Input Process Research Articles

Study into surface integrity aspects in single point incremental forming of AA 7079 sheet: experimental investigation and evolutionary parametric optimization

ABSTRACT Aluminum alloys have been used for the Single Point Incremental Forming (SPIF) process due to their formability, corrosion resistance, less weight and its isotropic mechanical properties. The proposed research work aims to experimentally investigate the optimisation of the process inputs affecting the quality of the SPIF incrementally formed surface. The experimentation was performed to optimise parameters under varying conditions, focusing on various process factors. The surface roughness (SR) has been found most significantly affected by the tool diameter, followed by vertical step size, side wall angle, feed rate, and rotational spindle speed. Experimentation result shows the outstanding values of the experimental set-up for surface finish found on the base of the ANOVA at 70° cone angle under the test setting of a TD of 13.0 mm, a VSS of 0.50 mm, RSS of 1000 rpm, and a FR of 2500 mm/min. The parametric optimisation for SR with Desirability approach, TLBO (Teacher learner-based optimisation), and ACO (ant colony optimisation) methods has been attempted, out of which the best optimum value (with ACO) for SR was achieved as 0.162 µm at the optimal parametric settings of TD = 14.75 mm, VSS = 0.660 mm, RSS = 521.70 rpm, FR = 2760 mm/min, and WA = 76.56°.

Micro-engraving on modified nickel-base superalloy using a short-pulsed laser source

ABSTRACT The modified nickel-base superalloy is micro-engraved with short pulsed laser with different parameter. The dimensional accuracy of the micro-profiles engraved through laser energy is evaluated through a scanning electron microscope. For the minimum speed of 50 mm per sec, the profile length and breadth have high dimensional stability compared to the average and high scan speed. The optical microscope is used to measure the depth of the micro-profile and the same has been metallurgically processed to read the microstructural changes. A maximum depth of 632 µm for the micro-profile is found with a maximum scan speed of 100 mm per sec at a low power of 2 watts and an average laser frequency of 7 kHz. Therefore, the input process parameters are invariant for the different responses on micro-engraving.

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally generated top-down information. In the neocortex, top-down projections primarily target layer 1, which contains NDNF (neuron-derived neurotrophic factor)-expressing interneurons and the dendrites of pyramidal cells. Here, we investigate the hypothesis that NDNF interneurons shape cortical computations in an unconventional, layer-specific way, by exerting presynaptic inhibition on synapses in layer 1 while leaving synapses in deeper layers unaffected. We first confirm experimentally that in the auditory cortex, synapses from somatostatin-expressing (SOM) onto NDNF neurons are indeed modulated by ambient Gamma-aminobutyric acid (GABA). Shifting to a computational model, we then show that this mechanism introduces a distinct mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons. This motif can control inhibition in a layer-specific way and introduces competition between NDNF and SOM interneurons for dendritic inhibition onto pyramidal cells on different timescales. NDNF interneurons can thereby control cortical information flow by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition.

In-situ evaluation of hole quality and cutting tool condition in robotic drilling of composite materials using machine learning

Abstract The massive adoption of industrial robots in the manufacturing sector has significantly increased automation of installation and inspection procedures, particularly benefiting the aerospace industry, where large volumes of holes are drilled in each aircraft. However, mechanical drilling remains challenging when dealing with composite materials due to their inherently heterogeneous structure. This work presents a novel approach for in-situ hole quality inspection utilising integrated sensor data of an industrial robotic drill, combined with a machine learning model. Additionally, a novel classification approach for evaluating hole quality is proposed. This study employed a KUKA industrial robot, fitted with a multifunctional end-effector, to drill holes in a composite material used in aerospace applications. An ensemble neural network (ENN) model, which combines an artificial neural network with a genetic algorithm, was used to assess the quality of these drilled holes. The model was specifically developed and tested on the machined holes to relate process input parameters and drilling torques to hole quality. The model predictions were validated with six unseen datasets, of which five were predicted accurately. A full factorial study of the process parameters was conducted using analysis of variance (ANOVA) to investigate the relationship between tool condition and drilling torque. The results of the ANOVA show that tool condition is the largest contributor to drilling torque. The method proposed in this work, which allows real-time monitoring of hole quality, has the potential to improve manufacturing productivity of drilled components while ensuring high-quality end products.

COCONUT PREDICTIVE ANALYSIS USING MACHINE LEARNING

“Coconut Predictive Analysis” focuses on leveraging advanced machine learning techniques to predict the suitability of coconuts for oil production. The project employs transfer learning with the Inception V3 model, a state-of-the-art convolutional neural network (CNN), to analyze complex patterns in coconut-related data, such as images of coconuts. Traditional methods for determining coconut suitability for oil production rely on manual inspection, which can be time-intensive, subjective, and inconsistent. By utilizing transfer learning, this project capitalizes on pre-trained Inception V3 models, which extract meaningful features from large-scale datasets like ImageNet, and fine-tunes them for the specific task of classifying dry coconuts based on their suitability for oil extraction. The fine-tuning process involves freezing initial layers to retain general features while optimizing later layers to improve prediction accuracy for coconut-specific characteristics such as texture, colour, and surface patterns. To ensure accessibility and usability, a Flask web application is developed. This application enables users to upload images of coconuts and receive real-time predictions on their suitability for oil production. The backend integrates the trained model to process inputs, extract features, and generate predictions, making it a scalable and practical solution for agricultural and industrial applications. This project demonstrates the potential of transfer learning in enhancing agricultural and processing efficiency by reducing training time and computational costs while providing accurate and actionable predictions. By combining cutting-edge deep learning with a user-friendly web interface, it bridges the gap between advanced AI models and practical applications, supporting decision-making for farmers, coconut processing units, and industry stakeholders. Key Words: Inception V3, fine-tuning, CNN, Flask, coconut analysis, oil production suitability

Sources, Water Quality, and Potential Risk Assessment of Heavy Metal Contamination in Typical Megacity River: Insights from Monte Carlo Simulation

Due to the intense human activities and rapid development of economy, dissolved heavy metals (DHMs) pose a significant threat to urban river ecosystems. Therefore, the distribution, sources, and potential risks of DHMs in the Chaobai River (typical urban river) were investigated via ICP-MS in detail. Results revealed considerable spatial heterogeneity of heavy metals with various concentrations from the upper to lower reach. Principal component analysis (PCA) revealed that V, Ni, As, Mo, and Pb mainly originated from a mixing process of industrial input and natural process, Cr and Cu were mainly derived from urban activities, and Zn was mainly influenced by agriculture activities. Furthermore, land use types within the buffer zone near sampling points were innovatively analyzed, revealing strong correlations between DHMs and regional land use patterns. Monte Carlo simulations were employed to assess the differentiated non-carcinogenic and carcinogenic risks associated with DHMs across four age groups. This study provided scientific references for the sustainable management of urban rivers and aquatic systems in such a megacity region.

Implementation of the I'DIS (Integrated Discipline) Application in Imposing Disciplinary Punishments on Civil Servants in Government Agencies in the Province of North Sulawesi

The use of the I'DIS Application in the implementation of the Imposition of Disciplinary Punishments for Civil Servants is indicated to be inconsistent between regulations and their implementation. Many Government Agencies in the working area of ​​the Regional Office XI BKN Manado still do not use the I'DIS Application and do not understand the input process or have not reached the stage of the process of making a Disciplinary Punishment Decree. This study aims to describe and analyze the Implementation of the I'DIS (Integrated Discipline) Application in the Imposition of Disciplinary Punishments for Civil Servants in Government Agencies in the Province of North Sulawesi. By using a qualitative design, it was found that the Implementation of the I'DIS Application in the imposition of disciplinary punishments for Civil Servants in Government Agencies in the Province of North Sulawesi still lacks a deeper understanding of the application, especially when technical guidance is carried out online. Likewise, resources including human resources, facilities and infrastructure are inadequate.

A Radiation-Based Analytical Model for the Computation of Temperature in the Arc Column of Submerged Arc Additive Manufacturing (SAAM) Process

Abstract Submerged Arc Additive Manufacturing (SAAM) is an advanced Wire Arc-based Additive Manufacturing (WAAM) technique known for its uniform deposition structure, high deposition rate, and superior surface finish. However, it is difficult and expensive to experimentally investigate the arc column in SAAM due to its submerged nature under flux. This paper presents an effective alternative: developing a radiation-based analytical model to predict the complex temperature distribution within the SAAM arc column. It focuses on the radiation heat transfer phenomenon since the temperatures attained inside the arc column are beyond the melting temperature of the material involved, and the presence of granular flux and slag intensifies the effect of the same. The model incorporates key input process parameters like wire diameter, stick-out length, current, voltage, and travel speed alongside design parameters like bead width, penetration depth, and material reinforcement. The double ellipsoidal heat source model is the foundation for generating the temperature profile. The predicted temperature profile resembles the double ellipsoid in other arc welding-based additive manufacturing techniques. The model's predictions showed a deviation of approximately 14% from experimental results, validating its effectiveness. Extensive parametric studies were carried out using the developed model. It was observed that longer stick-out lengths dampened temperature variations, while higher currents confined the affected zone further. Increased travel speed reduces the heating and cooling rate, while voltage behaves the opposite way. This analytical approach offers a cost-effective alternative for optimizing process parameters to achieve desired dimensions and deposition quality.

New design of two‐dimensional LQ control for batch processes with iterative learning error compensation

AbstractThis paper proposes a two‐dimensional infinite horizon linear quadratic iterative learning control (2D‐IHLQILC) strategy based on error compensation. The strategy aims to address the shortcomings of one‐dimensional infinite horizon linear quadratic control (1D‐IHLQC), which is unable to utilize historical batch information. Firstly, a novel extended state space model is established to describe the batch process, which provides more degrees of freedom for the controller design and enables additional adjustment of the batch process input increment and output increment. Secondly, an error compensation strategy is introduced using historical batch information, which extends the novel extended state‐space model from one to two dimensions and can effectively deal with the time delay problem. Finally, a novel 2D‐IHLQILC controller is designed, which empowers the controller to learn iteratively from historical batch information to improve the control performance batch by batch and to achieve full tracking of the setpoint trajectory. The effectiveness of the 2D‐IHLQILC is tested on the holding pressure control in the injection moulding process as an example.

A New Closed-Loop Control Paradigm Based on Process Moments

The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Effect of immersive virtual reality based upon input processing model for second language vocabulary retention

Effect of immersive virtual reality based upon input processing model for second language vocabulary retention

Neurofeedback and attention modulate somatosensory alpha oscillations but not pain perception.

Pain is closely linked to alpha oscillations (8 < 13 Hz) which are thought to represent a supra-modal, top-down mediated gating mechanism that shapes sensory processing. Consequently, alpha oscillations might also shape the cerebral processing of nociceptive input and eventually the perception of pain. To test this mechanistic hypothesis, we designed a sham-controlled and double-blind electroencephalography (EEG)-based neurofeedback study. In a short-term neurofeedback training protocol, healthy participants learned to up- and down-regulate somatosensory alpha oscillations using attention. Subsequently, we investigated how this manipulation impacts experimental pain applied during neurofeedback. Using Bayesian statistics and mediation analysis, we aimed to test whether alpha oscillations mediate attention effects on pain perception. The results showed that attention and neurofeedback successfully up- and down-regulated the asymmetry of somatosensory alpha oscillations. However, attention and neurofeedback did not modulate pain ratings or related brain responses. Accordingly, somatosensory alpha oscillations did not mediate attention effects on pain perception. Thus, our results challenge the hypothesis that somatosensory alpha oscillations shape pain perception. A causal relationship between alpha oscillations and pain perception might not exist or be more complex than hypothesized. Trial registration: Following Stage 1 acceptance, the study was registered at ClinicalTrials.gov NCT05570695.

Evaluation of the IMTAQ Religious Development Program Using SEM Approach in Asahan Regency

&lt;p class="Default"&gt;&lt;span lang="IN"&gt;The IMTAQ program, implemented by the Asahan Regency Government since 2011, aims to shape community character based on religious values. This study evaluates its effectiveness by analyzing input variables (socialization, training, funding, information media, technical guidelines, human resources, facilities, and implementing organizations), process variables (fund allocation, monitoring, administration, financial management, workshops), and output variables (internalization of IMTAQ values, preservation of Islamic cultural arts, Quranic development). Involving 552 participants from various community sectors, the findings highlight that information media, financial administration, and facilities are key success factors, with Quranic development and cultural preservation as main indicators. Using Structural Equation Modeling (SEM), the evaluation revealed an 11% annual improvement in implementation. The results provide guidance for optimizing the program’s Management Information System (MIS), including human resources, technology, data, and communication networks.&lt;/span&gt;&lt;/p&gt;

Learning Dendritic-Neuron-Based Motion Detection for RGB Images: A Biomimetic Approach.

In this study, we designed a biomimetic artificial visual system (AVS) inspired by biological visual system that can process RGB images. Our approach begins by mimicking the photoreceptor cone cells to simulate the initial input processing followed by a learnable dendritic neuron model to replicate ganglion cells that integrate outputs from bipolar and horizontal cell simulations. To handle multi-channel integration, we utilize a nonlearnable dendritic neuron model to simulate the lateral geniculate nucleus (LGN), which consolidates outputs across color channels, an essential function in biological multi-channel processing. Cross-validation experiments show that AVS demonstrates strong generalization across varied object-background configurations, achieving accuracy where traditional models like EfN-B0, ResNet50, and ConvNeXt typically fall short. Additionally, our results across different training-to-testing data ratios reveal that AVS maintains over 96% test accuracy even with limited training data, underscoring its robustness in low-data scenarios. This demonstrates the practical advantage of the AVS model in applications where large-scale annotated datasets are unavailable or expensive to curate. This AVS model not only advances biologically inspired multi-channel processing but also provides a practical framework for efficient, integrated visual processing in computational models.

Effectiveness of the Integrated Administrative Information System (PATEN) in Public Service

Problems related to population administration and civil registration, such as identity falsification and duplicate data due to lack of accurate details of population data documents, the process of obtaining a Population Identity Card and issuing a civil registration certificate still takes a long time because the data input process is still done manually. In this regard, the Garut Regency government, in this case the Tarogong Kaler District, is trying to collect valid population data so that it can develop mapping to overcome this problem. Currently there is an information system that makes it easier to provide services for creating and issuing resident document certificates, namely by implementing a website-based integrated administration service system or PATENT. This research aims to analyze Effectiveness of the Integrated Administrative Information System (PATEN) in Public Service. This research uses a qualitative descriptive type of research with four informants. Data collection techniques start from direct observation, interviews, and documentation. The data analysis techniques used are data collection, data reduction, data presentation and drawing conclusions and verification. The results of the research show that the community can enjoy the quality of services provided in Tarogong Kaler District. This can be seen from several things. The reliability dimension can be seen from the district operators who are reliable in providing services to the community. The responsiveness dimension related to the ability to respond is said to be good if the PATENT system has immediate disruption. can be overcome by not reducing services to the community. The obstacles are that the availability of human resources is still insufficient, as well as the need for socialization to the community about understanding the system.

The Lawfulness of Using Inventions for Generative AI Training : A Case Study of a US Lawsuit against OpenAI and Perplexity AI

Copyright protection in Indonesia is governed by Law No. 28 of 2014 (Law 28/2014), encompassing moral and economic rights. This law imposes limitations on the use of works for education, law enforcement, or technological development as long as such use does not harm the legitimate interests of the rightholder. The development of generative AI (GAI) poses challenges in determining the legality of using copyrighted works for GAI training. This study examines copyright regulations concerning GAI through normative, conceptual, and comparative legal approaches, including case studies on lawsuits against OpenAI and Perplexity AI. The findings indicate that the legality of using copyrighted works depends on the data input process and output (responses). Data scraping is considered an economic right of the rightholder, classified as reproduction under Article 9 of Law 28/2014. If such acts are conducted without the rightholder’s consent and for commercial purposes, they are deemed unlawful under Indonesia’s current copyright law. GAI outputs may also infringe copyright if: (1) the source is not cited, violating Article 7 on copyright management information; (2) substantial portions of the work are reproduced, violating the rightholder's economic rights under Article 9; or (3) the work is distorted in a way that harms the rightholder’s honor, infringing on moral rights under Article 5. To accommodate AI development, specific regulations integrating AI transparency principles outlined in SE Kominfo 9/2023 are required. These regulations could include obligations for AI companies to release summaries of training datasets, include Uni EropaLAs that define the responsibilities of AI developers and users, and provide disclaimers regarding AI's limitations. Regarding the fulfillment of rightholders’ economic rights, a non-exclusive blanket license through Collective Management Organizations (CMOs) as stipulated in Permenkumham 15/2024 is necessary. These regulations should be synchronized with related policies to establish legal certainty that adapts to technological advancements.

Implementation of Principal Decision Making Based on Management Information System on the Quality of Education at Batu Bolong State Elementary School, Central Lombok Regency

This study aims to determine: 1) Principal Decision Making Planning based on Management Information System on Education Quality 2) Implementation of Principal Decision Making based on Management Information System on Education Quality. 3) Supporting and Inhibiting Factors. The research method used is a qualitative approach with a case study research type. Data collection is carried out using interview, observation, and documentation techniques. As well as data analysis techniques used through three procedures, including data reduction, data presentation, and drawing conclusions. The data that has been obtained is then checked for data validity using triangulation techniques. The findings and analysis of the study show that 1) Principal Decision Making Planning based on Management Information System on Education Quality is carried out by forming a team, planning results and implementation strategies for MIS, building infrastructure and meetings with teachers. 2) In general, the implementation of Principal Decision Making based on Management Information System on Education Quality with data input, data authentication, data processing, data presentation and decision-making process based on MIS data. 3) Supporting and inhibiting factors, supporting factors are, the addition of adequate supporting facilities and infrastructure. Meanwhile, inhibiting factors are students or parents who do not yet have the devices to access the management information system.

Developing a Theoretical Framework of Export-Oriented Small Enterprises: A Multiple Case Study in an Emerging Country

Small enterprises are essential in supporting economic growth, particularly in emerging countries. Due to their constrained resources and capacities, many small businesses in developing nations encounter intricate obstacles when trying to enter the global market rapidly. The study is intended to develop a theoretical framework that can reveal the essential and integrated resources, and capabilities in the internationalization process. Instead of literature investigations, multiple case studies were adopted to explore the process of achieving the international success of Indonesia’s export-oriented small enterprises. In-depth interviews with twelve small enterprises across the culinary, fashion, and craft sectors were conducted to collect qualitative data. A content analysis followed the input–process–output–outcome structure as the basis for developing the robust framework. Based on the Resource-Based View (RBV), this study reveals the synergistic role of production, networks, marketing, learning, and legal capabilities in creating competitive advantages that support business continuity and sustainability. Research findings reveal that successful internationalization is not achieved by a single capability but through an integrated bundle of capabilities that can serve market needs. This study contributes to the literature by offering a comprehensive framework that maps the input–process–output–outcome structure of the internationalization process and offers practical insights for policymakers and practitioners aiming to enhance SME competitiveness. The results underscore the importance of capability development and government support in facilitating SME global expansion. Ultimately, this study provides a basis for further investigation into the dynamic capabilities that SMEs need to thrive in international markets.

Parametric Optimisation of Wire Electrical Discharge Machining of 15CDV6 HSLA Steel Using Taguchi Method

15CDV6 steel is a high strength bainitic High Strength Low Alloy (HSLA) steel with low concentrations of chromium, molybdenum, and vanadium as the main alloying constituents. This steel is very robust and well-tempered i.e., it has High strength and good toughness. It is used in the manufacture of transformers, electric motors, nuclear reactors, and its application to the aerospace industry for the manufacture of rocket motor cases is immense. Simplifying any process of machining is very difficult, since it basically includes forecasts of machining parameters and working requirements that are unpredictable and extremely non-linear in nature which influence the overall production Quality and costs. A key component of producing novel products, particularly for the automotive and aerospace sectors, is wire electro discharge machining (Wire-EDM). This technology got success and offers unique advantages for specific applications, such as working with hard, super-tough, brittle, or difficult-to-machine materials, producing intricate shapes, or achieving high precision. The high degree of the obtainable accuracy and the fine surface quality makes Wire-EDM valuable. The right selection of the process parameters or input variables is the considered as most important task in Wire Electro Discharge Machining of 15CDV6 HSLA steel material. The present work concentrates on optimisation of various process parameters are Pulse-on Time, Pulse-off Time, Water pressure and sensitivity on work material 15CDV6 HSLA steel, the output responses considered are Material Removal Rate (MRR), Surface Roughness (Ra), and Power Consumption (PC). Taguchi Method technique used on experimental results for optimisation and same results are analysed using Analysis of Variance (ANOVA) for identify percentage contribution of process parameters. This work helps in analysing the suitable process parameters and machining performance for machining of 15CDV6 steel and also reveals the research work area in which maximum research work can be done in future.

A Parallel Framework for Fast Charge/Discharge Scheduling of Battery Storage Systems in Microgrids

Fast charge/discharge scheduling of battery storage systems is essential in microgrids to effectively balance variable renewable energy sources, meet fluctuating demand, and maintain grid stability. To achieve this, parallel processing is employed, allowing batteries to respond instantly to dynamic conditions. By managing the complexity, high data volume, and rapid decision-making requirements in real time, parallel processing ensures that the microgrid operates with stability, efficiency, and safety. With the application of deep reinforcement learning (DRL) in scheduling algorithm design, the demand for computational power has further increased significantly. To address this challenge, we propose a Ray-based parallel framework to accelerate the development of fast charge/discharge scheduling for battery storage systems in microgrids. We demonstrate how to implement a real-world scheduling problem in the framework. We focused on minimizing power losses and reducing the ramping rate of net loads by leveraging the Asynchronous Advantage Actor Critic (A3C) algorithms and the features of the Ray cluster for real-time decision making. Multiple instances of OpenDSS were executed concurrently, with each instance simulating a distinct environment and efficiently processing input data. Additionally, Numba CUDA was utilized to facilitate GPU acceleration of shared memory, significantly enhancing the performance of the computationally intensive reward function in A3C. The proposed framework enhanced scheduling performance, enabling efficient energy management in complex, dynamic microgrid environments.