and nozzle tilt were studied. The data collected from parametric field experiments was used to build a feed-forward back-propagation neural net. The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load conditions and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in GA. The genetic search was used to find the optimum level of input operating conditions corresponding to low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.     

Statistics for estimating the population average of a Lifshitz–Slyozov–Wagner (LSW) distribution

A generic version of the Lifshitz–Slyozov–Wagner (LSW) distribution is introduced. Using this generic form, a maximum likelihood (ML) estimator for the population average has been developed. The statistical properties of the estimates obtained by the ML method, as well as the conventional sample average method, have been assessed by Monte Carlo simulations for seven sample sizes ranging from 10 to 1000. Results showed that (i) both estimators yield practically unbiased results, (ii) the standard deviation of estimates obtained by the ML method is significantly less than that of the sample averages, (iii) the distribution of estimates is neither normal, lognormal nor 2-parameter Weibull. Percentage points of the distribution of estimates for both methods have been developed. The use of these points for calculating confidence limits for the population average of the LSW distribution is demonstrated by examples in this article.     

Improving the machining performance characteristics of the µEDM drilling process by the online cryogenic cooling approach

Productivity and surface quality would significantly affect the performance of the micro electrical discharge machining process (µEDM). Thus, the machining performance would be enhanced by improving the material removal rate (MRR) and surface quality. In this investigation, cryogenic LN₂ cooling was introduced to the conventional µEDM setup for developing an innovative process of cryogenically cooled µEDM process (CµEDM). The favorable outcomes of this process were estimated by selecting discharge current (I_p) and pulse on duration (T_on) for determining the effects of the machining performance including MRR and surface integrity. Surface quality was also analyzed by microstructural analysis and a scanning electron microscope (SEM) for evaluating the effects of the cryogenically cooled µEDM process. The experimental result shows 54–62% improvement in MRR and 22–36% improvement in average roughness values. Hence, it is suggested that cryogenically cooled µEDM facilitates improvement in productivity and surface quality.     

Optimization of Machining Characteristics for Al／SiCp Composites using ANN／GA

The present work is focused on optimization of machining characteristics of Al/SiCp composites.The machining characteristics such as specific energy,tool wear and surface roughness were studied.The parameters such as volume fraction of SiC,cutting speed and feed rate were considered.Artificial neural networks(NN) was used to train and simulate the experimental data.Genetic algorithms(GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics .Validation of optimized results was also performed by confirmation experiments.     

A job‐plan based performance evaluation for construction value engineering study

Abstract

Value Engineering can reduce costs and enhance the value of projects. Currently, the acceptance rate of Value Engineering Study (VES) recommendations and total potential savings are considered the principal performance indicators of a VES. In reality the success of a VES depends on numerous interrelated factors not easy for a VES team to control. However, a study team can ensure the success of a VES by fully implementing a VES job plan. Thus, realizing a job plan is a principal contribution toward insuring the success of the VES. This study presents a novel model that is able to evaluate the performance of a VES for a construction project. Two questionnaires, distributed to VE experts, were used to collect data needed for this research. Factor analysis, AHP, and SWAM were applied to build the VES performance evaluation model which consists of six evaluation groups and thirty‐two evaluation items. A real‐life VES for a construction project was then analyzed using the proposed model to demonstrate its usefulness in performance assessment. Defects of the VES were identified and suggestions were provided for the particular VES. The proposed model can be used by VES teams for self‐diagnosis, improvement, and motivation in achieving enhanced performance.     

Photocatalytic performance of AgCl@Ag core–shell nanocubes for the hexavalent chromium reduction

Plasmonic nanomaterials have wide applications in many fields, such as photocatalysis, solar cell, and new energy generation system. Among them, AgCl–Ag nanomaterials with excellent plasmonic property are getting more and more attention. In this work, AgCl@Ag core–shell nanocubes (AgCl@Ag CS-NCs) with highly uniform morphology and controlled composition have been successfully prepared by the partial reduction of AgCl nanocubes. The morphology, composition, and structure of AgCl@Ag CS-NCs have been investigated systematically. AgCl@Ag CS-NCs with the ratio of AgCl/Ag?=?2.7:1 show the optimizational photocatalytic activity for the reduction of representative carcinogenic contaminant (Cr^VI), which is much higher than that of commercial P25 photocatalyst. The localized surface plasmon resonance and Schottky junction of AgCl@Ag CS-NCs contribute to the high photocatalytic activity for the Cr^VI photoreduction. After 5 times recyclable catalysis, the high photocatalytic activity of AgCl@Ag CS-NCs can still be maintained well, which demonstrates that AgCl@Ag CS-NCs possess excellent reusability and stability. The high activity and durability make AgCl@Ag CS-NCs to become a promising candidate for the environmental pollution purification under the sunlight irradiation condition.     

An Integrated Model for an Industrial Production–Distribution Problem

In this paper, we are interested in a multi-facility, multi-product and multi-period industrial problem. In this problem, both production and distribution costs are significant and they are inter-related. Therefore they should be considered simultaneously in a cost optimization problem. We model this combined production-distribution problem in the form of a network flow problem with relatively few additional 0-1 variables describing the linking constraints between periods. Computational experiments show that the real size problems we encountered can be solved in reasonable time using commercial linear programming codes like CPLEX     

Oxidation or oxide materials in the machining zone in superabrasive grinding—A factor of influence on the grinding performance

To enhance grinding performance the authors consider the application of some indirect measures to stimulate oxidation processes as well as the use of oxides and oxidation-prone materials in the wheel working layer, because the direct introduction of oxygen to the machining zone, which is an important factor to change tribotechnical characteristics of the contact surfaces, presents certain difficulties.     

Improving the performance of small Joule–Thomson cryocooler

This paper presents a new cycle for improving the performance of small Joule–Thomson (JT) cryocooler by applying an additional ejector in cycle system. Based on the presented JT cycle with an additional ejector (JTE), the performance of small JT cryocooler operating with pure N₂ and the mixture N₂?CH₄ for cryogenic applications in a temperature range of 80–130 K is investigated by theoretical calculations. It can be found that the refrigeration efficiency of the JTE cryocooler operating with pure N₂ increase by 55.6–25.1%, and the refrigeration capacity increase by a factor of 4.9–1.5 compared to a conventional JT cryocooler. Similar performance improvements are also obtained when the mixture N₂?CH₄ is used as the working gas. The refrigeration efficiency increase by 46.6–17.7% and the refrigeration capacity increase by a factor of 3.8–1.3. It is concluded that the performance of small JT cryocooler can be significantly improved based on the presented JTE cycle.     

The use of wire mesh–epoxy composite for enhancing the flexural performance of concrete beams

This paper presents a study of an ongoing research project on the use of new composites for enhancement of the performance of concrete beams. A plain concrete beam was externally bonded with wire mesh–epoxy composite using one to five wire mesh layers. The flexural performance of the beam specimens bonded with wire mesh layers was compared with the beam specimens bonded with carbon fibre as well as a hybrid of wire mesh–epoxy–carbon fibre composite. The test results show that the use of wire mesh with epoxy is an efficient way to improve the flexural performance of concrete beam specimens. The increase in wire mesh layers significantly enhances the flexural strength, cracking behaviour and energy absorption capability. In comparison with carbon fibre, wire mesh–epoxy composite is more efficient in flexural strength and ductility. In addition, it was found that a concrete beam bonded with a hybrid wire mesh–epoxy–carbon fibre composite has significantly more energy absorption capability compared to specimens bonded with only carbon fibre.     

Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting

Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment. Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction.     

Integrated information databases — One- stop chemical research for the information specialist

With the burgeoning number of online files provided by an increasing number of vendors, information specialists will need more sophisticated capabilities to retrieve online information. Integrated databases provide a means for online searchers to retrieve information holistically. Three online developments by vendors will improve information retrieval: (1) simultaneous multi-file searchers; (2) crossover between separate files; (3) tagged retrievals that correlate to like records in other files. Simplicity in software design tends to engender rigid systems, such as menu-driven software, while flexibility for searching encourages complexity in software design. Vendors will need to balance these elements in integrating databases. Where should the information industry be going to make life easier for the information specialist and end user? Combined full-text, bibliographic, and numeric files; dictionary and thesaurus files for term and strategy generation; and artificial intelligence interfaces will be part of the new systems that facilitate searching in the future.     

A highly accurate differential evolution–particle swarm optimization algorithm for the construction of initial value problem solvers

In this work a new evolutionary computation technique is introduced for the construction of initial value solvers based on Runge–Kutta (RK) pairs. The derivation of RK pairs corresponds to solving a nonlinear optimization problem with a multimodal objective function in a high dimensional search space; additional difficulty stems from the fact that only solutions with accuracy at least equal to machine epsilon are acceptable. The proposed approach involves hybridizing a Differential Evolution (DE) strategy with elements from Particle Swarm Optimization (PSO) in order to produce a method for solving optimization problems with high accuracy. The resulting methodology is applied to two different problems of RK pair derivation of orders 5 and 4 and compared with standard DE techniques. Numerical experiments show that the proposed hybrid DE-PSO satisfies the strict accuracy requirements imposed by the particular problem, while outperforming its rivals.     

Critical assessment of the fatigue performance of additively manufactured Ti–6Al–4V and perspective for future research

To realize the potential benefits of additive manufacturing technology in airframe and ground vehicle applications, the fatigue performance of load bearing additively manufactured materials must be understood. Due to the novelty of this rapidly developing technology, a very limited, yet swiftly evolving literature exists on the topic. Motivated by these two points, we have attempted to catalog and analyze the published fatigue performance data of an additively manufactured alloy of significant technological interest, Ti–6Al–4V. Focusing on uniaxial fatigue performance, we compare to traditionally manufactured Ti–6Al–4V, discussing failure mechanisms, defects, microstructure, and processing parameters. We then attempt to identify key knowledge gaps that must be addressed before AM technology can safely and effectively be employed in critical load bearing applications.     

Electrolytic concentration effect on the abrasive assisted-electrochemical machining of an aluminum–boron carbide composite

All engineering materials can be machined by one or combination of processes in such a way that the material’s potential is fully exploited. Electrochemical machining is found to be a most promising process that produces various components from the hard-to-machine materials for the various applications. Electrolyte concentration is playing a positive role by improving the electrolyte conductivity, but negatively forming the passivation layer on the cut surfaces. In order to improve the surface finish and removal of generated residual materials from the cut surfaces, abrasive particles were fed along with electrolyte into the machining zone. This present paper investigates the sodium chloride (NaCl) electrolyte with varied concentration (10–30%) in association with SiC abrasive particles on the material removal rate, surface roughness, and radial overcut while machining of aluminum 6061–boron carbide (5–15?wt%) composites. This study conclusively derived that electrolyte concentration up to 20% exhibited a positive role in the material removal rate for the machining of composites because the rate of dissolution was of higher magnitude. Externally supplied abrasive particles along with electrolyte reduced the surface roughness and radial over cut to an extent. Conversely, at higher electrolyte concentration, the externally supplied abrasive particles have a little effect on the removal of the formed passivation layer as confirmed by SEM analysis.     

Evaluating the performance of the ORTEC® Detective™ for emergency urine bioassay

The performance of the ORTEC(?) Detective? as a field deployable tool for emergency urine bioassay of (137)Cs, (60)Co, (192)Ir, (169)Yb and (75)Se was evaluated against ANSI N13.30. The tested activity levels represent 10 % RL (reference level) and 1 % RL defined by [Li C., Vlahovich S., Dai X., Richardson R. B., Daka J. N. and Kramer G. H. Requirements for radiation emergency urine bioassay techniques for the public and first responders. Health Phys (in press, 99(5), 702-707 (2010)]. The tests were conducted for both single radionuclide and mixed radionuclides at two geometries, one conventional geometry (CG) and one improved geometry (IG) which improved the MDAs (minimum detectable amounts) by a factor of 1.6-2.7. The most challenging radionuclide was (169)Yb. The measurement of the mixture radionuclides for (169)Yb at the CG did not satisfy the ANSI N13.30 requirements even at 10 % RL. At 1 % RL, (169)Yb and (192)Ir were not detectable at either geometry, while the measurement of (60)Co in the mixed radionuclides satisfied the ANSI N13.30 requirements only at the IG.     

On the beat of the drum: improving the flow shop performance of the Drum–Buffer–Rope scheduling mechanism

One of the main elements of the theory of constraints is its Drum–Buffer–Rope (DBR) scheduling (or release) mechanism that controls the release of jobs to the system. Jobs are not released directly to the shop floor – they are withheld in a backlog and released in accordance with the output rate of the bottleneck (i.e. the drum). The sequence in which jobs are considered for release from the backlog is determined by the schedule of the drum, which also determines in which order jobs are processed or dispatched on the shop floor. In the DBR literature, the focus is on the urgency of jobs and the same procedure is used both for backlog sequencing and dispatching. In this study, we explore the potential of using different combinations of rules for sequencing and dispatching to improve DBR performance. Based on controlled simulation experiments in a pure and general flow shop we demonstrate that, although the original procedure works well in a pure flow shop, it becomes dysfunctional in a general flow shop where job routings vary. Performance can be significantly enhanced by switching from a focus on urgency to a focus on the shortest bottleneck processing time during periods of high load.