Search Results (245)

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry. Full article

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

The Moroccan garment industry faces challenges in scheduling small order batches, often hindered by traditional product family-based methods that increase downtime by 15–20%. This study proposes a clustering-based scheduling approach, grouping garments by technological times rather than product families to reduce changeovers and downtime by 30–35%. A case study in a Moroccan factory with 50–100-unit batches showed a 20% lead time reduction and a 15% productivity boost. Using methods like K-Means, the approach enhances planning flexibility and resource use. This methodology offers a scalable solution for optimizing production and maintaining competitiveness in fast fashion markets. Full article

Reducing setup costs and improving product quality are critical objectives in a sustainable production processes. The significance of these goals lies in their direct impact on efficiency. It affects competitiveness and customer satisfaction. Businesses can reduce setup costs to maximize resource usage. It can reduce downtime between production runs and improve overall operational agility. Sustained performance and expansion in contemporary manufacturing environments focus on setup cost reduction and product quality improvement. The present paper discusses a production inventory model for the product, which produces by-products as secondary products from the same manufacturing process. Setup cost is reduced for the setup of production and refining processes. A production process may change from being under control to an uncontrolled one. As a result of this, imperfect products are formed. This paper considers product quality improvement for both produced and processed items. The outcome shows that dealing with by-products helps make the system more profitable. Sensitivity analysis is performed for various costs and parameters. Mathematica 11 software was used for calculation and graphical work. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

We investigated the application of artificial intelligence (AI) technology for the inspection of semiconductor process equipment to address key issues such as low production efficiency and high equipment failure rates. The semiconductor industry, being central to modern technology, requires complex and precise processes where even minor defects lead to product failures, negatively impacting yield and increasing costs. Traditional inspection methods are not adequate for modern high-precision, high-efficiency production demands. By integrating advanced AI technologies, such as machine learning, deep learning, and pattern recognition, large volumes of experimental data are collected and analyzed to optimize process parameters, enhance stability, and improve product yield. By using AI, the identification and classification of defects are automated to predict potential equipment failures and reduce downtime and overall costs. By combining AI with automated optical inspection (AOI) systems, a widely used defect detection tool has been developed for semiconductor manufacturing. However, under complex conditions, AOI systems are prone to producing false positives, resulting in overkill rates above 20%. This wastes perfect products and increases the cost due to the need for manual re-inspection, hindering production efficiency. This study aims to improve wafer inspection accuracy using AI technology and reduce false alarms and overkill rates. By developing intelligent detection models, the system automatically filters out false defects and minimizes manual intervention, boosting inspection efficiency. We explored how AI is used to analyze inspection data to identify process issues and optimize workflows. The results contribute to the reduction in labor and time costs, improving equipment performance, and significantly benefitting semiconductor production management. The AI-driven method can be applied to other manufacturing processes to enhance efficiency and product quality and support the sustainable growth of the semiconductor industry. Full article

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance. Full article

Large-scale heating, ventilation, and air conditioning (HVAC) control systems in pharmaceutical manufacturing are characterized by complex operational parameters, delayed and often challenging fault detection, and stringent regulatory compliance requirements. To address these issues, this study presents an innovative data-driven fault detection framework that integrates Principal Component Analysis (PCA) with Nonlinear State Estimation Technology (NSET), specifically tailored for highly regulated pharmaceutical production environments. A dataset comprising 13,198 operational records was collected from the SCADA system of a pharmaceutical facility in Zhejiang, China. The data underwent preprocessing and key parameter extraction, after which a nonlinear state estimation predictive model was constructed, with PCA applied for dimensionality reduction and sensitivity enhancement. Fault detection was performed by monitoring deviations in the mixing room temperature, identifying faults when the residuals between observed and predicted values exceeded a statistically determined threshold (mean ± three standard deviations), in accordance with the Laida criterion. The framework’s effectiveness was validated through comparative analysis before and after documented fault events, including temperature sensor drift and abnormal equipment operation. Experimental results demonstrate that the proposed PCA-NSET model enables timely and accurate detection of both gradual and abrupt faults, facilitating early intervention and reducing potential production downtime. Notably, this framework outperforms traditional fault detection methods by providing higher sensitivity and specificity, while also supporting continuous quality assurance and regulatory compliance in pharmaceutical HVAC applications. The findings underscore the practical value and novelty of the integrated PCA-NSET approach for robust, real-time fault detection in mission-critical industrial environments. Full article

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies. Full article

Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article