Journal of Manufacturing and Materials Processing

Research

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17 pages, 1316 KiB

Open AccessArticle

A Step beyond Reliability in the Industry 4.0 Era: Operator-Leveraged Manufacturing

by Alejandro Muro Belloso, Kerman López de Calle Etxabe, Eider Garate Perez and Aitor Arnaiz

J. Manuf. Mater. Process. 2024, 8(5), 215; https://doi.org/10.3390/jmmp8050215 - 28 Sep 2024

Viewed by 812

Abstract

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a [...] Read more.

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a manufacturing scenario involving a circular blade rubber cutting machine, where the goal is to minimize downtime. Historical cutting data are available, and the aim is to provide the machine operators with an intuitive tool that helps them reduce this downtime. This work demonstrates how, in an Industry 4.0 environment, data can be leveraged to minimize downtime. To achieve this, different survival model approaches are compared, a Health Index (HI) is developed, and the model deployment is analysed, highlighting the importance of understanding the model as a dynamic system in which the operator plays a key role. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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15 pages, 3553 KiB

Open AccessArticle

Business Models Definition for Next-Generation Vision Inspection Systems

by Francesco Lupi, Antonio Maffei and Michele Lanzetta

J. Manuf. Mater. Process. 2024, 8(4), 161; https://doi.org/10.3390/jmmp8040161 - 27 Jul 2024

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Abstract

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the [...] Read more.

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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22 pages, 8399 KiB

Open AccessArticle

Process Optimization and Distortion Prediction in Directed Energy Deposition

by Adem Ben Hammouda, Hatem Mrad, Haykel Marouani, Ahmed Frikha and Tikou Belem

J. Manuf. Mater. Process. 2024, 8(3), 116; https://doi.org/10.3390/jmmp8030116 - 30 May 2024

Viewed by 995

Abstract

Directed energy deposition (DED), a form of additive manufacturing (AM), is gaining traction for its ability to produce complex metal parts with precise geometries. However, defects like distortion, residual stresses, and porosity can compromise part quality, leading to rejection. This research addresses this [...] Read more.

Directed energy deposition (DED), a form of additive manufacturing (AM), is gaining traction for its ability to produce complex metal parts with precise geometries. However, defects like distortion, residual stresses, and porosity can compromise part quality, leading to rejection. This research addresses this challenge by emphasizing the importance of monitoring process parameters (overlayer distance, powder feed rate, and laser path/power/spot size) to achieve desired mechanical properties. To improve DED quality and reliability, a numerical approach is presented and compared with an experimental work. The parametric finite element model and predictive methods are used to quantify and control material behavior, focusing on minimizing residual stresses and distortions. Numerical simulations using the Abaqus software 2022 are validated against experimental results to predict distortion and residual stresses. A coupled thermomechanical analysis model is employed to understand the impact of thermal distribution on the mechanical responses of the parts. Finally, new strategies based on laser scan trajectory and power are proposed to reduce residual stresses and distortions, ultimately enhancing the quality and reliability of DED-manufactured parts. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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12 pages, 1160 KiB

Open AccessArticle

Green Innovation Practices: A Case Study in a Foundry

by Gianluca Fratta, Ivan Stefani, Sara Tapola and Stefano Saetta

J. Manuf. Mater. Process. 2024, 8(3), 111; https://doi.org/10.3390/jmmp8030111 - 26 May 2024

Viewed by 1532

Abstract

The foundry industry is responsible for the production of several potentially polluting and hazardous compounds. One of the major sources of pollution is the use of organic binders for the manufacturing of sand cores and sand moulds. To address this problem, in recent [...] Read more.

The foundry industry is responsible for the production of several potentially polluting and hazardous compounds. One of the major sources of pollution is the use of organic binders for the manufacturing of sand cores and sand moulds. To address this problem, in recent years, the use of low-emission products, known as inorganic binders, has been proposed. Their use in ferrous foundries, otherwise, is limited due to some problematic features that complicate their introduction in the manufacturing process, as often happens when a breakthrough innovation is introduced. In light of this, the aim of this work is to provide a Green Innovation Practice (GIP) to manage the introduction of green breakthrough innovations, as previously described, within an existing productive context. This practice was applied to better manage the experimental phase of the Green Casting Life Project, which aims to evaluate the possibility of using inorganic binders for the production of ferrous castings. After describing the state of the art of GIPs and their application in manufacturing contexts, the paper described the proposed GIP and its application to a real case consisting of testing inorganic binders in a ferrous foundry. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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15 pages, 8219 KiB

Open AccessArticle

An Experiment-Based Variable Compensation Method to Improve the Geometric Accuracy of Sub-mm Features Fabricated by Stereolithography (SLA)

by Francesco Modica, Vito Basile and Irene Fassi

J. Manuf. Mater. Process. 2024, 8(3), 90; https://doi.org/10.3390/jmmp8030090 - 29 Apr 2024

Viewed by 1246

Abstract

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via stereolithography (SLA) of features at the sub-mm scale. Deviations in sub-mm hemispherical cavity diameters were detected and measured on customized samples by confocal microscopy. The characterization and [...] Read more.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via stereolithography (SLA) of features at the sub-mm scale. Deviations in sub-mm hemispherical cavity diameters were detected and measured on customized samples by confocal microscopy. The characterization and experimental observations of samples allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on cavity diameters were up to −46%. A compensation method was defined and implemented, resulting in relevant improvements in dimensional accuracy. However, measurements on sub-mm cavities having different sizes revealed that a constant compensation parameter (i.e., C = 85, 96, 120 μm) is not fully effective at the sub-mm scale, where average errors remain at −24%, −18.8%, and −16% for compensations equal to 85, 96 and 120 μm, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the sub-mm feature size C = f(D). Results show a sharp improvement in dimensional accuracy on sub-mm cavity fabrication, with errors consistently below +8.2%. The proposed method can be extended for the fabrication of any sub-mm features without restrictions on the specific technology implementation. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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13 pages, 4353 KiB

Open AccessArticle

Additive In-Time Manufacturing of Customised Orthoses

by Christian Friedrich, Stephan Rothstock, Laura Slabon and Steffen Ihlenfeldt

J. Manuf. Mater. Process. 2024, 8(2), 63; https://doi.org/10.3390/jmmp8020063 - 21 Mar 2024

Viewed by 1898

Abstract

Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the [...] Read more.

Additive manufacturing of plastic components in medical technology enables greater freedom of design when designing patient-specific products, in particular, in production of customised medical products, such as orthoses. In the present contribution, the advantages of a digital process chain are combined, from the 3D scan of the patient to CAD-supported modelling of the corrective form and the orthosis design until the path planning of a printable geometry. The main disadvantages of current additive printing techniques, such as the fused filament fabrication (FFF) process, are high printing times (>12 h) for larger components as well as the low degree of freedom in the 2.5D printing technique that prevent the subsequent application of geometry features to the product. The fast SEAMHex (Screw Extrusion Additive Manufacturing) printing technology with a hexapod kinematic printing bed provides a solution to the mentioned difficulties. Consequently, the high-performance printer has been prepared for the individual requirements of medical technology in terms of materials and geometries. An effective additive manufacturing process has been realised and tested in combination with a digital process chain for orthosis modelling. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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14 pages, 2479 KiB

Open AccessArticle

Innovative Smart Drilling with Critical Event Detection and Material Classification

by Kantawatchr Chaiprabha and Ratchatin Chancharoen

J. Manuf. Mater. Process. 2023, 7(5), 155; https://doi.org/10.3390/jmmp7050155 - 23 Aug 2023

Viewed by 2201

Abstract

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the [...] Read more.

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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Review

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15 pages, 2867 KiB

Open AccessReview

Optimizing Milling Parameters for Enhanced Machinability of 3D-Printed Materials: An Analysis of PLA, PETG, and Carbon-Fiber-Reinforced PETG

by Mohamad El Mehtedi, Pasquale Buonadonna, Rayane El Mohtadi, Gabriela Loi, Francesco Aymerich and Mauro Carta

J. Manuf. Mater. Process. 2024, 8(4), 131; https://doi.org/10.3390/jmmp8040131 - 26 Jun 2024

Cited by 1 | Viewed by 1767

Abstract

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability [...] Read more.

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability of 3D-printed PLA, PETG, and carbon-fiber-reinforced PETG, focusing on surface roughness and burr formation. A Design of Experiments (DoE) with a full-factorial design was used, considering three factors: rotation speed, feed rate, and depth of cut. Each factor had different levels: rotational speed at 3000, 5500, and 8000 rpm; feed rate at 400, 600, and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D-printed components made of three different materials—PLA, PETG, and carbon-fiber-reinforced PETG—an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate, and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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48 pages, 8831 KiB

Open AccessReview

Tool Wear Monitoring with Artificial Intelligence Methods: A Review

by Roberto Munaro, Aldo Attanasio and Antonio Del Prete

J. Manuf. Mater. Process. 2023, 7(4), 129; https://doi.org/10.3390/jmmp7040129 - 11 Jul 2023

Cited by 11 | Viewed by 5717

Abstract

Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge [...] Read more.

Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols). Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)

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