Purpose The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes. Design/methodology/approach In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance. Findings The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes. Practical implications This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values. Originality/value The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

Low-density polyethylene (LDPE) film was chemically modified by chromic acid treatment to generate polar groups on the surface. The film samples were etched by chromic acid with variation of temperature at a constant time (30 min) and variation of time at a constant temperature (room temperature ϭ 26°C). The variation of weight and thickness of the film samples before and after etching was measured. The surface morphology of the etched films was studied by Scanning Electron Microscopy (SEM). IR and XPS analysis revealed the introduction of polar groups like OCOOH, ͘CAO, OSO 3 H on the etched LDPE film surface, which exhibited improved printability. Etching also enhanced adhesion with epoxy resin. The mechanical properties of the laminates of the two specimens of the same film sample with epoxy resin were also measured.

In order to avoid working in a constrained hazardous environment, manual spray painting operation is gradually being replaced by automated robotic systems in many manufacturing industries. Application of spray painting robots ensures defect-free painting of dissimilar components with higher repeatability, flexibility, productivity, reduced cycle time and minimum wastage of paint. Due to availability of a large number of viable options in the market, selection of a spray painting robot suitable for a given application poses a great problem. Thus, this paper proposes the integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods to identify the most apposite spray painting robot for an automobile industry based on seven evaluation criteria (payload, mass, speed, repeatability, reach, cost and power consumption). The SWARA method identifies cost as the most significant criterion based on a set preference order, whereas, Fanuc P-350iA/45 is selected as the best spray painting robot by CoCoSo method. The derived ranking results are also contrasted with other popular multi-criteria decision making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, PROMETHEE and MOORA) and subjective criteria weighting methods (AHP, PIPRECIA, BWM and FUCOM). High degrees of similarity in the ranking patterns between the adopted approach and other MCDM techniques prove its effectiveness in solving complex industrial robot selection problems. This integrated approach is proved to be quite robust being almost unaffected by the changing values of the corresponding tuning parameter in low-dimensional MCDM problems.

In this paper, we present a novel formulation of exemplar-based image inpainting as a metric labeling problem, and solve it through the simulated annealing algorithm. Due to their greedy nature, exemplar-based methods sometimes produce inpainted images, which are visually inconsistent. These methods are highly dependent upon the initialization. To solve these problems, we generate five images with a different initialization. A suitable mixture of these five images produces a good inpainted image. The cost function of the proposed metric labeling problem consists of three components, namely, neighbor cost, total variation cost, and structure cost. A linear combination among these components is used to maintain better visual consistency in the inpainted region having smooth transition from the bordering regions of the source image. We use a quality measure to this end. Our experiments on a wide variety of images demonstrate that the proposed technique produces better inpainting images as compared with some other state-of-the-art techniques.

Low-density polyethylene (LDPE) films were treated with fuming nitric acid (FNA). The surface characteristics and also the insertion of polar groups like --COOH, >C=O, -NO2, and -NO on the etched LDPE film surface were measured by SEM, IR and XPS analyses, respectively. The mechanical performance of a laminate of the etched film with epoxy resin and also the printability of the etched film surface were tested and compared with the unetched sample. The surface roughening and the presence of polar groups enhance the mechanical strength of the laminate of FNA-treated film due to mechanical interlocking and chemical interaction. The printability of the treated film surface is also superior to that of the untreated LDPE film surface. ZUS AMMENFASSUNG:Filme aus Polyethylen niedriger Dichte (LDPE) wurden mit rauchender Salpetersaure behandelt. Die Oberflacheneigenschaften sowie die Bildung polarer Gruppen wie --COOH, >C=O, -NO2 oder -NO auf der geatzten Filmoberflache wurden mit SEM-, IR-und XPS-Methoden analysiert. Die mechanische Festigkeit eines aus dem geatzten LDPE-Film und einem Epoxidharz hergestellten Laminats sowie die Bedruckbarkeit der geatzten Oberflachen wurden untersucht und mit einer unbehandelten Probe verglichen. Die Oberflachenrauhigkeit und die Gegenwart polarer Gruppen erhohen die mechanische Festigkeit des Laminats aus dem geatzten Film durch mechanische Verankerung und chemischer Wechselwirkungen. Die Oberflache des behandelten LDPE-Films ist besser bedruckbar als die des unbehandelten Films.

Segmentation of foreground and background has been an important research problem arising out of many applications including video surveillance. A method commonly used for segmentation is "background subtraction" or thresholding the difference between the estimated background image and current image. Adaptive Gaussian mixture based background modelling has been proposed by many researchers for increasing the robustness against environmental changes. However, all these methods, being computationally intensive, need to be optimized for efficient and real-time performance especially at a higher image resolution. In this paper, we propose an improved foreground/background segmentation method which uses Experiential Sampling technique to restrict the computational efforts in the region of interest. We exploit the fact that the region of interest in general is present only in a small part of the image, therefore, the attention should only be focused in those regions. The proposed method shows a significant gain in processing speed at the expense of minor loss in accuracy. We provide experimental results and detailed analysis to show the utility of our method.

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Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts.