Next Issue
Volume 4, September
Previous Issue
Volume 4, March
 
 

Designs, Volume 4, Issue 2 (June 2020) – 8 articles

Cover Story (view full-size image): Additive manufacturing (AM) is a crucial element in the context of Industry 4.0. It is an umbrella term encompassing several manufacturing techniques that manufacture products by adding layers on top of each other. These technologies have been widely researched and implemented for product development with complex geometries. This paper focuses on the interrelationship between AM and other elements of Industry 4.0 through a comprehensive AM-centric literature review. It also proposes a conceptual digital thread that integrates AM and Industry 4.0 technologies. The development of such a digital thread will promote organizational agility and accelerate the shift toward smart manufacturing. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
11 pages, 2864 KiB  
Article
Shaft Integrated Electromagnetic Energy Harvester with Gravitational Torque
by Michel Ullrich, Maik Wolf, Mathias Rudolph and Wolfgang Diller
Designs 2020, 4(2), 16; https://doi.org/10.3390/designs4020016 - 23 Jun 2020
Cited by 2 | Viewed by 3086
Abstract
This paper presents the development of an electromagnetic energy harvester for electrical supply of a sensor unit integrated on the rotating inner ring of a rolling bearing. This energy harvester is of special interest for condition monitoring tasks on rotating shafts. A sensory [...] Read more.
This paper presents the development of an electromagnetic energy harvester for electrical supply of a sensor unit integrated on the rotating inner ring of a rolling bearing. This energy harvester is of special interest for condition monitoring tasks on rotating shafts. A sensory monitor on the inner ring can detect wear conditions at an early stage. The harvester works without mechanical and energetic contact to surrounding components by utilizing the rotational energy of the shaft. The functionality of the Energy Harvester is enabled by the inertia principle, which is caused by an asymmetrical mass distribution. We provide simulations to validate the designs. This work includes simulation studies on the electrical power output of the harvester. Therefore, the necessary simulation of the magnetic problems is realized in a substitute simulation environment. The harvester design enables existing machines to be equipped with the harvester to provide an energy supply on rotating shafts. This clamp connection enables shaft mounting independent of location without mechanical work on the shaft. With an electrical power of up to 163.6 m W, at 3600 rpm, the harvester is used as an energy supply, which enables sensor-based monitoring of slow wear processes. Full article
Show Figures

Graphical abstract

19 pages, 5948 KiB  
Article
A Novel Deep Learning Backstepping Controller-Based Digital Twins Technology for Pitch Angle Control of Variable Speed Wind Turbine
by Ahmad Parvaresh, Saber Abrazeh, Saeid-Reza Mohseni, Meisam Jahanshahi Zeitouni, Meysam Gheisarnejad and Mohammad-Hassan Khooban
Designs 2020, 4(2), 15; https://doi.org/10.3390/designs4020015 - 22 Jun 2020
Cited by 23 | Viewed by 4100
Abstract
This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, [...] Read more.
This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, which is an increasingly growing method in a variety of applications. In DDPG-NIB-MFC, the pitch angle is considered as the control input that depends on the optimal rotor speed, which is usually derived from effective wind speed. The system stability according to the Lyapunov theory can be achieved by the recursive nature of the backstepping theory and the integral action has been used to compensate for the steady-state error. Moreover, due to the nonlinear characteristics of wind turbines, the MFC aims to handle the un-modeled system dynamics and disturbances. The DDPG algorithm with actor-critic structure has been added in the proposed control structure to efficiently and adaptively tune the controller parameters embedded in the NIB controller. Under this effort, a digital twin of a presented controller is defined as a real-time and probabilistic model which is implemented on the digital signal processor (DSP) computing device. To ensure the performance of the proposed approach and output behavior of the system, software-in-loop (SIL) and hardware-in-loop (HIL) testing procedures have been considered. From the simulation and implementation outcomes, it can be concluded that the proposed backstepping controller based DDPG is more effective, robust, and adaptive than the backstepping and proportional-integral (PI) controllers optimized by particle swarm optimization (PSO) in the presence of uncertainties and disturbances. Full article
Show Figures

Figure 1

15 pages, 2614 KiB  
Article
Interconnections for Additively Manufactured Hybridized Printed Electronics in Harsh Environments
by Clayton Neff, Edwin Elston and Amanda Schrand
Designs 2020, 4(2), 14; https://doi.org/10.3390/designs4020014 - 18 Jun 2020
Cited by 7 | Viewed by 4181
Abstract
The ability to fabricate functional 3D conductive elements via additive manufacturing has opened up a unique sector of ‘hybridized printed electronics’. In doing so, many of the rigid standards (i.e., planar circuit boards, potting, etc.,) of traditional electronics are abandoned. However, one critical [...] Read more.
The ability to fabricate functional 3D conductive elements via additive manufacturing has opened up a unique sector of ‘hybridized printed electronics’. In doing so, many of the rigid standards (i.e., planar circuit boards, potting, etc.,) of traditional electronics are abandoned. However, one critical challenge lies in producing robust and reliable interconnections between conductive inks and traditional hardware, especially when subjected to harsh environments. This research examines select material pairings for the most resilient interconnection. The method of test is wire bond pull testing that would represent a continuous strain on a connection and high acceleration testing of up to 50,000 g that would represent a sudden shock that electronics may experience in a drop or crash. Although these two environments may be similar to an overall energy exerted on the connection, the rate of force exerted may lead to different solutions. The results of this research provide insight into material selection for printed electronic interconnections and a framework for interconnection resiliency assessment, which is a critical aspect in realizing the production of next generation electronics technologies for the most demanding environments. Full article
(This article belongs to the Special Issue 3D Printing Functionality: Materials, Sensors, Electromagnetics)
Show Figures

Figure 1

33 pages, 2555 KiB  
Review
Exploring the Interrelationship between Additive Manufacturing and Industry 4.0
by Javaid Butt
Designs 2020, 4(2), 13; https://doi.org/10.3390/designs4020013 - 17 Jun 2020
Cited by 91 | Viewed by 14062
Abstract
Innovative technologies allow organizations to remain competitive in the market and increase their profitability. These driving factors have led to the adoption of several emerging technologies and no other trend has created more of an impact than Industry 4.0 in recent years. This [...] Read more.
Innovative technologies allow organizations to remain competitive in the market and increase their profitability. These driving factors have led to the adoption of several emerging technologies and no other trend has created more of an impact than Industry 4.0 in recent years. This is an umbrella term that encompasses several digital technologies that are geared toward automation and data exchange in manufacturing technologies and processes. These include but are not limited to several latest technological developments such as cyber-physical systems, digital twins, Internet of Things, cloud computing, cognitive computing, and artificial intelligence. Within the context of Industry 4.0, additive manufacturing (AM) is a crucial element. AM is also an umbrella term for several manufacturing techniques capable of manufacturing products by adding layers on top of each other. These technologies have been widely researched and implemented to produce homogeneous and heterogeneous products with complex geometries. This paper focuses on the interrelationship between AM and other elements of Industry 4.0. A comprehensive AM-centric literature review discussing the interaction between AM and Industry 4.0 elements whether directly (used for AM) or indirectly (used with AM) has been presented. Furthermore, a conceptual digital thread integrating AM and Industry 4.0 technologies has been proposed. The need for such interconnectedness and its benefits have been explored through the content-centric literature review. Development of such a digital thread for AM will provide significant benefits, allow companies to respond to customer requirements more efficiently, and will accelerate the shift toward smart manufacturing. Full article
Show Figures

Graphical abstract

15 pages, 7492 KiB  
Article
Design and Analysis of Porous Functionally Graded Femoral Prostheses with Improved Stress Shielding
by Morassa Jafari Chashmi, Alireza Fathi, Masoud Shirzad, Ramazan-Ali Jafari-Talookolaei, Mahdi Bodaghi and Sayed Mahmood Rabiee
Designs 2020, 4(2), 12; https://doi.org/10.3390/designs4020012 - 2 Jun 2020
Cited by 27 | Viewed by 3559
Abstract
One of the most important problems of total hip replacement is aseptic loosening of the femoral component, which is related to the changes of the stress distribution pattern after implantation of the prosthesis. Stress shielding of the femur is recognized as a primary [...] Read more.
One of the most important problems of total hip replacement is aseptic loosening of the femoral component, which is related to the changes of the stress distribution pattern after implantation of the prosthesis. Stress shielding of the femur is recognized as a primary factor in aseptic loosening of hip replacements. Utilizing different materials is one of the ordinary solutions for that problem, but using functionally graded materials (FGMs) could be better than the conventional solutions. This research work aims at investigating different porous FGM implants and a real femoral bone by a 3D finite element method. The results show that a neutral functionally graded prosthesis cannot extraordinarily make changes in the stress pattern of bone and prosthesis, but an increasing functionally graded prosthesis leads a lower level of stress in the prosthesis, and a decreasing functionally graded prosthesis can properly reduce the stress shielding among these three architectures. Due to the absence of similar results in the specialized literature, this paper is likely to fill a gap in the state-of-the-art bio-implants, and provide pertinent results that are instrumental in the design of porous femoral prostheses under normal walking loading conditions. Full article
Show Figures

Figure 1

31 pages, 1430 KiB  
Review
A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0
by Javaid Butt
Designs 2020, 4(2), 11; https://doi.org/10.3390/designs4020011 - 10 May 2020
Cited by 98 | Viewed by 26815
Abstract
Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and [...] Read more.
Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition. Full article
Show Figures

Graphical abstract

20 pages, 11863 KiB  
Article
Generative Design by Using Exploration Approaches of Reinforcement Learning in Density-Based Structural Topology Optimization
by Hongbo Sun and Ling Ma
Designs 2020, 4(2), 10; https://doi.org/10.3390/designs4020010 - 1 May 2020
Cited by 36 | Viewed by 6664
Abstract
A central challenge in generative design is the exploration of vast number of solutions. In this work, we extend two major density-based structural topology optimization (STO) methods based on four classes of exploration algorithms of reinforcement learning (RL) to STO problems, which approaches [...] Read more.
A central challenge in generative design is the exploration of vast number of solutions. In this work, we extend two major density-based structural topology optimization (STO) methods based on four classes of exploration algorithms of reinforcement learning (RL) to STO problems, which approaches generative design in a new way. The four methods are: first, using ε -greedy policy to disturb the search direction; second, using upper confidence bound (UCB) to add a bonus on sensitivity; last, using Thompson sampling (TS) as well as information-directed sampling (IDS) to direct the search, where the posterior function of reward is fitted by Beta distribution or Gaussian distribution. Those combined methods are evaluated on some structure compliance minimization tasks from 2D to 3D, including the variable thickness design problem of an atmospheric diving suit (ADS). We show that all methods can generate various acceptable design options by varying one or two parameters simply, except that IDS fails to reach the convergence for complex structures due to the limitation of computation ability. We also show that both Beta distribution and Gaussian distribution work well to describe the posterior probability. Full article
Show Figures

Figure 1

14 pages, 1966 KiB  
Article
Digital Triplet Approach for Real-Time Monitoring and Control of an Elevator Security System
by Michael M. Gichane, Jean B. Byiringiro, Andrew K. Chesang, Peterson M. Nyaga, Rogers K. Langat, Hasan Smajic and Consolata W. Kiiru
Designs 2020, 4(2), 9; https://doi.org/10.3390/designs4020009 - 21 Apr 2020
Cited by 21 | Viewed by 9069
Abstract
As Digital Twins gain more traction and their adoption in industry increases, there is a need to integrate such technology with machine learning features to enhance functionality and enable decision making tasks. This has lead to the emergence of a concept known as [...] Read more.
As Digital Twins gain more traction and their adoption in industry increases, there is a need to integrate such technology with machine learning features to enhance functionality and enable decision making tasks. This has lead to the emergence of a concept known as Digital Triplet; an enhancement of Digital Twin technology through the addition of an ’intelligent activity layer’. This is a relatively new technology in Industrie 4.0 and research efforts are geared towards exploring its applicability, development and testing of means for implementation and quick adoption. This paper presents the design and implementation of a Digital Triplet for a three-floor elevator system. It demonstrates the integration of a machine learning (ML) object detection model and the system Digital Twin. This was done to introduce an additional security feature that enabled the system to make a decision, based on objects detected and take preliminary security measures. The virtual model was designed in Siemens NX and programmed via Total Integrated Automation (TIA) portal software. The corresponding physical model was fabricated and controlled using a Programmable Logic Controller (PLC) S7 1200. A control program was developed to mimic the general operations of a typical elevator system used in a commercial building setting. Communication, between the physical and virtual models, was enabled using the OPC-Unified Architecture (OPC-UA) protocol. Object recognition using “You only look once” (YOLOV3) based machine learning algorithm was incorporated. The Digital Triplet’s functionality was tested, ensuring the virtual system duplicated actual operations of the physical counterpart through the use of sensor data. Performance testing was done to determine the impact of the ML module on the real-time functionality aspect of the system. Experiment results showed the object recognition contributed an average of 1.083 s to an overall signal travel time of 1.338 s. Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop