Digital Triplet Approach for Real-Time Monitoring and Control of an Elevator Security System

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 1.083s to an overall signal travel time of 1.338 s

Investigation on Optimal Cutting Parameters in Turning AISI 8660 Steel Using Silicon (Sic) Whisker Reinforced Ceramic Tool

This research was to investigate the effects of process parameter that is cutting speed, feed rate, depth of cut and machining time on the response variables in turning AISI 8660 material using whisker reinforced ceramic cutting tool. Cutting tools are weak and there is continuous effort to improving their performance and wear characteristics so that different grades of materials with varied degree of hardness are machined at minimal cost and economies of production can be realized during machining. This study investigated the rate tool wear and the cutting forces involved during the machining process. High speed machine lathe (Type: MORESEKI) was used on which a three force component dynamometer was mounted on the tool post to measure the cutting forces involved during the machining process. A Toolmakers microscope (model no: 80091) was used to measure the tool flank wear (VB) and the maximum tool wear recorded was 0.27mm and occurred at approximately 3.0 minutes during the machining process. Design of Experiment based on Taguchi technique was developed to obtain the experimental data. Response Surface Methodology (RSM) was used to analyze the data by developing 3D surface plots, contour plots and Main effects plots for Signal to Noise Ratio. The residuals plots analysis for cutting force revealed a normal probability plot for the data used indicating a close fit to the best of line. The histogram indicated 80% and 10% as the highest and lowest frequency for the cutting force. The optimal cutting conditions for toolwear were obtained at v = 158.28 mm/min, f = 1.116mm/rev, d = 1.38mm, and t = 2min with the process having a high composite desirability at 0.8557. The high composite desirability means that the process variable satisfies the target goals which are minimizing cutting forces and toolwear and that SiC whisker reinforced cutting is the recommended tool when machining this material

Process Modelling of Geothermal Drilling System Using Digital Twin for Real-Time Monitoring and Control

Currently, Kenya supplies its energy demand predominantly through hydroelectric power, which fluctuates due to poor and unpredictable rainfall in particular years. Geothermal energy is proposed as a clean and reliable energy source in meeting Kenya’s increasing energy demand. During geothermal drilling operations, disruptions due to tool wear and breakages increases the cost of operation significantly. Some of these causes can be mitigated by real-time monitoring of the tool head during operations. This paper presents the design and implementation of a digital twin model of a drilling tool head, represented as a section of a mechatronic assembly system. The system was modelled in Siemens NX and programmed via the TIA portal using S7 1200 PLC. The digital model was programmed to exactly match the operations of the physical system using OPC (open platform communications) standards. These operations were verified through the motion study by simultaneous running of the assembly system and digital twin model. The study results substantiate that a digital twin model of a geothermal drilling operation can closely mimic the physical operation