Editorial

5 pages, 199 KiB

Open AccessEditorial

by Michele Calì

Appl. Sci. 2021, 11(17), 8202; https://doi.org/10.3390/app11178202 - 03 Sep 2021

Cited by 3 | Viewed by 2046

This Special Issue of Applied Sciences provides a collection of original papers on smart manufacturing technology with the aim of: examining emerging aspects of digitalization in the industrial and biomedical fields, as well as in business management and sustainability; proposing and developing a [...] Read more.

This Special Issue of Applied Sciences provides a collection of original papers on smart manufacturing technology with the aim of: examining emerging aspects of digitalization in the industrial and biomedical fields, as well as in business management and sustainability; proposing and developing a new approach useful for companies, factories, and organizations to achieve greater innovation and productivity—as well as sustainability—by applying smart manufacturing technologies; and exploring new ideas and encouraging research directions so as to obtain autonomous and semiautonomous processes, high-quality products, and services with a greater integration and interconnection of resources while reducing costs. The advantages of new methods and experimental results obtained in the collected contributions are discussed promoting further design, implementation, and application in the various fields. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

Research

Jump to: Editorial, Other

19 pages, 8217 KiB

Open AccessArticle

HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge

by Hyun-Chul Kang, Hyo-Nyoung Han, Hee-Chul Bae, Min-Gi Kim, Ji-Yeon Son and Young-Kuk Kim

Appl. Sci. 2021, 11(16), 7593; https://doi.org/10.3390/app11167593 - 18 Aug 2021

Cited by 10 | Viewed by 3599

Abstract

We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting [...] Read more.

We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object’s location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

17 pages, 5204 KiB

Open AccessArticle

Research on Temperature Compensation Method in Crankshaft Online Measurement System

by Tingting Gu, Xiaoming Qian and Peihuang Lou

Appl. Sci. 2021, 11(16), 7558; https://doi.org/10.3390/app11167558 - 18 Aug 2021

Cited by 3 | Viewed by 2014

Abstract

The crankshaft online measurement system has realized the full inspection function with fast beats, at the same time it requires for high-precision measurement. Considering the effect of ambient temperature and temperature changes on measuring machine, the calibration part, the measured crankshaft and displacement [...] Read more.

The crankshaft online measurement system has realized the full inspection function with fast beats, at the same time it requires for high-precision measurement. Considering the effect of ambient temperature and temperature changes on measuring machine, the calibration part, the measured crankshaft and displacement sensor, a temperature compensation method is proposed. Firstly, relationship between calibration part and ambient temperature can be get through the zero calibration. Then use the material properties to obtain compensation values of the calibration part and the measured crankshaft part at different temperatures. Finally, the compensation parameters for displacement sensor can be obtained through the BP algorithm. The improved dragonfly algorithm (DA) is used to optimize the parameters of BP neural network algorithm. Experiments verify the effectiveness of IDA-BP for LVDT in temperature compensation. After temperature compensation, the error range of main journal radius is reduced from 0.0156 mm to 0.0028 mm, the residual error decreased from −0.0282 mm~+0.0018 mm to −0.0058 mm~−0.0008 mm. The influence of temperature changes on the measurement is reduced and measurement accuracy is improved through the temperature compensation method. The effectiveness of the method is proved. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

12 pages, 2026 KiB

Open AccessArticle

Visioning the Future of Smart Fashion Factories Based on Media Big Data Analysis

by Sae-Eun Lee, Naan Ju and Kyu-Hye Lee

Appl. Sci. 2021, 11(16), 7549; https://doi.org/10.3390/app11167549 - 17 Aug 2021

Cited by 2 | Viewed by 2769

Abstract

Recently, many companies have adopted smart factories to increase productivity and efficiency. However, the fashion industry is one of the industries that have been relatively slow at embracing automation and switching to a smart factory. The purpose of the study is to suggest [...] Read more.

Recently, many companies have adopted smart factories to increase productivity and efficiency. However, the fashion industry is one of the industries that have been relatively slow at embracing automation and switching to a smart factory. The purpose of the study is to suggest the future direction of the low-maturity smart factory in the fashion industry through newspaper analysis. In this study, semantic network analysis and convergence of iterated correlation (CONCOR) analysis were performed on 15,523 news articles. The analyses revealed that the smart fashion factory was developing to incorporate automated, unmanned, and intelligent operation. The problem of job loss owing to the smart factory was also heavily addressed in the news articles. In the newspaper articles, the view that the smart factory is efficient, fast, and innovative, and concerns regarding the possible damages that will result from hacking and machine malfunction were simultaneously expressed. Therefore, if news about security improvement emerges in the future, negative public opinion will be reduced, positively influencing the government’s support for smart factories and policy making. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

19 pages, 1937 KiB

Open AccessArticle

Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance

by Paul Georg Wagner, Christian Lengenfelder, Gerrit Holzbach, Maximilian Becker, Pascal Birnstill, Michael Voit, Ali Bejhad, Tim Samorei and Jürgen Beyerer

Appl. Sci. 2021, 11(16), 7339; https://doi.org/10.3390/app11167339 - 10 Aug 2021

Viewed by 1662

Abstract

The automated documentation of work steps is a requirement of many modern manufacturing processes. Especially when it comes to important procedures such as safety critical screw connections or weld seams, the correct and complete execution of certain manufacturing steps needs to be properly [...] Read more.

The automated documentation of work steps is a requirement of many modern manufacturing processes. Especially when it comes to important procedures such as safety critical screw connections or weld seams, the correct and complete execution of certain manufacturing steps needs to be properly supervised, e.g., by capturing video snippets of the worker to be checked in hindsight. Without proper technical and organizational safeguards, such documentation data carries the potential for covert performance monitoring to the disadvantage of employees. Naïve documentation architectures interfere with data protection requirements, and thus cannot expect acceptance of employees. In this paper we outline use cases for automated documentation and describe an exemplary system architecture of a workflow recognition and documentation system. We derive privacy protection goals that we address with a suitable security architecture based on hybrid encryption, secret-sharing among multiple parties and remote attestation of the system to prevent manipulation. We finally contribute an outlook towards problems and possible solutions with regards to information that can leak through accessible metadata and with regard to more modular system architectures, where more sophisticated remote attestation approaches are needed to ensure the integrity of distributed components. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

16 pages, 4175 KiB

Open AccessArticle

Research on Fish Slicing Method Based on Simulated Annealing Algorithm

by Shuo Liu, Hao Wang and Yong Cai

Appl. Sci. 2021, 11(14), 6503; https://doi.org/10.3390/app11146503 - 15 Jul 2021

Cited by 6 | Viewed by 1488

Abstract

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of [...] Read more.

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of a cut object through sensors and use machines instead of manual decision making. However, the traditional sequential algorithm cannot satisfy multiobjective optimization problems. This paper studies the multiobjective optimization problem of irregular objects in the field of aquatic product processing and uses the information guidance strategy to develop a simulated annealing algorithm to solve the problem according to the characteristics of the object itself. By optimizing the mutation strategy, the ability of the simulated annealing algorithm to jump out of the local optimal solution is improved. The project team developed an experimental prototype to verify the algorithm. The experimental results show that compared with the traditional sequential algorithm method, the simulated degradation algorithm designed in this paper effectively improves the quality of the target solution and greatly enhances the economic value of the product by addressing the multiobjective optimization problem of squid cutting. At the end of the article, the cutting error is analyzed. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

13 pages, 943 KiB

Open AccessArticle

Product Quality Prediction for Wire Electrical Discharge Machining with Markov Transition Fields and Convolutional Long Short-Term Memory Neural Networks

by Jehn-Ruey Jiang and Cheng-Tai Yen

Appl. Sci. 2021, 11(13), 5922; https://doi.org/10.3390/app11135922 - 25 Jun 2021

Cited by 9 | Viewed by 2220

Abstract

This paper proposes a wire electrical discharge machining (WEDM) product quality prediction method, called MTF-CLSTM, to integrate the Markov transition field (MTF) and the convolutional long short-term memory (CLSTM) neural network. The proposed MTF-CLSTM method can accurately predict WEDM workpiece surface roughness right [...] Read more.

This paper proposes a wire electrical discharge machining (WEDM) product quality prediction method, called MTF-CLSTM, to integrate the Markov transition field (MTF) and the convolutional long short-term memory (CLSTM) neural network. The proposed MTF-CLSTM method can accurately predict WEDM workpiece surface roughness right after manufacturing by collecting and analyzing static machining parameters and dynamic manufacturing conditions. The highly accurate prediction is due to the following two reasons. First, MTF can transform data into images to extract data temporal information and state transition probability information. Second, the CLSTM neural network can extract image spacial features and temporal relationship of data that are separated far apart. In short, MTF-CLSTM predicts WEDM workpiece surface roughness with the MTF model and the CLSTM neural network using static machining parameters and dynamic manufacturing conditions. MTF-CLSTM is compared with 10 related research studies in many aspects. There is only one existing method that is like MTF-CLSTM to predict WEDM workpiece surface roughness by using static machining parameters and dynamic manufacturing conditions. Experiments are conducted to evaluate MTF-CLSTM performance to show that MTF-CLSTM significantly outperforms the existing method in terms of the prediction mean absolute percentage error. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

15 pages, 5048 KiB

Open AccessFeature PaperArticle

A Study on Distance Measurement Module for Driving Vehicle Velocity Estimation in Multi-Lanes Using Drones

by Kwan-Hyeong Lee

Appl. Sci. 2021, 11(9), 3884; https://doi.org/10.3390/app11093884 - 25 Apr 2021

Cited by 4 | Viewed by 2011

Abstract

A method of estimating driving vehicle information usually uses a speed gun and a fixed speed camera. Estimating vehicle information using the speed gun has a high risk of traffic accidents by the operator and the fixed speed camera is not efficient in [...] Read more.

A method of estimating driving vehicle information usually uses a speed gun and a fixed speed camera. Estimating vehicle information using the speed gun has a high risk of traffic accidents by the operator and the fixed speed camera is not efficient in terms of installation cost and maintenance. The existing driving vehicle information estimation method can only measure each lane’s driving vehicle information, so it is impossible to measure multi-lanes simultaneously with a single measuring device. This study develops a distance measurement module that can acquire driving vehicle information in multi-lanes simultaneously with a single system using a drone. The distance measurement module is composed of two LiDAR sensors to detect the driving vehicle in one lane. The drone is located above the edge of the road and each LiDAR sensor emits the front/rear point of the road measuring point to detect the driving vehicle. The driving vehicle velocity is estimated by detecting the driving vehicle’s detection distance and transit time through radiation, with the drone LiDAR sensor placed at two measurement points on the road. The drone LiDAR sensor radiates two measuring points on the road and estimates the velocity based on driving vehicle’s detection distance and driving time. As an experiment, the velocity accuracy of the drone driving vehicle is compared with the speed gun measurement. The vehicle velocity RMSE for the first and second lanes using drones is 0.75 km/h and 1.3 km/h, respectively. The drone and the speed gun’s average error probabilities are 1.2% and 2.05% in the first and second lanes, respectively. The developed drone is more efficient than existing driving vehicle measurement equipment because it can acquire information on the driving vehicle in a dark environment and a person’s safety. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

25 pages, 6435 KiB

Open AccessEditor’s ChoiceArticle

A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems

by Matevz Resman, Jernej Protner, Marko Simic and Niko Herakovic

Appl. Sci. 2021, 11(8), 3639; https://doi.org/10.3390/app11083639 - 18 Apr 2021

Cited by 31 | Viewed by 5311

Abstract

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven [...] Read more.

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

14 pages, 5570 KiB

Open AccessArticle

Solid-State Foaming Process Optimization for the Production of Shape Memory Polymer Composite Foam

by Tamem Salah and Aiman Ziout

Appl. Sci. 2021, 11(8), 3433; https://doi.org/10.3390/app11083433 - 12 Apr 2021

Cited by 2 | Viewed by 2271

Abstract

This research examined the optimization of the sustainable manufacturing process for polyester-based polymers/Fe₃O₄ nanocomposite foaming. The foamed structure was achieved by using a solid-state foaming process, where the prepared foams were tested in order to ascertain the optimum foaming parameters [...] Read more.

This research examined the optimization of the sustainable manufacturing process for polyester-based polymers/Fe₃O₄ nanocomposite foaming. The foamed structure was achieved by using a solid-state foaming process, where the prepared foams were tested in order to ascertain the optimum foaming parameters with the highest foaming ratios and the lowest foaming densities. The foaming parameters used in this research were the polymer type, nanoparticle percentage, packing pressure, holding time, foaming temperature, and foaming time. Two levels were selected for each factor, and a Taguchi plan was designed to determine the number of experiments required to reach a conclusion. Further characterization techniques, namely, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used with the original samples to gain a better understanding of their structure and chemical composition. The data analysis showed that regardless of the parameters used, a high foaming ratio resulted in a low density. The introduction of nanoparticles (NPs) to the polymer structure resulted in higher foaming ratios. This increment in foaming ratio was noticeable on Corro-Coat PE Series 7^® (CC) polymer more than Jotun Super Durable 2903^® (JSD). The optimum parameters to prepare the highest foaming ratios were as follows: CC polymer with 2% NPs, compressed under a pressure of 10 K lbs. for a 3 min holding time and foamed at 290 °C for 15 min in the oven. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

22 pages, 957 KiB

Open AccessArticle

Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing

by Dennis Bauer, Thomas Bauernhansl and Alexander Sauer

Appl. Sci. 2021, 11(5), 2205; https://doi.org/10.3390/app11052205 - 03 Mar 2021

Cited by 8 | Viewed by 2419

Abstract

Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from [...] Read more.

Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturing control’s decisions. In addition to the requirements, the concept describes the structure of the system as a control loop, a reinforcement learning-based controlling element as the central decision-making component, and the integration into the existing production IT landscape of a company as well as with latest internet of things (IoT) devices and cyber-physical systems. The benefits of the concept were elaborated in expert workshops. In summary, this approach enables an effective and efficient response to events from the supply network through smarter manufacturing control, and thus more resilient manufacturing. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

13 pages, 3139 KiB

Open AccessArticle

Utilization of Optimization of Internal Topology in Manufacturing of Injection Moulds by the DMLS Technology

by Tomas Coranic, Stefan Gaspar and Jan Pasko

Appl. Sci. 2021, 11(1), 262; https://doi.org/10.3390/app11010262 - 29 Dec 2020

Cited by 9 | Viewed by 1987

Abstract

The present paper is focused on the issue of creating residual stresses in the manufacturing of the moulded parts for injection moulding machines using DMLS technology. Thus, fractures and cracks can cause deformations and geometric inaccuracies in the final part. Moreover, they pose [...] Read more.

The present paper is focused on the issue of creating residual stresses in the manufacturing of the moulded parts for injection moulding machines using DMLS technology. Thus, fractures and cracks can cause deformations and geometric inaccuracies in the final part. Moreover, they pose a potential damage risk to the machine itself. The simulation tools for the analysis of the Direct Metal Laser Sintering (DMLS) process were used to expose the critical points of the original monolithic shaped insert in which the highest stresses during the manufacture occur. Subsequently, an alternative solution was created via the optimization of the internal topology. This solution was analysed, and in terms of strength characteristics, compared to the original model in order to ensure the proper function and durability of the manufactured part. The present study was created in cooperation with a company engaged in the production of injection moulds. The internal topology optimization of the part itself is used in combination with the appropriate orientation of the model in the workspace, unlike other research in the given field, where either the model orientation optimization or the support structure is used in this design. However, except for the mentioned reduction of residual stresses, it has a positive effect on mechanical properties, reducing material consumption and savings in time; thus, the obtained results can be applied to other methods of additive manufacturing. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

13 pages, 4180 KiB

Open AccessFeature PaperArticle

Engineering and Manufacturing of a Dynamizable Fracture Fixation Device System

by Giancarlo Dichio, Michele Calì, Mara Terzini, Giovanni Putame, Elisabetta Maria Zanetti, Piero Costa and Alberto Luigi Audenino

Appl. Sci. 2020, 10(19), 6844; https://doi.org/10.3390/app10196844 - 29 Sep 2020

Cited by 3 | Viewed by 2629

Abstract

The present work illustrates the dynamization of an orthopaedic plate for internal fracture fixation which is thought to shorten healing times and enhance the quality of the new formed bone. The dynamization is performed wirelessly thanks to a magnetic coupling. The paper shows [...] Read more.

The present work illustrates the dynamization of an orthopaedic plate for internal fracture fixation which is thought to shorten healing times and enhance the quality of the new formed bone. The dynamization is performed wirelessly thanks to a magnetic coupling. The paper shows the peculiarities of the design and manufacturing of this system: it involves two components, sliding with respect to each other with an uncertain coefficient of friction, and with a specific compounded geometry; there are stringent limits on component size, and on the required activation energy. Finally, the device belongs to medical devices and, as such, it must comply with the respective regulation (EU 2017/745, ASTM F382). The design of the dynamizable fracture fixation plate has required verifying the dynamic of the unlocking mechanism through the development of a parametric multibody model which has allowed us to fix the main design variables. As a second step, the fatigue strength of the device and the static strength of the whole bone-plate system was evaluated by finite element analysis. Both analyses have contributed to defining the final optimized geometry and the constitutive materials of the plate; finally, the respective working process was set up and its performance was tested experimentally on a reference fractured femur. As a result of these tests, the flexural stiffness of the bone-plate system resulted equal to 370 N/mm, while a maximum bending moment equal to 75.3 kN·mm can be withstood without plate failure. On the whole, the performance of this dynamic plate was proved to be equal or superior to those measured for static plates already on the market, with excellent clinical results. At the same time, pre-clinical tests will be an interesting step of the future research, for which more prototypes are now being produced. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

13 pages, 3038 KiB

Open AccessArticle

A New Generation of Bio-Composite Thermoplastic Filaments for a More Sustainable Design of Parts Manufactured by FDM

by Michele Calì, Giulia Pascoletti, Massimiliano Gaeta, Giovanni Milazzo and Rita Ambu

Appl. Sci. 2020, 10(17), 5852; https://doi.org/10.3390/app10175852 - 24 Aug 2020

Cited by 40 | Viewed by 3940

Abstract

The most recent developments of Fused Deposition Modelling (FDM) techniques are moving the application of Additive Manufacturing (AM) technologies toward new areas of investigation such as the biomedical, aerospace, and marine engineering in addition to the more consolidated industrial and civil fields. Some [...] Read more.

The most recent developments of Fused Deposition Modelling (FDM) techniques are moving the application of Additive Manufacturing (AM) technologies toward new areas of investigation such as the biomedical, aerospace, and marine engineering in addition to the more consolidated industrial and civil fields. Some specific characteristics are required for the components designed for peculiar applications, such as complex geometries, lightweight, and high strength as well as breathability and aesthetic appearance specifically in the biomedical field. All these design specifications could be potentially satisfied by manufacturing with 3D printing techniques. Moreover, the development of purpose-dedicated filaments can be considered a key factor to successfully meet all the requirements. In this paper, fabrication and applications of five new thermoplastic materials with fillers are described and analyzed. They are organic bio-plastic compounds made of polylactic acid (PLA) and organic by-products. The growing interest in these new composite materials reinforced with organic by-products is due to the reduction of production management costs and their low environmental impact. In this study, the production workflow has been set up and described in detail. The main properties of these new thermoplastic materials have been analyzed with a major emphasis on strength, lightweight, and surface finish. The analysis showed that these materials can be particularly suitable for biomedical applications. Therefore, two different biomedical devices were selected and relative prototypes were manufactured with one of the analyzed thermoplastic materials. The feasibility, benefits, and performance of the thermoplastic material considered for these applications were successfully assessed. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

Other

Jump to: Editorial, Research

16 pages, 6847 KiB

Open AccessTechnical Note

Prediction of Model Distortion by FEM in 3D Printing via the Selective Laser Melting of Stainless Steel AISI 316L

by Marek Pagac, Jiri Hajnys, Radim Halama, Tariq Aldabash, Jakub Mesicek, Lukas Jancar and Jan Jansa

Appl. Sci. 2021, 11(4), 1656; https://doi.org/10.3390/app11041656 - 12 Feb 2021

Cited by 25 | Viewed by 4528

Abstract

This paper deals with an experimental analysis of stress prediction and simulation prior to 3D printing via the selective laser melting (SLM) method and the subsequent separation of a printed sample from a base plate in two software programs, ANSYS Addictive Suite and [...] Read more.

This paper deals with an experimental analysis of stress prediction and simulation prior to 3D printing via the selective laser melting (SLM) method and the subsequent separation of a printed sample from a base plate in two software programs, ANSYS Addictive Suite and MSC Simufact Additive. Practical verification of the simulation was performed on a 3Dprinted topologically optimized part made of AISI 316L stainless steel. This paper presents a typical workflow for working with metallic 3D printing technology and the state-of-the-art knowledge in the field of stress analysis and simulation of printed components. The paper emphasizes the role of simulation software for additive production and reflects on their weaknesses and strengths as well, with regard to their use not only in science and research but also in practice. Full article

(This article belongs to the Special Issue Smart Manufacturing Technology)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Smart Manufacturing Technology

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (15 papers)

Editorial

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI