Journal of Manufacturing and Materials Processing

Research

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16 pages, 3244 KiB

Open AccessArticle

Research on Machining Quality Prediction Method Based on Machining Error Transfer Network and Grey Neural Network

by Dongyue Qu, Wenchao Liang, Yuting Zhang, Chaoyun Gu and Yong Zhan

J. Manuf. Mater. Process. 2024, 8(5), 203; https://doi.org/10.3390/jmmp8050203 - 18 Sep 2024

Viewed by 613

Abstract

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, [...] Read more.

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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14 pages, 2964 KiB

Open AccessArticle

Revealing the Relationship between Critical Inlet Velocity and a Double-Layer Oxide Film Combined with Low-Pressure Casting Technology

by Ziao Qiu, Chaojun Zhang, Lunyong Zhang, Fuyang Cao, Hongxian Shen, Zhishuai Jin, Guanyu Cao, Xinyi Zhao, Heqian Song and Jianfei Sun

J. Manuf. Mater. Process. 2024, 8(4), 168; https://doi.org/10.3390/jmmp8040168 - 3 Aug 2024

Viewed by 783

Abstract

In the context of low-pressure casting, an excessive inlet velocity may result in the introduction of an oxide film and air into a liquid metal, leading to the formation of a two-layer film structure within the casting. Such defects can significantly degrade the [...] Read more.

In the context of low-pressure casting, an excessive inlet velocity may result in the introduction of an oxide film and air into a liquid metal, leading to the formation of a two-layer film structure within the casting. Such defects can significantly degrade the mechanical properties of the castings. In order to optimize the advantages of low-pressure casting, an empirically designed equation for the inlet velocity was formulated and the concept of critical inlet velocity was further refined. A comprehensive numerical simulation was conducted to meticulously analyze the liquid metal spreading phase within the cavity. Subsequently, low-pressure casting experiments were carried out with actual castings of an A357 alloy, using two different entrance velocities—one critical and the other exceeding the critical entrance velocity. Tensile test specimens were extracted from the castings for the comparative evaluation of mechanical properties. It was observed that the average tensile strength of specimens cast at the critical inlet velocity exhibited a notable 16% enhancement. In contrast, specimens cast at velocities exceeding the critical inlet velocity manifested the presence of double oxide film defects. This evidence suggests that casting at a velocity faster than the critical inlet velocity leads to the formation of double oxide film defects, which in turn reduces the mechanical properties of the castings. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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20 pages, 6242 KiB

Open AccessArticle

A New Grinding Wheel Design with a 3D Internal Cooling Structure System

by Sharlane Costa, Paulina Capela, Maria S. Souza, José R. Gomes, Luís Carvalho, Mário Pereira and Delfim Soares

J. Manuf. Mater. Process. 2024, 8(4), 159; https://doi.org/10.3390/jmmp8040159 - 26 Jul 2024

Viewed by 1203

Abstract

This work discusses challenges in conventional grinding wheels: heat-induced tool wear and workpiece thermal damage. While textured abrasive wheels improve heat dissipation, the current surface-only methods, such as those based on laser and machining, have high renewal costs. The proposed manufacturing technology introduces [...] Read more.

This work discusses challenges in conventional grinding wheels: heat-induced tool wear and workpiece thermal damage. While textured abrasive wheels improve heat dissipation, the current surface-only methods, such as those based on laser and machining, have high renewal costs. The proposed manufacturing technology introduces an innovative 3D cooling channel structure throughout the wheel, enabling various channel geometries for specific abrasive wheel applications. The production steps were designed to accommodate the conventional pressing and sintering phases. During pressing, a 3D organic structure was included in the green body. A drying cycle eliminated all present fluids, and a sintering one burnt away the structure, revealing channels in the final product. Key parameters, such as binder type/content and heating rate, were optimized for reproducibility and scalability. Wear tests showed a huge efficiency increase (>100%) in performance and durability compared of this system to conventional wheels. Hexagonal channel structures decreased the wear rates by 64%, displaying superior wear resistance. Comprehensive CFD simulations evaluated the coolant flow through the cooling channels. This new design methodology for three-dimensionally structured grinding wheels innovates the operation configuration by delivering the coolant directly where it is needed. It allows for increasing the overall efficiency by optimizing cooling, reducing tool wear, and enhancing manufacturing precision. This 3D channel structure eliminates the need for reconditioning, thus lowering the operation costs. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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17 pages, 3887 KiB

Open AccessArticle

Investigation of Metal Powder Blending for PBF-LB/M Using Particle Tracing with Ti-6Al-4V

by Ina Ludwig, Anatol Gerassimenko and Philipp Imgrund

J. Manuf. Mater. Process. 2024, 8(4), 151; https://doi.org/10.3390/jmmp8040151 - 16 Jul 2024

Viewed by 1124

Abstract

Laser-based powder bed fusion of metals (PBF-LB/M) is the most used additive manufacturing (AM) technology for metal parts. Nevertheless, challenges persist in effectively managing metal powder, particularly in blending methodologies in the choice of blenders as well as in the verification of blend [...] Read more.

Laser-based powder bed fusion of metals (PBF-LB/M) is the most used additive manufacturing (AM) technology for metal parts. Nevertheless, challenges persist in effectively managing metal powder, particularly in blending methodologies in the choice of blenders as well as in the verification of blend results. In this study, a bespoke laboratory-scale AM blender is developed, tailored to address these challenges, prioritizing low-impact blending to mitigate powder degradation. As a blending type, a V-shape tumbling geometry meeting the requirements for laboratory AM usage is chosen based on a literature assessment. The implementation of thermal oxidation as a powder marking technique enables particle tracing. Blending validation is achieved using light microscopy for area measurement based on binary image processing. The powder size and shape remain unaffected after marking and blending. Only a small narrowing of the particle size distribution is detected after 180 min of blending. The V-shape tumbling blender efficiently yields a completely random state in under 10 min for rotational speeds of 20, 40, and 60 rounds per minute. In conclusion, this research underscores the critical role of blender selection in AM and advocates for continued exploration to refine powder blending practices, with the aim of advancing the capabilities and competitiveness of AM technologies. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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16 pages, 7266 KiB

Open AccessArticle

Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining

by Mohammadjafar Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi and Amir Alinaghizadeh

J. Manuf. Mater. Process. 2024, 8(1), 41; https://doi.org/10.3390/jmmp8010041 - 14 Feb 2024

Cited by 5 | Viewed by 2382

Abstract

Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior [...] Read more.

Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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20 pages, 7381 KiB

Open AccessArticle

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

by Daniele Farioli, Ertuğrul Kaya, Andrea Fumagalli, Paolo Cattaneo and Matteo Strano

J. Manuf. Mater. Process. 2023, 7(3), 92; https://doi.org/10.3390/jmmp7030092 - 8 May 2023

Cited by 4 | Viewed by 3442

Abstract

The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring [...] Read more.

The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring progressive wear or at the detection of sudden failures or anomalies. Early detection of tool failure is the method preferred by the recent literature on data management in sheet metal stamping. However, the stamping of small parts poses challenges due to multiple tools and signals and limited visibility of die wear, requiring management of multiple sensors and data sources. This paper proposes a failure prevention approach for progressive die stamping using global and local force sensors with upper bounds for maximum values to indicate unhealthy conditions. The methodology was tested on millions of small washers made of carbon steel. The stamping process was implemented using a servo-press with a high rate. The press was equipped with eight in-process sensors, including strain gauges, thin foil force sensors, and acoustic sensors. The data of material properties, maintenance reports, statistical process control data, and in-process sensors were collected and stored in a data lake. By combining the in-process sensor acquisition with the corresponding log events and maintenance data in the same time span, it is possible to look for correlations among the variables and build an effective tool health prevention policy. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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9 pages, 4681 KiB

Open AccessCommunication

Effect of Welding Orientation in Angular Distortion in Multipass GMAW

by Lucas Martins Garcia, Verônica Teixeira Noronha and João Ribeiro

J. Manuf. Mater. Process. 2021, 5(2), 63; https://doi.org/10.3390/jmmp5020063 - 18 Jun 2021

Cited by 5 | Viewed by 3626

Abstract

The use of the welding process on an industrial scale has become significant over the years and is currently among the main processes for joining metallic materials. Along the weld, structural changes occur in the vicinity of the joint. These thermal stresses and [...] Read more.

The use of the welding process on an industrial scale has become significant over the years and is currently among the main processes for joining metallic materials. Along the weld, structural changes occur in the vicinity of the joint. These thermal stresses and geometric distortions are mostly undesirable and are complex to predict with precision. Using S235JR steel as the base material, laboratory experiments were carried out using the multipass GMAW process, with the aim of investigating the influence of the welding direction on angular distortion. To measure the distortions, a methodology was applied using equipment to identify the coordinates in the operational space with metrological precision. Through metrological and statistical analyses, we found that the orientation factor significantly influenced the final distortions and that the alternated orientation sequence resulted in less distortions. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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Review

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30 pages, 10193 KiB

Open AccessReview

Review on the Application of the Attention Mechanism in Sensing Information Processing for Dynamic Welding Processes

by Jingyuan Xu, Qiang Liu, Yuqing Xu, Runquan Xiao, Zhen Hou and Shanben Chen

J. Manuf. Mater. Process. 2024, 8(1), 22; https://doi.org/10.3390/jmmp8010022 - 28 Jan 2024

Cited by 1 | Viewed by 1997

Abstract

Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement [...] Read more.

Arc welding is the common method used in traditional welding, which constitutes the majority of total welding production. The traditional manual and manual teaching welding method has problems with high labor costs and limited efficiency when faced with mass production. With the advancement in technology, intelligent welding technology is expected to become a solution to this problem in the future. To achieve the intelligent welding process, modern sensing technology can be employed to effectively simulate the welder’s sensory perception and cognitive abilities. Recent studies have advanced the application of sensing technologies, leading to the advancement in intelligent welding process. The review is divided into two aspects. First, the theory and applications of various sensing technologies (visual, sound, arc, spectral signal, etc.) are summarized. Then, combined with the generalization of neural networks and attention mechanisms, the development trends in welding sensing information processing and modeling technology are discussed. Based on the existing research results, the feasibility, advantages, and development direction of attention mechanisms in the welding field are analyzed. In the end, a brief conclusion and remarks are presented. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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20 pages, 1658 KiB

Open AccessReview

Insight into the Expected Impact of Sustainable Development in the Context of Industry 4.0: A Documentary Analysis Approach Based on Multiple Case Studies across the World

by Wilian Jesús Pech-Rodríguez, Eddie Nahúm Armendáriz-Mireles, Gladis Guadalupe Suárez-Velázquez, Carlos Adrián Calles-Arriaga and Enrique Rocha-Rangel

J. Manuf. Mater. Process. 2022, 6(3), 55; https://doi.org/10.3390/jmmp6030055 - 20 May 2022

Cited by 1 | Viewed by 3927

Abstract

Although industry 4.0 has gained increased attention in the industry, academic, and governmental fields, there is a lack of information about the relationship between this digital transformation and sustainable development. This work explores the concept of sustainability applied in industry 4.0 and the [...] Read more.

Although industry 4.0 has gained increased attention in the industry, academic, and governmental fields, there is a lack of information about the relationship between this digital transformation and sustainable development. This work explores the concept of sustainability applied in industry 4.0 and the main advantages that this revolution incorporates into society. To this end, a conscientiously documented investigation was conducted by reviewing actual case studies or scenarios where sustainability was applied in different manufacturing industries, enterprises, or research fields worldwide. A critical and descriptive analysis of the information was performed to identify the main tools and procedures that can be implemented in the industry to address the triple bottom line perspective of industry 4.0, and the results are presented in this document. From the analysis, it was observed that currently, I4.0 has been mainly adopted to improve efficiency and cost reduction in manufacturing companies. However, since only a few enterprises embrace the social paradigm of I4.0, a significant gap in understanding and unbalance is visualized. Therefore, we conclude that there is a lack of information on social benefits and the barriers that must be overcome from the social perspective. On the other hand, this work highlights the importance of adopting industry 4.0 as a positive way to improve the performance of emerging technologies, such as fuel cells, solar cells, and wind turbines, while producing products or services with high efficiency and profitability incomes. For practitioners, this work can provide insightful information about the real implications of I4.0 from a sustainability perspective in our daily life and the possible strategies to improve sustainable development. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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17 pages, 700 KiB

Open AccessReview

Causal Discovery in Manufacturing: A Structured Literature Review

by Matej Vuković and Stefan Thalmann

J. Manuf. Mater. Process. 2022, 6(1), 10; https://doi.org/10.3390/jmmp6010010 - 14 Jan 2022

Cited by 50 | Viewed by 9208

Abstract

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality [...] Read more.

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed. Full article

(This article belongs to the Special Issue Industry 4.0: Manufacturing and Materials Processing)

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