Research

18 pages, 7941 KiB

Open AccessArticle

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

by Le Ling, Xuejian Zhang, Xiaobing Hu, Yucong Fu, Dongming Yang, Enpei Liang and Yi Chen

Machines 2024, 12(2), 100; https://doi.org/10.3390/machines12020100 - 01 Feb 2024

Viewed by 813

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying [...] Read more.

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

39 pages, 14477 KiB

Open AccessArticle

Methodology for Rationalization of Pre-Production Processes Using Virtual Reality Based Manufacturing Instructions

by Konstantin Novikov, Petr Hořejší, Jan Kubr, Matěj Dvořák, Miroslav Bednář, David Krákora, Matěj Krňoul and Michal Šimon

Machines 2024, 12(1), 2; https://doi.org/10.3390/machines12010002 - 19 Dec 2023

Viewed by 1019

Abstract

This article deals with the rationalization of manufacturing processes within the product life cycle with emphasis on the pre-production phase of production. A new methodology for evaluating the applicability of modern visualization tools in manufacturing processes is presented. This methodology includes a modified [...] Read more.

This article deals with the rationalization of manufacturing processes within the product life cycle with emphasis on the pre-production phase of production. A new methodology for evaluating the applicability of modern visualization tools in manufacturing processes is presented. This methodology includes a modified Z-score for categorizing manufacturing processes and has been validated by the successful implementation of 10 real projects. Ultimately, the methodology provides a practical decision-making aid for manufacturing companies in deploying such Computer Aided Instruction tools. For the pre-production phase of products and their development, the possibilities of using modern visualization tools to support CAD instructions and assembly instructions are being explored. These modern visualization tools are video tutorials, augmented reality tutorials and virtual reality tutorials. This paper explores the use of these tools for rationalization of pre-production processes. A methodology was designed to select the most appropriate tool for rationalizing process execution in preparation for production. The functionality of the methodology was verified by applying the methodology in industrial practice and subsequent implementation of the recommended solutions. The methodology was validated by testing key combinations that can arise based on the methodology directly in the operations of manufacturing companies. A total of 10 implementations in real production processes were tailored to this study and carried out over 2 years and the functionality of the methodology was confirmed (that consisted also of a new software development). It was found that there is a dependency between the visualization tools chosen to create the instructions in the context of organizational production preparation and the nature of the production processes. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

19 pages, 6298 KiB

Open AccessArticle

Correction of Shape Error at Cut-In and Cut-Out Points in Abrasive Waterjet Cutting of Carbon Fiber Reinforced Polymer (CFRP)

by Ioan Alexandru Popan, Cosmin Cosma, Alina Ioana Popan, Nicolae Panc, Daniel Filip and Nicolae Balc

Machines 2023, 11(8), 800; https://doi.org/10.3390/machines11080800 - 03 Aug 2023

Cited by 1 | Viewed by 759

Abstract

This paper presents a solution aimed at enhancing the accuracy of abrasive waterjet cutting (AWJC) for the processing of carbon-fiber-reinforced polymers (CFRP). Processing CFRP with high accuracy and good surface quality in a short processing time is a difficult task. One crucial problem [...] Read more.

This paper presents a solution aimed at enhancing the accuracy of abrasive waterjet cutting (AWJC) for the processing of carbon-fiber-reinforced polymers (CFRP). Processing CFRP with high accuracy and good surface quality in a short processing time is a difficult task. One crucial problem is the occurrence of shape errors, overcuts, at the cut-in and cut-out point during the cutting process. Shape errors have the potential to create mechanical stress concentrators, which can result in structural failures and compromise the integrity and reliability of components. The primary objective of this study was to gain a comprehensive understanding of the formation mechanism underlying the shape error. The observed shape error is closely associated with both the lead-in/lead-out strategies employed and the process parameters selected. The experimental investigation focused on two commonly used strategies for CFRP cutting: lead-in/lead-out in arc and lead-in/lead-out in line. In order to minimize shape errors, this study proposed a correction method that offers a set of recommendations for selecting the appropriate lead-in/out strategy and a suitable combination of process parameters. Additionally, a mathematical model has been developed to determine the depth of the shape error. The conclusions drawn from this study have been successfully validated through industrial applications. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

15 pages, 8044 KiB

Open AccessArticle

Simulation Design and Measurement of Welding Robot Repeatability Utilizing the Contact Measurement Method

by Martin Pollák and Karol Goryl

Machines 2023, 11(7), 734; https://doi.org/10.3390/machines11070734 - 13 Jul 2023

Cited by 2 | Viewed by 1304

Abstract

ISO 9283 is a significant guiding standard for assessing the performance characteristics of robots. The main objective of the present paper was to verify the repeatability of the Panasonic TM-2000 welding robot at a manufacturing company. The paper describes the workflow of the [...] Read more.

ISO 9283 is a significant guiding standard for assessing the performance characteristics of robots. The main objective of the present paper was to verify the repeatability of the Panasonic TM-2000 welding robot at a manufacturing company. The paper describes the workflow of the robot control program in the simulation software RoboDK, which created a complete welding station. The measurement, as well as the simulation, were possible thanks to designing the measuring device, the imaginary ISO cube, the measuring plane, the measuring points, and last but not least, the cycle that determined in which order the points were measured. The analytical part of the paper resulted in a direct measurement of the position repeatability of the welding robot. A total of five points were measured for the X-axis and five points for the Y-axis. Each point was recorded 30 times, with measurements taken in the positive direction of motion. The results were compared with the value given by the manufacturer, and the measured deviations were presented graphically. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

24 pages, 7935 KiB

Open AccessArticle

Analysis of the Design of the Single-Cylinder Steam Engine of the Grasshopper Beam by Henry Muncaster

by José Ignacio Rojas-Sola and José Francisco Gutiérrez-Antúnez

Machines 2023, 11(7), 703; https://doi.org/10.3390/machines11070703 - 02 Jul 2023

Cited by 2 | Viewed by 2114

Abstract

In this paper, the analysis of the design of the single-cylinder steam engine of the Grasshopper beam designed by Henry Muncaster in 1912 is shown. This engine was incorporated into ships and railways and it was published in the Model Engineer journal in [...] Read more.

In this paper, the analysis of the design of the single-cylinder steam engine of the Grasshopper beam designed by Henry Muncaster in 1912 is shown. This engine was incorporated into ships and railways and it was published in the Model Engineer journal in 1957. It shows great complexity due to its ability to transform reciprocating movement into rotary movement and its high number of elements (more than 150). To this end, a study of computer-aided engineering (CAE) was carried out using the software Autodesk Inventor Nastran, consisting of a linear static analysis using the finite element method (FEM) of the 3D CAD model under real operating conditions in order to learn if it was well designed, according to material resistance criteria. The assembly is analyzed in the two most unfavorable situations in order to determine the von Mises stresses, the displacements and the safety coefficient distributions. Finally, it was found that the work pressure (maximum admissible steam pressure in the admission) was 0.15 MPa. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

13 pages, 13205 KiB

Open AccessArticle

Green Machining of NFRP Material

by Zuzana Mitaľová, Dagmar Klichová, František Botko, Juliána Litecká, Radoslav Vandžura and Dušan Mitaľ

Machines 2023, 11(7), 692; https://doi.org/10.3390/machines11070692 - 01 Jul 2023

Viewed by 974

Abstract

Nowadays, great emphasis is placed on environmental aspects of production processes with focus to lower carbon footprint. Natural fibre-reinforced plastics (NFRP) show potential for application in many fields of industry due their specific properties. Machining of NFRP-based materials is meeting several problems arising [...] Read more.

Nowadays, great emphasis is placed on environmental aspects of production processes with focus to lower carbon footprint. Natural fibre-reinforced plastics (NFRP) show potential for application in many fields of industry due their specific properties. Machining of NFRP-based materials is meeting several problems arising from non-homogenous structure as well as plastic-based matrix. Machining of NFRP using conventional technologies meets limitations due to the properties and geometry of the tools. Abrasive water jet (AWJ) machining can solve some of the problems machining NFRP materials. The presented article focused on surface topography evaluation of one kind of NFRP composite material after cutting by AWJ. Optical profilometry and 3D microscopy were applied for measurement of surface roughness parameters of surfaces created by AWJ with variable cutting parameters. Maximal height of profile Rz was measured in 20 lines perpendicular to the jet direction form upper to lower cut line. Structure of cut surface was observed and evaluated for different technologic parameters. The obtained results show promising presuppositions for application of AWJ technology for cutting of NFRP based materials. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

13 pages, 4833 KiB

Open AccessArticle

Artificial Neural Network-Based Predictive Model for Finite Element Analysis of Additive-Manufactured Components

by Sorin D. Grozav, Alexandru D. Sterca, Marek Kočiško, Martin Pollák and Vasile Ceclan

Machines 2023, 11(5), 547; https://doi.org/10.3390/machines11050547 - 12 May 2023

Cited by 1 | Viewed by 1187

Abstract

Additive manufacturing is becoming one of the most utilized tools in an increasing number of fields from Industry 4.0 concepts, engineering, and manufacturing to aerospace and medical applications. One important issue with additive-manufactured components is their orthotropic behaviour where mechanical properties are concerned. [...] Read more.

Additive manufacturing is becoming one of the most utilized tools in an increasing number of fields from Industry 4.0 concepts, engineering, and manufacturing to aerospace and medical applications. One important issue with additive-manufactured components is their orthotropic behaviour where mechanical properties are concerned. This behaviour is due to the layer-by-layer manufacturing process and is particularly hard to predict since it depends on a number of factors, including the manufacturing parameters used during the manufacturing process (speed, temperature, etc.). This study aimed to create and train an artificial neural network-based predictive model using empirical tensile strength data obtained from additive manufactured test parts using the FDM method and PLA material. The predictive model was designed to predict mechanical characteristics for different orientation axis, which were used to set the material properties for finite element analysis. Results indicate a strong correlation between predicted finite element analysis behaviour and real-world tests on additive-manufactured components. The neural network model was trained to an accuracy of ~93% for predicting the mechanical characteristics of 3D-printed PLA material. Using the predicted mechanical characteristics for defining a custom orthotropic material profile in finite element analysis, the simulated failure mode and the behaviour of a complex geometry component agreed with the real-world test. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

15 pages, 17427 KiB

Open AccessArticle

Effect of Hardening Temperature on Maraging Steel Samples Prepared by Direct Metal Laser Sintering Process

by Radoslav Vandzura, Vladimir Simkulet, Matus Gelatko, Michal Hatala and Zuzana Mitalova

Machines 2023, 11(3), 351; https://doi.org/10.3390/machines11030351 - 03 Mar 2023

Cited by 3 | Viewed by 1361

Abstract

This paper deals with the application of the direct metal laser sintering (DMLS) process, which already has a dominant position in the area of additive manufacturing (AM). This DMLS technology is used in many branches of industry and medicine, especially in piece production, [...] Read more.

This paper deals with the application of the direct metal laser sintering (DMLS) process, which already has a dominant position in the area of additive manufacturing (AM). This DMLS technology is used in many branches of industry and medicine, especially in piece production, small series, and prototypes. The portfolio of used metal powder materials includes aluminum alloys, austenitic steels, maraging steels, special alloys of nickel and titanium. The properties of these products are very often improved by further heat treatment after printing, such as a hardening process, by which microstructure and hardness can be increased. Heat treatment processes of metal AM components are already described, but experiments focused on optimization of these processes are still missing. In the article, the maraging steel samples printed by the DMLS method are subjected to testing after hardening processes, which differ by reducing the maintaining time at a defined temperature, recommended by the manufacturer. The result of the evaluation will be the reaching of similar results, which are set by the powder manufacturer, however, with shorter time of samples treatment. Therefore, the elevated temperature is selected, with the purpose of monitoring the shortest possible time of a temperature impact. The experimental temperature was set 590 °C with different durations at this temperature, for 1, 2, 3, 4, 5 and 6 h. The cooling process runs controlled in the furnace or in the still air. The maintaining time proved to be the most ideal already at 1 h exposure and cooled in the still air, where a higher hardness value of around 50 HRC was reached. During the resulting microstructure evaluations, fine carbids and martensitic lamellae were observed. More uniform and finer lamellar microstructure occurred at 5 and 6 h temperature intervals. Full article

(This article belongs to the Special Issue Advances in Computer-Aided Technology II)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advances in Computer-Aided Technology II

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI