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Many procedures have been introduced to improve the efficacy and performance of the exponentially weighted moving average (EWMA) control chart. The objective of these research studies is to introduce an efficient and precise monitoring method to detect smaller shifts in the process mean. Azam et al. [1] presented a new control chart called hybrid exponentially weighted moving average (HEWMA); where they have developed a new model and derived new control schemes. However, minor mistakes were observed in the article. This note will modify Azam et al.’s article in order to get the maximum benefit out of their proposed new model. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of productivity calculation and prediction, this paper aims to establish a mathematical model, which takes multi-factor revision into account, to evaluate the personnel operational capacity (POC) of aircraft assembly lines. Firstly, the assembly operation is divided into assembly operating elements (AOEs), which is based on the features of aircraft assembling and is for the provision of statistical units. Secondly, effective man-hour (EMH) is adopted as a yardstick to measure the POC. Then, considering the technical characteristics and environmental constraints of the real assembly line, detailed quantitative descriptions about the chosen key factors, namely assembling accuracy, learning effect, and assembling intensity, are provided, and these quantitative descriptions are used for the revision of the EMH in order to guarantee a higher precision. After that, several critical parameters such as the cycle time, tact time, and output of an assembly line can be solved out, from which the POC can be analyzed and reflected. Finally, this method has been successfully applied to a practical case, and the result justifies its advantage as well as feasibility. [ABSTRACT FROM AUTHOR]

In sheet metal production, a typical process chain contains hot rolling, cold rolling and annealing as a sequence of consecutive processing steps. We investigate the grain structure evolution of body centered cubic low carbon steel and focus on recrystallization, by employing different computational methods which operate across the process chain and across length scales. In particular, we combine finite element crystal plasticity with phase-field simulations to study the effect of deformation of the grain structure by hot and cold rolling on recrystallization during annealing. The overall goal is to represent the most important technological quantities such as texture evolution and the fraction of recrystallization. The results of grain quantities are validated by a comparison of the orientation distribution functions with experimental electron backscatter measurements. The coupling of the simulation methods has shown that the effects of recrystallization can be recovered well, depending on the preceding processing conditions. [ABSTRACT FROM AUTHOR]

A novel process chain for serial production of polymer-based devices incorporating both micro- and nano-scale features is proposed. The process chain is enabled by the use of Zr-based bulk metallic glasses (BMG) to achieve the necessary level of compatibility and complementarity between its component technologies. It integrates two different technologies, namely laser ablation and focused ion beam (FIB) milling for micro-structuring and sub-micron patterning, respectively, thus to fabricate inserts incorporating different length scale functional features. Two alternative laser sources, namely nano-second (NS) and pico-second (PS) lasers, were considered as potential candidates for the first step in this master-making process chain. The capabilities of the component technologies together with some issues associated with their integration were studied. To validate the replication performance of the produced masters, a Zr-based BMG insert was used to produce a small batch of micro-fluidic devices by micro-injection moulding. Furthermore, an experimental study was also carried out to determine whether it would be possible by NS laser ablation to structure the Zr-based BMG workpieces with a high surface integrity whilst retaining the BMG's non-crystalline morphology. Collectively, it was demonstrated that the proposed process chain could be a viable fabrication route for mass production of polymer devices incorporating different length scale features. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining (WEDM) has a significant position among production technologies mainly due to its capacity of machining hard materials and intricate shapes. One of the major problems with this process is the error in cutting small-radius corners. Processing forces acting on the wire and low rigidity of the wire are responsible for wire deflection, which has a direct influence on the accuracy of the corner cutting. In this research, investigation is focused on small-radius convex and concave corner radii errors, and alternative solutions are proposed for the case of successive cuts (one roughing and two finishings). Initial experiments are carried out for roughing operation by considering frequency of discharges and feed speed as input variables. Residual materials on straight and small-radius convex curved paths are the output parameters. One important conclusion is that roughing is the most influential stage of cutting by WEDM. Results also indicate that optimization of these input parameters has a better influence on controlling the residual material thickness on straight paths than on small-radius convex curved corners. Further experiments are carried out by considering the influence of corner angle and corner radii on small-radius concave corners in successive corner cuts. Errors at radii of different corner angles are identified and relations established to arc length and residual material thickness in the curved corner. Finally, an effective approach is presented to improve accuracy of small-radius concave corners at finishing stage. The main conclusion is that to achieve accurate corner radii, one must increase the traversed corner arc length by wire in the small-radius concave corner. [ABSTRACT FROM AUTHOR]

One of the main problems in the machining of thin-walled parts made of high-strength aluminum alloys is distortion and dimensional instability after machining, which leads to an increase in scraped parts and the cost of production. In general, distortion and dimensional instability in machined parts made of aluminum alloy is a function of the residual stresses. In this article, the correlation between machining-induced residual stresses and distortion in a thin-walled work pieces is investigated. Several experiments are carried out under different machining conditions using two carbide and polycrystalline diamond (PCD) tools on a thin-walled cylinder made of AL7075-T6 alloy. Rates of variations in geometrical tolerances and distortion for all work pieces are measured. To study the effect of mechanical and thermal loads on the residual stresses and distortion, the machining force and temperature of cutting area are measured. Finally, the correlation between the residual stress and distortion is studied by measurement of residual stress on some work pieces. Similarly, in order to investigate the effect of work pieces thickness on distortion, several tests are carried out on three work pieces with different thicknesses. The results indicate that the force and temperature have direct effect on the residual stresses and distortion in the thin-walled parts. [ABSTRACT FROM AUTHOR]

An aggregate production-distribution (P-D) planning generates an aggregate plan for regular time, overtime, outsourcing, hiring, and firing that takes into account distributing, inventory holding, backordering, and machine capacity for a definite planning horizon. A large number of P-D problems require decisions to be made in the presence of uncertainty. Previous research on this topic mainly utilized either stochastic programming or fuzzy programming to cope with the uncertainty. This may lead into huge challenges for supply chain managers that use non-robust P-D planning in uncertain environments. Moreover, there has been little discussion about robust optimization approach in aggregate P-D problems so far.Therefore, the aim of this paper is to develop robust optimization in P-D planning in order to minimize the total cost of a three-level supply chain including multiple production facilities, multiple distribution centers, and multiple customer zones with uncertain parameters in terms of a limited set of discrete future economic scenarios. These scenarios are associated with probabilities in an industrial case study. In addition, a trade-off between solution robustness and model robustness using specified weight is studied, and a sensitivity analysis for this trade-off is implemented. Finally, the applicability of the proposed methodology is demonstrated. [ABSTRACT FROM AUTHOR]

In production engineering, highest surface qualities and low tolerances are produced by grinding processes. Defects of the grinding wheel caused by unbalance, wrong dressing cycles, cavities, or waviness result in vibrations and damage of workpiece or spindle. Therefore, monitoring of the tool is important in practical use. In order to avoid scrap, small faults of the grinding wheel and very small amplitudes of the resulting vibrations are of major interest. This paper presents a new monitoring method, which estimates the defects recursively during the grinding process. Different sensors are analyzed using this method. Among sensors for academic purposes, typical sensors in grinding machines such as the displacement, acceleration, motor current, and acoustic emission are in use for monitoring. Compared to monitoring in the frequency domain and signal feature extraction by filtering, the recursive estimation can reduce noise. In cylindrical plunge grinding, wavy tool defects may result in unequal wear and self-exiting waviness of the wheel over a long period of grinding and start at very low amplitudes. This regenerative chatter is subject of monitoring of highest productivity of grinding processes and is therefore analyzed in this paper as well as the strongly depending selection of dressing cycles initiated by the monitoring algorithm. [ABSTRACT FROM AUTHOR]

The development of an automatic system for the intelligent support of continuous cast billet production control processes is described. The motivation for the development of the system is that it should improve the efficiency of production facilities and minimize the possibility of producing inferior and unacceptable quality products. A theoretical analysis of the information relating to the quality control of the processes and the finished products is presented, enabling the identification of the sources of information, methods of information acquisition, and techniques for processing it to ensure improved product quality. The development of mathematical support is described for a program analyzer that automatically and reliably identifies the defects and quality of the continuously cast billets. The application of graphic information acquisition and processing techniques concerning the quality of the metal products is also presented. The development of mathematical and software support is described for the set point adjustment module operating in the automatic system for the intelligent support of the multistage continuous cast billet production control facility. This module makes use of adaptive fuzzy trees with dynamic structures to provide scientifically grounded analysis of factors causing billet defects. The introduction of the developed systems, including practical issues, into the operation of a production facility is explained. The study identifies the general lack of automatic systems that encompass and control the whole production chain on the basis of product quality. Typical savings resulting from quality improvements in a continuous cast billet production facility can approach a million rubles. [ABSTRACT FROM AUTHOR]

Manufacturers around the globe are presented with the evident need to successfully capture and efficiently satisfy the increasing demand towards highly customised products. The trend for higher levels of customisation increases the operational costs, affects delivery times and worsens the environmental footprint of production. Moreover, the feasible alternative manufacturing network configurations increase together with the exploding product variety and the large pool of cooperating suppliers. The proposed research work describes an intelligent method that utilises three adjustable control parameters and can be used for the identification of efficient globalised manufacturing network configurations capable of carrying out the production of mass customised products. The decision support system presented allows the generation of alternative manufacturing network configurations and their evaluation, through a set of multiple conflicting user-defined criteria of cost, time, quality and environmental impact. The suggested approach, which is implemented into a web-based software tool, is investigated through a probabilistic analysis for guiding the decision-maker when selecting the values of the adjustable control parameters, in order to obtain high-quality manufacturing network designs. The applicability of the method is validated through a real-life pilot case, using data acquired from an automotive manufacturer. [ABSTRACT FROM AUTHOR]

Non-traditional machining (NTM) processes have already emerged out as the suitable substitutes for the conventional metal removal methods due to their capability of generating complicated shape geometries on diverse difficult-to-machine engineering materials. For these NTM processes, it is always proposed that they should be operated while setting their various input parameters at the optimal levels for achieving better machining performance. In this paper, the application of a data mining tool in the form of development of association rules is explored to determine the best machining conditions for three NTM processes, i.e. electrochemical machining process, ultrasonic machining process and electrical discharge machining process. These rules, presented as simple 'If-Then' statements, would also guide the concerned process engineers in investigating the effects of various NTM process parameters on the considered responses. It is observed that the most preferred parametric combinations identified based on the generated association rules closely match with those as perceived by the past researchers. [ABSTRACT FROM AUTHOR]

Microbiologically induced calcite precipitation following the ureases is a bio-geo-chemical soil improvement technique in which the microorganism facilitates to create an environment for the precipitation of carbonates among the grains. It results in binding the loose granular media and prevention against mechanical failure. Although, the bio-cemented processes and media have been studied in the past in qualitative sense with experimental programs, the mathematical and numerical modelling techniques to quantify the strength parameters are rare. In this article, we propose the lattice element methodology which we applied to perform numerical computations of unconfined compression tests on bio-cemented sands. We also provide the experimental results of the unconfined compression tests on bio-cemented sands treated with a different number of cycles that we conducted in our laboratory. The experimental procedure is explained in details. The ultimate goal is to study the macroscopic response and also to quantify the process for engineering applications. The developed model with an embedded discontinuity can capture the macroscopic behaviour from meso-scale element failure, where the diagonal shear cracks which are seldom inherent to compression failure of highly cemented granular media lead the specimens to final failure. The model can capture the complex interaction of the cracks such as initiation and propagation, branching, coalescence and fingering at a nominal computation cost. The numerical and experimental results show good agreement to a large extent. The developed model is suitable to study brittle, and quasi-brittle behaviour of highly cemented granular media. [ABSTRACT FROM AUTHOR]

Selection of material in engineering design process is a difficult and elusive task due to enormous number of dissimilar materials availability. For effective selection of materials, the designers have to take into account a number of definite qualitative and quantitative criteria. In the same context, this paper proposes a hybrid TOPSIS-PSI approach for effective material selection in marine applications. In this paper, the selection index value has been calculated by using logical combination of PSI and TOPSIS algorithm and these values have been ranked in ascending or descending order. The highest preference selection index value has been taken as the best alternative for the marine application. To prove the effectiveness of the proposed hybrid TOPSIS-PSI algorithm, two practical examples are considered and the result shows that the proposed procedure provides satisfactory results when compared with past literature. Furthermore, hybrid procedure is performed for selection of best wt.% combination among hybrid aluminum nanocomposites for marine applications based on its physical, mechanical and corrosive behavior. The result reveals that 9 wt.% and 6 wt.% reinforced hybrid aluminum nanocomposites have optimum combination of all physical, mechanical and corrosion properties, respectively according to hybrid TOPSIS-PSI approach. [ABSTRACT FROM AUTHOR]