Journal of Manufacturing and Materials Processing

Research

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31 pages, 6044 KiB

Open AccessArticle

Transforming Manufacturing Quality Management with Cognitive Twins: A Data-Driven, Predictive Approach to Real-Time Optimization of Quality

by Asif Ullah, Muhammad Younas and Mohd Shahneel Saharudin

J. Manuf. Mater. Process. 2025, 9(3), 79; https://doi.org/10.3390/jmmp9030079 - 28 Feb 2025

Viewed by 673

Abstract

In the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through [...] Read more.

In the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Q_f), machine degradation (Q_m), product processing quality (Q_p), and quality inspection (Q_i). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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21 pages, 7674 KiB

Open AccessArticle

Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0

by Saman Fattahi, Bahman Azarhoushang and Heike Kitzig-Frank

J. Manuf. Mater. Process. 2025, 9(2), 62; https://doi.org/10.3390/jmmp9020062 - 17 Feb 2025

Viewed by 597

Abstract

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, quality assessment, and anomaly detection. A key advancement is the transition from static to adaptive design [...] Read more.

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, quality assessment, and anomaly detection. A key advancement is the transition from static to adaptive design of experiments (DoE), using real-time analytics for iterative refinement. This paper introduces an innovative adaptive DoE embedded in an expert system for grinding, combining data-driven and knowledge-based methodologies. The KSF Grinding Expert™ system recommends optimized grinding control variables, guided by a multi-objective optimization framework using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Gray Relational Analysis (GRA). The rule-based adaptive DoE iteratively refines recommendations through feedback and historical insights, reducing the number of trials by excluding suboptimal parameters. A case study validates the approach, demonstrating significant enhancements in process efficiency and precision. This knowledge-based adaptive strategy reduces experimental trials, adapts DoE according to different grinding processes, and can prevent critical defects such as surface cracks. In the case study, optimized results which are offered by the expert system and validated with over 90% accuracy are incorporated into the system’s knowledge base, enabling continuous improvement and reduced experimentation in future iterations. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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11 pages, 2507 KiB

Open AccessArticle

Deposition and Characterization of Fluoropolymer–Ceramic (ECTFE/Al₂O₃) Coatings via Atmospheric Plasma Spraying

by Mariem Abdennadher, Beatriz Garrido, Vicente Albaladejo-Fuentes, Irene Garcia-Cano, Anas Bouguecha and Riadh Elleuch

J. Manuf. Mater. Process. 2025, 9(2), 50; https://doi.org/10.3390/jmmp9020050 - 5 Feb 2025

Viewed by 808

Abstract

Thermal spray techniques allow coatings to be deposited from a wide range of materials with controlled thicknesses, from micrometres to millimetres. For this reason, thermal spraying can optimize performance for diverse applications across industries, ensuring strong adhesion and the durability of coated surfaces. [...] Read more.

Thermal spray techniques allow coatings to be deposited from a wide range of materials with controlled thicknesses, from micrometres to millimetres. For this reason, thermal spraying can optimize performance for diverse applications across industries, ensuring strong adhesion and the durability of coated surfaces. In this work, composite ethylene chlorotrifluoroethylene/ceramic (ECTFE/Al₂O₃) coatings with different ceramic ratios were deposited by plasma spraying. Four coatings were produced by spraying blended powders consisting of pure ECTFE and ECTFE with 5%, 10%, and 15 wt.% Al₂O₃. The effect of varying the ceramic ratio on the coatings’ microstructure and properties was investigated. Morphology and particle size distributions were determined for the raw powders. The microstructural examination of the coatings showed proportional increases in Al₂O₃ content. An improvement in adhesion was achieved with ceramic in the coatings from 5 wt.% Al₂O₃. Enhanced friction coefficients were obtained with ceramic, except for 15 wt.% Al₂O₃. Taber abrasion tests showed a minimal influence on ceramic content. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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32 pages, 45465 KiB

Open AccessArticle

Interfacial Stability of Additively Manufactured Alloy 625–GRCop-42 Bimetallic Structures

by Ariel Rieffer and Andrew Wessman

J. Manuf. Mater. Process. 2025, 9(2), 34; https://doi.org/10.3390/jmmp9020034 - 24 Jan 2025

Viewed by 869

Abstract

This study examines the diffusion behavior, thermal stability, and mechanical properties of the bimetallic interface between additively manufactured copper alloy GRCop-42 and nickel alloy 625 (UNS N06625) following elevated temperature exposure at service-relevant conditions for high-temperature superalloys. The copper alloy was additively manufactured [...] Read more.

This study examines the diffusion behavior, thermal stability, and mechanical properties of the bimetallic interface between additively manufactured copper alloy GRCop-42 and nickel alloy 625 (UNS N06625) following elevated temperature exposure at service-relevant conditions for high-temperature superalloys. The copper alloy was additively manufactured using laser powder bed fusion. The nickel alloy was subsequently deposited directly onto the copper alloy using powder-based directed energy deposition. The samples were held at a temperature of 816 °C (1500° F) for varying exposure times between 50 and 500 h. Significant material loss (averaging ~430

μ m

at 50 h and ~1830

μ m

at 500 h) due to oxidation was noted in the copper alloy. The bondline interface was examined using optical microscopy as well as electron microprobe analysis. Composition maps from the electron microprobe showed the formation of oxides in the copper alloy and Laves phase in the nickel alloy at thermal exposure times of 200 h or more. By analyzing diffusion across the bondline, this study demonstrates the ability of machine learning-based diffusion models to predict diffusion coefficients of copper into alloy 625 (

2.38 \times 10^{- 12} c m^{2} / s

) and of nickel into GRCop-42 (

1.90 \times 10^{- 12} c m^{2} / s

) and the ability of commercially available diffusion code (Pandat) to provide reasonably accurate diffusion profiles for this system. Tensile and fatigue tests were performed in the as-built and 200 h thermal exposure conditions. The thermally exposed samples exhibited an average 18.6% reduction in yield strength compared to the as-built samples. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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23 pages, 3687 KiB

Open AccessArticle

End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components

by Amaia Arregi, Aitor Barrutia and Iñigo Bediaga

J. Manuf. Mater. Process. 2025, 9(1), 12; https://doi.org/10.3390/jmmp9010012 - 3 Jan 2025

Viewed by 935

Abstract

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a [...] Read more.

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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26 pages, 5235 KiB

Open AccessArticle

Flexible Symbiosis for Simulation Optimization in Production Scheduling: A Design Strategy for Adaptive Decision Support in Industry 5.0

by Mohaiad Elbasheer, Francesco Longo, Giovanni Mirabelli and Vittorio Solina

J. Manuf. Mater. Process. 2024, 8(6), 275; https://doi.org/10.3390/jmmp8060275 - 30 Nov 2024

Viewed by 950

Abstract

In the rapidly evolving landscape of Industry 4.0 and the transition towards Industry 5.0, manufacturing systems face the challenge of adapting to dynamic, hyper-customized demands. Current Simulation Optimization (SO) systems struggle with the flexibility needed for quick reconfiguration, often requiring time-consuming, resource-intensive efforts [...] Read more.

In the rapidly evolving landscape of Industry 4.0 and the transition towards Industry 5.0, manufacturing systems face the challenge of adapting to dynamic, hyper-customized demands. Current Simulation Optimization (SO) systems struggle with the flexibility needed for quick reconfiguration, often requiring time-consuming, resource-intensive efforts to develop custom models. To address this limitation, this study introduces an innovative SO design strategy that integrates three flexible simulation modeling techniques—template-based, structural modeling, and parameterization. The goal of this integrated design strategy is to enable the rapid adaptation of SO systems to diverse production environments without extensive re-engineering. The proposed SO versatility is validated across three manufacturing scenarios (flow shop, job shop, and open shop scheduling) using modified benchmark instances from Taillard’s dataset. The results demonstrate notable effectiveness in optimizing production schedules across these diverse scenarios, enhancing decision-making processes, and reducing SO development efforts. Unlike conventional SO system design, the proposed design framework ensures real-time adaptability, making it highly relevant to the dynamic requirements of Industry 5.0. This strategic integration of flexible modeling techniques supports efficient decision support, minimizes SO development time, and reinforces manufacturing resilience, therefore sustaining competitiveness in modern industrial ecosystems. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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22 pages, 13474 KiB

Open AccessArticle

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

by Ángel-Gabriel Salinas-Martínez, Joaquín Cunillé-Rodríguez, Elías Aquino-López and Angel-Iván García-Moreno

J. Manuf. Mater. Process. 2024, 8(6), 274; https://doi.org/10.3390/jmmp8060274 - 30 Nov 2024

Viewed by 1684

Abstract

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, [...] Read more.

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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23 pages, 1064 KiB

Open AccessArticle

A Universal Framework for Skill-Based Cyber-Physical Production Systems

by Max Hossfeld and Andreas Wortmann

J. Manuf. Mater. Process. 2024, 8(5), 221; https://doi.org/10.3390/jmmp8050221 - 2 Oct 2024

Viewed by 1367

Abstract

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and [...] Read more.

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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25 pages, 5085 KiB

Open AccessArticle

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

by Asif Ullah and Muhammad Younas

J. Manuf. Mater. Process. 2024, 8(5), 214; https://doi.org/10.3390/jmmp8050214 - 28 Sep 2024

Cited by 3 | Viewed by 2316

Abstract

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, [...] Read more.

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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19 pages, 4551 KiB

Open AccessArticle

Development of a Method and a Smart System for Tool Critical Life Real-Time Monitoring

by Shih-Ming Wang, Wan-Shing Tsou, Jian-Wei Huang, Shao-En Chen and Chia-Che Wu

J. Manuf. Mater. Process. 2024, 8(5), 194; https://doi.org/10.3390/jmmp8050194 - 5 Sep 2024

Viewed by 1394

Abstract

Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. [...] Read more.

Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. This study introduces a novel, indirect approach that leverages spindle-load current variations as a proxy for cutting force analysis. Compared to conventional methods relying on machining vibration or direct cutting force measurement, this technique provides a safer, simpler, and more cost-effective means of data aquisition, with reduced computational demands. Consequently, it is ideally suited for real-time monitoring and long-term analyses such as tool-life prediction and surface-roughness evolution induced by tool wear. An intelligent tool wear monitoring system was developed based on spindle-load current data. The system employs extensive cutting experiments to identify and analyze the correlation between tool wear and spindle-load current signal patterns. By establishing a tool wear near-end-of-life threshold, the system enables intelligent monitoring using C#. Experimental validation under both roughing and finishing conditions demonstrated the system’s exceptional diagnostic accuracy and reliability. The results demonstrate that the current ratio threshold value has good universality in different materials, indicating that monitoring the machining current ratio to estimate the degree of tool wear is a feasible research direction, and that the average error between the experimental surface-roughness measurement value and the predicted value was 10%. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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Review

Jump to: Research

23 pages, 2096 KiB

Open AccessReview

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

by Getachew Ambaye, Enkhsaikhan Boldsaikhan and Krishna Krishnan

J. Manuf. Mater. Process. 2024, 8(2), 79; https://doi.org/10.3390/jmmp8020079 - 16 Apr 2024

Cited by 5 | Viewed by 6681

Abstract

Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic [...] Read more.

Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic soft robots, which are composed of compliant materials like silicones, offer compelling solutions to manipulating delicate objects, operating in unstructured environments, and facilitating safe human–robot interactions. However, despite their numerous advantages, there are some fundamental challenges to overcome, which particularly concern motion precision and stiffness compliance in performing physical tasks that involve external forces. In this regard, enhancing the operation performance of soft robots necessitates intricate, complex structural designs, compliant multifunctional materials, and proper manufacturing methods. The objective of this literature review is to chronicle a comprehensive overview of soft robot design, manufacturing, and operation challenges in conjunction with recent advancements and future research directions for addressing these technical challenges. Full article

(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)

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