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    Effectiveness improvement in manufacturing industry; trilogy study and open innovation dynamics
    (Multidisciplinary Digital Publishing Institute (MDPI), 2021) Tayal, Ashwani; Kalsi, Nirmal Singh; Gupta, Munish Kumar; Pimenov, Danil Yurievich; Sarikaya, Murat; Pruncu, Catalin I.
    The purpose of this investigation is to compute overall equipment effectiveness (OEE) in the small-scale industry. The novel approach is introduced to detect bottlenecks by which OEE can be improved. This study attempts to help small-medium enterprises in analyzing performance in a better way. The automotive industry was chosen for conducting the research. The present study is comprised of three phases. In the first phase, OEE was computed and compared with world-class manufacturing. The second phase included three-level of Pareto analysis followed by making fishbone diagram to mitigate the losses. The third phase conducted improved OEE in the industry. There are seven major losses present in the industry that adversely affect the effectiveness of machine in any industry. This approach can reduce these losses and improve the quality, asset utilization (AU), OEE, total effective equipment performance (TEEP) and productivity of the machine. The study exposes that Pareto analysis uncovers all the losses and works on the principle of 80/20 rule. The major losses were thoroughly explored with the help of the fishbone diagram and solutions were implemented at the shop floor. As a result, availability, performance, quality, OEE, AU, and TEPP show improvements by 4.6%, 8.06%, 6.66%, 16.23%, 4.16%, and 14.58%, respectively. The approach offers a good opportunity for both researchers and small-medium enterprises around the world to analyze the indicators of production losses, performance, and productivity in the manufacturing industry. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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    Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140
    (Mdpi, 2021) Kuntoglu, Mustafa; Acar, Osman; Gupta, Munish Kumar; Saglam, Haci; Sarikaya, Murat; Giasin, Khaled; Pimenov, Danil Yurievich
    The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi's S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.
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    Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy
    (Mdpi, 2021) Sap, Emine; Usca, Usame Ali; Gupta, Munish Kumar; Kuntoglu, Mustafa; Sarikaya, Murat; Pimenov, Danil Yurievich; Mia, Mozammel
    The features of composite materials such as production flexibility, lightness, and excellent strength put them in the class of materials that attract attention in various critical areas, i.e., aerospace, defense, automotive, and shipbuilding. However, the machining of composite materials displays challenges due to the difficulty in obtaining structural integrity. In this study, Cu/Mo-SiCP composite materials were produced by powder metallurgy with varied reinforcement ratios and then their machinability was investigated. In machinability experiments, the process parameters were selected as cutting speed (v(C)), feed rate (f), depth of cut (a(P)), and reinforcement ratio (R-R). Two levels of these parameters were taken as per the Taguchi's L8 orthogonal array, and response surface methodology (RSM) is employed for parametric optimization. As a result, the outcomes demonstrated that R-R = 5%, f = 0.25 mm/rev, a(P) = 0.25 mm, v(C) = 200 m/min for surface roughness, R-R = 0%, f = 0.25 mm/rev and a(P) = 0.25 mm and v(C) = 200 m/min for flank wear and R-R = 0%, f = 0.25 mm/rev, a(P) = 0.25 mm, v(C) = 150 m/min for cutting temperature for cutting temperature and flank wear should be selected for the desired results. In addition, ANOVA results indicate that reinforcement ratio is the dominant factor on all response parameters. Microscope images showed that the prominent failure modes on the cutting tool are flank wear, built up edge, and crater wear depending on reinforcement ratio.