Intelligent nozzle design for the Laser Metal Deposition process in the Industry 4.0

Laser Metal Deposition (LMD) is an AM (Additive Manufacturing) process that enables to build 3D geometries or enhance the surface properties of the base material by the generation of a coating. With the aim of integrating the AM inside the Industry 4.0 trend and improve the quality of the resulting parts, smart nozzles are required. Therefore, authors have developed an intelligent LMD nozzle by means of the integration of various sensing and control systems in a continuous coaxial LMD nozzle prototype. The nozzle is capable of regulating the laser power based on the temperature measurement of the melt pool. Moreover, it adjusts the powder flux that reaches the processing area according to an algorithm that ensures a constant powder income per surface unit area. Lastly, the nozzle evaluates the geometry of the deposited clad using an optical sensor

DC Motor PID Control System for Tamarind Turmeric Herb Packaging on Rotary Cup Sealer Machine

The end of this research is to find PID tuning value on the packaging automation process using the PID method. By finding the most suitable PID tuning value, a fast packaging process is obtained. Herbal ingredients in herbs that are left in the open for a long time tend to be damaged more quickly. So after the production process ends, the herbs must be packaged quickly. With the packaging automation method, the product can be hygienic and does not spoil quickly. One of the most widely and easy-to-use for automation methods in the industry is the PID control method because it can accelerate the system response, stabilize the system to match the setpoint and minimize overshoot. This study will discuss how the design of the PID control system using DC motor transfer function modeling in Matlab and the Second Ziegler-Nichols PID tuning method, the effect of the load on the motor response, and the effect of PID on the production speed. The system was tested with PID tuning values are Kp = 12, Ki = 12,506, Kd = 0.0028785, speed motor 24 RPM and a load of 3,160 Kg produces a good output response are delay time = 0.502 s, rise time = 0.804 s, settling time = 4.023 s, peak time = 133.084 s, Overshoot = 0.125% and Steady State Error = 0%. The effect of PID control on production speed is 83% faster than manual production and 29% faster than systems without PID

Long term temperature stability of thermal cycler developed using low profile microprocessor cooler

Developing a low-cost thermal cycler for a polymerase chain reaction (PCR) is becoming interested in the pandemic era caused by viruses. PCR is the standard gold for the diagnostic. However, in a low-income country, the availability of the device is limited. In this work, the development of a thermal cycler uses electronic modules available in the market. The central part is thermoelectric for heating and cooling, an embedded system to control, and a low-profile cooling fan. The system temperature control used a combination of feedforward, bang-bang, and proportional-integral-derivative (PID) control. The control parameter of the PID was successfully obtained by using Chien servo tuning. The feedforward and bang-bang control are used to optimize the cooling cycle and minimize the rise time. The system shows a well-suited temperature accuracy at the denaturation, annealing, and extension temperature with a temperature deviation of less than 0.5 °C. System performance is maintained even though the system has been running non-stop for 24 hours. The low-profile cooling fan, which is usually used for CPU cooling, shows good results in maintaining temperature stability

Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structure

Proportional-integral-derivate (PID) controller has gained popularity since the advancement of smart devices especially in suppressing the vibration on flexible structures using different approaches. Such structures required accurate and reliable responses to prevent system failures. Swarm intelligence algorithm (SIA) is one of the optimization methods based on nature that managed to solve real-world problems. Crow search is a well-known algorithm from the SIA group that can discover optimum solutions in both local and global searches by utilizing fewer tuning parameters compared to other methods. Hence, this study aimed to simulate a PID controller tuned by SIA via crow search for vibration cancellation of horizontal flexible plate structures. Prior to that, an accurate model structure is developed as a prerequisite for PID controller development. After the best model is achieved, the proportional-integral-derivative-crow-search (PID-CS) performance was compared to a traditional tuning approach known as the Ziegler Nichols (ZN) to validate its robustness. The result revealed the PID-CS outperformed the proportional-integral-derivative-Ziegler Nichols (PID-ZN) with attenuation values of 44.75 and 42.74 dB in the first mode of vibration for single sinusoidal and real disturbances, respectively. In addition, the value of mean squared error (MSE) for PID-ZN and PID-CS for single sinusoidal disturbance are 0.0167 and 0.0081, respectively. Meanwhile, PID-ZN and PID-CS achieved 2.3981 × 10−4 and 2.3737 × 10−4 when they were exerted with real disturbance. This proves that the PID-CS is more accurate compared to the PID-ZN as it achieved the lowest MSE value

Formal Specification Language for Vehicular Ad-Hoc Networks

Vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (wireless Network), originally used to provide safety & comfort for passengers, & currently being used to establish Dedicated Short Range Communications (DSRC) among near by Vehicles (V2V Communications) and between vehicles and nearby fixed infrastructure equipments; Roadside equipments (V2I Communications). VANET was used also to warn drivers of collision possibilities, road sign alarms, auto-payment at road tolls and parks. Usually VANET can be found in Intelligent Transportation Systems (ITS). VANET is the current and near future hot topic for research, that has been targeted by many researchers to develop some applications and protocols specifically for the VANET. But a problem facing all VANET researchers is the unavailability of a formal specification language to specify the VANET systems, protocols, applications and scenarios proposed by those researchers. A specification language is a formal language that is used during the systems design, analysis, and requirements analysis. Using a formal specification language, a researcher can show “What his system does”, Not How. As a contribution of our research we have created a formal specification language for VANET. We made the use of some Romans characters & some basic symbols to represent VANET Systems & Applications. In addition, we have created some combined symbols to represent actions and operations of the VANET system and its participating devices. Our formal specification language covers many of the VANET aspects, and offers Validity Test and Consistency Test for the systems. Using our specification language, we have presented three different case studies based on a VANET system model we have created and put them into the system validity and consistency tests and showed how to describe a VANET system and its applications using our formal specification language

A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head

In recent years, the use of 3D anthropometry for product design has become more appealing because of advances in mesh parameterisation, multivariate analyses and clustering algorithms. The purpose of this study was to introduce a new method for the clustering of 3D head scans. A novel hierarchical algorithm was developed, in which a squared Euclidean metric was used to assess the head shape similarity of participants. A linkage criterion based on the centroid distance was implemented, while clusters were created one after another in an enhanced manner. As a result, 95.0% of the studied sample was classified inside one of the four computed clusters. Compared to conventional hierarchical techniques, our method could classify a higher ratio of individuals into a smaller number of clusters, while still satisfying the same variation requirements within each cluster. The proposed method can provide meaningful information about the head shape variation within a population, and should encourage ergonomists to use 3D anthropometric data during the design process of head and facial gear

Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis

In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods

Batch-to-batch iterative learning control of a fed-batch fermentation process

PhD ThesisRecently, iterative learning control (ILC) has been used in the run-to-run control of batch processes to directly update the control trajectory. The basic idea of ILC is to update the control trajectory for a new batch run using the information from previous batch runs so that the output trajectory converges asymptotically to the desired reference trajectory. The control policy updating is calculated using linearised models around the nominal reference process input and output trajectories. The linearised models are typically identified using multiple linear regression (MLR), partial least squares (PLS) regression, or principal component regression (PCR). ILC has been shown to be a promising method to address model-plant mismatches and unknown disturbances. This work presents several improvements of batch to batch ILC strategy with applications to a simulated fed-batch fermentation process. In order to enhance the reliability of ILC, model prediction confidence is incorporated in the ILC optimization objective function. As a result of the incorporation, wide model prediction confidence bounds are penalized in order to avoid unreliable control policy updating. This method has been proven to be very effective for selected model prediction confidence bounds penalty factors. In the attempt to further improve the performance of ILC, averaged reference trajectories and sliding window techniques were introduced. To reduce the influence of measurement noise, control policy is updated on the average input and output trajectories of the past a few batches instead of just the immediate previous batch. The linearised models are re-identified using a sliding window of past batches in that the earliest batch is removed with the newest batch added to the model identification data set. The effects of various parameters were investigated for MLR, PCR and PLS method. The technique significantly improves the control performance. In model based ILC the weighting matrices, Q and R, in the objective function have a significant impact on the control performance. Therefore, in the quest to exploit the potential of objective function, adaptive weighting parameters were attempted to study the performance of batch to batch ILC with updated models. Significant improvements in the stability of the performance for all the three methods were noticed. All the three techniques suggested have established improvements either in stability, reliability and/or convergence speed. To further investigate the versatility of ILC, the above mentioned techniques were combined and the results are discussed in this thesis