Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing(De Gruyter Open Ltd, 2023) Manoj, I.V.; Narendranath, S.; Mashinini, P.M.; Soni, H.; Rab, S.; Ahmad, S.; Hayat, A.Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce "smart manufacturing,"which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturing-related fields. © 2023 the author(s), published by De Gruyter.Item Spatiotemporal capacity estimation of bus rapid transit system based on dwell time analysis(King Saud University, 2024) Angadi, V.S.; Halyal, S.; Mulangi, R.H.The performance study of an urban transport system, particularly a Bus Rapid Transit System (BRTS), must report on its operations and reliability. Such study of BRTS comprises numerous facets, including capacity, which directly influences how the system practically operates and serves the commuters. Hubballi-Dharwad Bus Rapid Transit System (HDBRTS) has been operational since 2018. A performance study is necessary to evaluate the performance of HDBRTS, which aids in its upgradation and improvement. The current research uses the experimental technique through an innovative and inspired basis to comment on the HDBRTS's performance by estimating the corridor's operational capacity at different spatial and temporal fluctuations. The selected route of the HDBRTS comprises combined segregated (exclusive traffic environment) and unsegregated (mixed traffic environment) stretches. The current study mainly conducted a video graphics-based survey to acquire the necessary data on identified spatial and temporal trends at various HDBRTS bus stations. The essential data gathered consists of Dwell Time (DT)-based data at each station, summarising the total time a bus takes to serve passengers at a station. DT is inversely proportional to the capacity of the particular bus station, which is related to the Failure Rate (FR). FR values of all the bus stations of the route were analyzed using DT, and then capacity values were calculated at different spatiotemporal patterns. Study results show that the busiest stations of the identified routes with critical DT values have FR values in the range of 1–2%, contradicting previous studies. The variations in the capacity of the stations, both spatially and temporally, were graphically represented with the minimum capacity of the segregated stretch as 36 buses/hr and the unsegregated stretch as 31 buses/hr. Finally, the Level Of Service (LOS) of the chosen study corridor was developed using the K-Means clustering algorithm and validated using the Silhouette Coefficient technique. The silhouette coefficient values obtained range from 0.52 to 0.74, indicating a reasonable structure. © 2023 The AuthorsItem PRIDES: A Probabilistic Model for Recurrent User Interest Drift Identification in Session-Based Recommendation(Institute of Electrical and Electronics Engineers Inc., 2025) Chaitanya, V.S.; Santhi Thilagam, P.S.A Session-Based Recommendation (SBR) identifies correlations among session interactions to understand user preferences and generate appropriate recommendations. A key challenge in this context is the dynamic change in user preferences, particularly when preferences disappear and reappear within a session, a phenomenon referred to as Recurrent User Interest Drift (RUID). Effectively capturing RUID is significant for aligning recommendations with ongoing user preferences. Existing SBR approaches often misclassify user preferences that differ from other session interactions as noise (unintentional interactions), relying on dwell time (the amount of time a user spends viewing an item) or neighboring sessions, thereby overlooking their potential reappearance as RUID later in the session. To the best of our knowledge, this work is the first to address the challenge of identifying RUID in SBR. The proposed approach assigns probabilistic scores to each interaction by considering its similarity to the immediate previous interaction, its inclusion among popular items (items with a higher number of interactions), its similarity to previous interactions, and the dwell time. As user preference may reappear anytime during the session, and RUID identification requires analyzing subsequent interactions, a list-based approach is used to retain these interactions until the session ends, enabling effective RUID identification. The matrix factorization-based attentive session encoder incorporates both short-term (ongoing) preferences and long-term (historical) preferences to generate personalized recommendations. Experimental results on three benchmark datasets, Yoochoose, Last.fm, and Gowalla, show that our method outperforms 14 state-of-the-art baselines, achieving an improvement of 2.28% in recall@20 and 1.39% in Mean Reciprocal Rank (MRR@20) on Yoochoose, 3.58% in recall@20 and 2.70% in MRR@20 on Last.fm, and 5.35% in recall@20 and 4.17% in MRR@20 on Gowalla datasets. © 2013 IEEE.Item Heat Transfer and Deposition Strategies for Enhanced Mechanical Performance of Wire Arc Additively Manufactured SS316L Alloy(Springer, 2025) Pai, K.R.; Vijayan, V.; Samuel, A.; Prabhu, K.N.The work investigates the effect of various deposition strategies for wire arc additive manufacturing of SS316L on an SS304 substrate for industrial applications. Droplet deposition of SS316L on an SS304 substrate at varying current values (60–130 A) identifies the operational range for line deposition. The wettability, contact angle and spread area are evaluated along with heat flux transients for each current value. Heat flow calculated during line deposition at 90 A for horizontal and vertical substrates was 34297 kJ/m2 and 24137 kJ/m2 respectively. The corresponding values of porosity and micro-hardness indicate superior deposition at 90 A. Further investigation on deposition strategies such as interlayer current change with and without dwell time, deposition at 90 A with a dwell time of 30 s for five cycles, preheated substrates and Continuous Multi-Pass Deposition with 2 s is explored. Heat flux transients are computed for every deposition cycle using an inverse solver. Heat flow was found to be 63260 kJ/m2 and 58863 kJ/m2 for the 15th layer of interlayer current change of 90 ± 10 A and constant current of 90 A with dwell time respectively. By altering deposition parameters such as interlayer time gap and current the chromium content achieved through high-current density deposition significantly increased from 17.2% to 26% and 25.4% respectively. The ultimate tensile strength for the 80A sample without deposition strategies was found to be lower. Columnar grain morphology with dendritic structure was observed at higher currents. Finer equiaxed grains with lower interlayer fusion were observed at lower currents. Finer grain growth across the layers was achieved by adjusting the current between cycles in response to observed heat flux transients. EBSD analysis reveals the formation of brass texture with {110} in deposition strategies involving time gap and interlayer current change, indicating directional solidification thereby enhancing the overall mechanical performance of the as-deposited SS316L. © ASM International 2025.