Estimation of optimal machining control parameters using artificial bee colony

Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (Ra) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum Ra value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively

Individualized situation recognition using approximate case-based reasoning

Situation recognition is a significant part of humans perception as well as in the process of supervising human operators decision making in unplanned, imprecise, and uncertain environments. It is a process for identification of actual situation as the result of the occurring events within the environment. With outstanding performance, different situation recognition approaches for various applications have been developed. However, far too little attention has been paid to individualization of situation recognition. Situation recognition process could be individualized for supervision of human operators by learning and considering exclusive behaviors, preferences, and priorities of individual human operators. The event-discrete situations which are generated with a sequence of triggered actions could express individual behaviors of human operators. Accordingly, representation and identification of event-discrete situations are considered in this contribution. The purpose of this thesis is to propose a new framework for individualized situation recognition by applying novel knowledge representation and reasoning approaches. The most major challenges to be solved through the proposed framework for individualized situation recognition are modeling and representation of experienced knowledge as well as learning the new unknown situations. Those challenges could be stated as two questions as follows: How to model and represent the event-discrete situations to a knowledge base? How to reuse the knowledge for further situation recognition? In this work, Case-Based Reasoning (CBR) approach is applied to realize individualized situation recognition for supervision of human operators. The classical CBR is improved with a new learning process to recognize known occurring situations and generate new knowledge from unknown occurring situations. To deal with the noted challenges and realize the situation recognition, the classical CBR is also improved with application of a knowledge representation approach based on Situation-Operator Modeling (SOM) and fuzzy logic (FL). This work details the proposed CBR approach as a part of approximate reasoning. An integrated knowledge representation approach based on SOM and FL is introduced for representation of knowledge in the CBR. The SOM approach models the knowledge and describes the relations between discrete-event situations in a dynamic environment. The presented SOM approach supports the learning process by defining a sequence of situations and actions for each situation pattern. The FL approach structures the knowledge modeled by SOM for approximate knowledge inference. Additional processes need to be carried out in the proposed fuzzy SOM-based CBR to support online learning, data reduction, and knowledge indexing. The presented framework is applied for realization of an individualized lane-change situation recognition to supervise human drivers. The goal is to recognize the suitable driving situations for changing the lane for individual human drivers. The framework is evaluated using various data acquired by a driving simulator. This evaluation is done using different test drivers to highlight the effectiveness of the proposed approach for individualized situation recognition. The results demonstrate that the proposed framework can realize a successful driving situation recognition in terms of accuracy, detection rate, false alarm rate, and recognition elapsed time. It is shown that individualized situation recognition can significantly improve the recognition accuracy.Situationserkennung ist sowohl ein wichtiger Aspekt der menschlichenWahrnehmung wie auch bei der Überwachung der Entscheidungsfindung eines menschlichen Nutzers (human operator) in ungeplanten, ungenauen und unsicheren Umgebungen. Die Situationserkennung ist ein Prozess zur Identifizierung der aktuellen Situation, die aus Änderungen innerhalb der Umgebung resultiert. Für verschiedene Anwendungen wurden verschiedene Situationserkennungsansätze erfolgreich entwickelt. Die Individualisierung der Situationserkennung wurde in der Literatur bisher wenig beachtet. Der Situationserkennungsprozess konnte für die Unterstützung der menschlichen Nutzer durch das Lernen und Berücksichtigung individueller Verhaltensweisen, Präferenzen und Priorit¨aten verbessert werden. Ereignisdiskrete Situationen, die aus einer Folge von Aktionen resultieren, können individuelle Verhaltensweisen menschlicher Nutzer repräsentieren. In dieser Arbeit werden Repräsentation und Identifizierung von ereignisdiskreten Situationen betrachtet. In dieser Arbeit wird ein neues Framework für eine individualisierte Situationserkennung vorgeschlagen, indem neuartige Wissensrepräsentationen und Schlussfolgerungsansätze angewendet werden. Die wichtigsten Herausforderungen die durch die eingeführten Frameworks für die individualisierte Situationserkennung zu lössen sind, sind die Modellierung und Repr¨asentation von Wissen sowie das Lernen auf neuen, unbekannten Situationen. Diese Herausforderungen können basierend wie folgt angegeben werden: Wie können die ereignisdiskreten Situationen modelliert und in einen Wissensspeicher übertragen werden? Wie kann das gespeicherte Wissen für die weitere Situationserkennung verwendet werden? In dieser Arbeit wird der Case-Based Reasoning (CBR)-Ansatz verwendet, um eine individualisierte Situationserkennung zur Unterstützung menschlicher Nutzer zu realisieren. Der klassische CBR-Ansatz wird um einen neuen Lernprozessansatz erweitert, sodass bekannte Situationen erkannt werden können und neues Wissen aus unbekannten Situationen generiert werden kann. Um die erwähnten Herausforderungen zu bewältigen und die Situationerkennung zu realisieren, wird der klassische CBR-Ansatz um den Situation-Operator Modeling (SOM)-Ansatz und Fuzzy-Logik (FL)-Ansatz erweitert. Der SOM-Ansatz ermöglicht eine wissensorientierte Modellbildung und wird zur strukturierten Beschreibung der Beziehungen der ereignisdiskreten Situationen innerhalb dynamischer Umgebungen verwendet. Der präsentierte SOM-Ansatz wird innerhalb des Lernprozesses dazu verwendet, Sequenzen von Situationen und Aktionen einzelnen Situationsmustern zuzuordnen. Der FL-Ansatz strukturiert das SOM-basierte Wissen für approximate reasoning. Zusätzliche Prozesse werden zur Unterstützung des Onlinelernens, der Datenreduktion, und Wissensindexierung inerhalb des vorschlagenen fuzzy SOM-basierte CBRAnsatzes verwendet. Die Funktionsweise des vorgestellten Frameworks wird am Beispiel der Realisierung einer individualisierten Spurwechsel-Situationserkennung zur Unterstützung menschlicher Fahrer verdeutlicht. Hierbei ist das Ziel, eine passende Fahrsituation zum Spurwechsel zu erkennen. Das Framework wird unter Verwendung von Testdaten evaluiert. Die Datenerfassung erfolgt auf Basis eines Fahrsimulators und unterschiedlicher Testfahrer. Hierauf aufbauend wird der Nachweis einer erfolgreichen Verwendung des vorgestellten Anzatzes zur individualisierten Situationserkennung erbracht. Die Ergebnisse zeigen, dass unter Verweldung des vorgestellten Ansatzes eine erfolgreiche Fahrsituationerkennung realisiert werden kann, im Sinne von Genauigkeit, Erkennungsrate, negativer Alarmrate und Erkennungszeit. Abschliessend wird gezeigt, dass eine Individualisierung die Genauigkeit der Situationserkennung signifikant verbessert werden kann

Development of Dynamic Intelligent Risk Management Approach

A dynamic Risk Management (RM) provides monitoring, recognition, assessment, and follow-up action to reduce the risk whenever it rises. The main problem with dynamic RM (when applied to plan for, how the unknown risk in unexpected conditions should be addressed in information systems) is to design an especial control to recover/avoid of risks/attacks that is proposed in this research. The methodology, called Dynamic Intelligent RM (DIRM) is comprised of four phases which are interactively linked; (1) Aggregation of data and information (2) Risk identification (3) RM using an optional control and (4) RM using an especial control. This study, therefore, investigated the use of artificial neural networks to improve risk identification via adaptive neural fuzzy interface systems and control specification using learning vector quantization. Further experimental investigations are needed to estimate the results of DIRM toward unexpected conditions in the real environment

Robust optimization of ANFIS based on a new modified GA

Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. Training the adaptive modeling parameters in ANFIS is still a challengeable problem which has been recently considered by researchers. Hybridizing of a robust optimization algorithm with ANFIS as its training algorithm provides a scope to improve the effectiveness of membership functions and fuzzy rules in the model. In this paper, a new Modified Genetic Algorithm (MGA) by using a new type of population is proposed to optimize the modeling parameters for membership functions and fuzzy rules in ANFIS. As well, a case study on a machining process is considered to illustrate the robustness of the proposed training technique in prediction of machining performances. The prediction results have demonstrated the superiority of the presented hybrid ANFIS-MGA in term of prediction accuracy (with 97.74%) over the other techniques such as hybridization of ANFIS with Genetic Algorithm (GA), Taguchi-GA, Hybrid Learning algorithm (HL), Leave-One-Out Cross-Validation (LOO-CV), Particle Swarm Optimization (PSO) and Grid Partition method (GP), as well as RBFN and basic Grid Partition Method (GPM). In addition, an attempt is done to specify the effectiveness of different improvement rates on the prediction result and measuring the number of function evaluations required. The comparison result reveals that MGA with improvement rate 0.8 raises the convergence speed and accuracy of the prediction results compared to GA

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms

A multi-performance prediction model based on ANFIS and new modified-GA for machining processes

In the recent years, there has been an increasing interest in presenting a comprehensive modeling technique to predict machining performances in different processes. As well, this paper proposes a new hybrid technique anchored in adaptive network-based fuzzy inference system (ANFIS) and modified genetic algorithm (MGA) to model the relationship between machining parameters and multi performances. MGA which employs a new type of population is effectively applied as the training algorithm to optimize the modeling parameters, finding appropriate fuzzy rules and membership function in the model. In the proposed MGA, a list of parameters is randomly considered as a solution and a collection of experiences ’in optimizing the solution’ is utilized as population. To show the effectiveness of the presented model, it is applied to wire electrical discharge machining (WEDM) process for predicting material removal rate and surface roughness. The prediction results are compared with the most common prediction modeling techniques based on ANN and ANFIS–GA. The statistical evaluation results reveal that the ANFIS–MGA considerably enhances accuracy of the optimal solution and coverage rate

Modeling, reasoning, and application of fuzzy Petri net model: A survey

A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the ‘state-explosion problem’ are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions

Estimation of optimal machining control parameters using artificial bee colony

Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum R a value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively

Fuzzy logic for modeling machining process: a review

The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings : A Review of Methodologies and Machine Learning Approaches

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains