Search
15 articles for “Chemical machining”
-
Utilizing Chemical Machining Methods in the Metalworking Sector
Abstract: Chemical machining is a kind of material removal technique used to produce desired shapes and dimensions by selectively or completely removing material through carefully regulated chemical attack using etchant solutions, which are solutions of acids or alkalis. One type of chemical machining is chemical milling, also known as contour machining, etching, or chemiluminescence. The aerospace industry uses chemical milling to remove thin layers of material from extruded parts for aircraft …
Published in International Journal of Manufacturing and Production Engineering · Vol. 2, Issue 1, 2024 · pp. 17–23 Read article
-
AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects
Abstract: The chemical industry, a key growth indicator of the global manufacturing ecosystem, is experiencing a digital transformation driven mainly by advancements in Artificial Intelligence (AI) and Machine Learning (ML) in this sector. These technologies are totally revolutionizing current and traditional methodologies by significantly improving process efficiency, reducing costs of manufacturing, accelerating R&D, and improving safety and sustainability standards. Proper utilization of Artificial intelligence (AI) and machine learning (ML) in chemical …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
-
QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
-
Fruit Adulteration Detection Utilizing Machine Learning Methods
Abstract: A device utilizing Internet of Things (IoT) technology was developed for the identification of fruit adulteration through machine learning methods, specifically targeting formalin content assessment. The identification of the fruits based on their extracted traits has been accomplished using a variety of machine-learning techniques. The formalin concentration can be detected as an estimate of the generated voltage of any fruit via an Arduino Uno board 3 and a volatile compound …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 1, 2023 · pp. 32–45 Read article
-
Enhancing Machinability in the Heat Treatment of Inconel 718: A Comprehensive Review
Abstract: The increasing need for materials with high strength and heat resistance, particularly in aerospace applications, creates machining issues. These materials are generally difficult to machine because to their strong wear resistance, abrasion resistance, and low heat conductivity. This produces strong cutting forces and temperatures, resulting in a limited tool life. variances in the microstructures of various materials can induce variations in machinability due to variances in chemical composition, casting, forging …
Published in International Journal of Energy and Thermal Applications · Vol. 2, Issue 1, 2024 · pp. 9–20 Read article
-
Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
-
Machine Learning for Finding Materials for Membranes
Abstract: Traditionally, finding and improving membrane materials has depended on trial-and-error experiments, which can take a long time, cost a lot of money, and only cover a small area. Recent improvements in machine learning (ML) have the potential to change the way membrane materials are designed by making it possible to make predictions about performance, selectivity, and stability based on data. ML algorithms can find hidden links between the structure, composition, …
Published in International Journal of Membranes · Vol. 3, Issue 1, 2026 · pp. 1–7 Read article
-
Fabrication of Natural Fibre Reinforced Polymer Composite by Hand Lay-Up Process
Abstract: The recent development in technologies has increased the usage of non-renewable resources during pro- duction due to remarkable properties. However, it has been a threat and unable to be replenished once used. In addition to this, natural fibre strengthens in a more significant way and it’s widely applicable in polymer composites materials. Furthermore, the use of plant fibres in polymer composites is due to its low cost and significant properties. …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 13–19 Read article
-
Impact of Lubricant Additives on Friction Reduction and Wear Prevention in Machinery
Abstract: Lubrication is essential for ensuring the lifespan and operating efficiency of machinery because it minimizes wear and reduces friction. Modern lubricants are made up of several chemical compounds called lubricant additives, which are essential to boost the lubricant's ability to reduce wear and friction. This study thoroughly examines how lubricant additives affect wear prevention and friction reduction in equipment. This work attempts to break down the principles behind the efficacy …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 1, Issue 2, 2023 · pp. 1–6 Read article
-
Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
-
From Molecular Mechanics to Nanocomposites: Engineering Polytetrafluoroethylene (PTFE) for High-performance Coating Applications
Abstract: Polytetrafluoroethylene (PTFE) exhibits remarkable chemical inertness, hydrophobicity, antifriction, self-lubrication, and high-temperature resilience, making it an ideal polymer for various industrial applications, including coatings for medical implants, machinery parts, and corrosion-resistant structures. This study presents a comprehensive analysis of PTFE’s structural properties, focusing on helix reversals within its helical carbon-fluorine chains and their effects on mechanical behavior. The phase transitions of PTFE at temperatures from ambient to high (198°C and above) …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 2, 2024 · pp. 14–19 Read article
-
Recent Applications and Influences of Artificial Intelligence (AI) In Chemical and Allied Sciences
Abstract: Artificial Intelligence (AI), the future tool of mankind that can revolutionise scientific research by making it faster, add more efficiently and accurately. During the pandemic situation, the scientific community was parted into two distinct groups, the computation-dependent community could easily continue their research work from their resident, while the works of the other group of researchers with lab-oriented research stopped entirely. In this connection use of AI becomes important and …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 22–48 Read article
-
Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
-
Advancements in Molecular Engineering: Innovations at the Nexus of Chemistry and Technology
Abstract: Molecular engineering, a frontier of chemistry, merges precision and innovation to design and assemble molecular structures with unprecedented control. This abstract explores recent advances, highlighting key breakthroughs and their transformative impacts. Starting with its roots in chemical synthesis and materials science, it traces the evolution towards rational design driven by computational tools and advanced characterization techniques, enabling tailored molecular architectures. A focal point is programmable molecules, where DNA nanotechnology principles …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 2, 2023 · pp. 37–43 Read article
-
DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives
Abstract: This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 41–56 Read article