Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

Numerical modeling and optimization of waterjet based surface decontamination

The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study. The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an extended database representing jet based decoating has been compiled and feasibility of the damage free decontamination of various surfaces including highly sensitive ones is demonstrated. Artificial Intelligence techniques (Fuzzy Logic, Artificial Neural Networks, Genetic Computing) have been applied for processing of the acquired information and a realistic procedure of such an application has been developed and demonstrated. This procedure enables us to integrate available information about surface in question and existing numerical models. The developed procedure allows a user to incorporate both qualitative (linguistic) and quantitative (crisp) information into a process model and to predict operational conditions for treatment of an unknown surface using a readily detectable single experimental parameter that characterizes a deposit/substrata combination. The suggested technique is shown to perform reliably in the case of incomplete and chaotic information, where the traditional regression based methods fail. Numerical simulations of the two-phase flow inside a waterjet nozzle are conducted. Numerical solutions of the partial differential equations of the two-phase turbulent jet flow are obtained using FLUENT package. The numerical prediction of jet velocity profiles and the interface between the two phases (water - air) inside a nozzle are in good agreement with experimental data available in the literature. Thus the current problem setup and the results of simulations can be applied to improvement in the nozzle design. A realistic procedure for the design of the jet based surfaces decontamination developed, as a result of this study, is applied for optimization of the removal of the paint, rust, tar and rubber from the steel surface

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed

Monitoring of focusing tube wear during abrasive waterjet (AWJ) cutting of AISI 309

The paper deals with the investigating the possibility of using vibrations as a potential source of information for the detection of the malfunctions during the abrasive supplying and focusing tube wear in the process of AWJ. The tested material was the stainless steel AISI 309. Variable factors in the experiment were the abrasive mass flow ma and the focusing tube diameter df. The scanned vibration signal of the material was subjected to frequency analysis. With the increase of the abrasive mass flow, the shift of the amplitudes will follow the opposite direction and decrease. Frequency spectra of all assessed signals are similar by shape in the high-frequency area

Applications of optimization techniques for parametric analysis of non-traditional machining processes: A Review

The constrained applications of conventional machining processes in generating complex shape ge-ometries with the desired degree of tolerance and surface finish in various advanced engineering materials are being gradually compensated by the non-traditional machining (NTM) processes. These NTM processes usually have higher procurement, maintenance, operating and tooling cost. Hence, in order to attain their maximum machining performance, they are usually operated at their optimal or near optimal parametric settings which can easily be determined by the application of dif-ferent optimization techniques. In this paper, 133 international research papers published during 2012-16 on parametric optimization of NTM processes are extensively reviewed to have an idea on the selected process parameters, observed responses, work materials machined and optimization techniques employed in those processes while generating varying part geometries for their industrial use. It is observed that electro discharge machining is the mostly employed NTM process, applied voltage is the identified process parameter with maximum importance, surface roughness and material removal rate are the two maximally preferred responses, different steel grades are the mostly machined work materials and grey relational analysis is the most popular tool utilized for para-metric optimization of NTM processes. These observations would help the process engineers to attain the machining performance of the NTM processes at their fullest extents for different work material and shape feature combinations

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Taguchi-fuzzy based mapping of EDM-machinability of aluminium foam

Konvencionalna obrada aluminijskih pjena težak je zadatak s obzirom na činjenicu da su njihove ćelije i rubovi ćelija oštećeni i/ili urušeni tijekom procesa obrade i time pogoršana njihova izvorna svojstva. Taj se problem može prevladati u određenoj mjeri obradom ovog materijala procesom elektro-erozijske obrade (EDM). U ovom članku identificiraju se različiti kontrolni parametri mjerodavni za učinkovitu obradu aluminijske pjene. Tehnika utemeljena na Taguchi-fuzzy logici koristi se za oblikovanje parametara radnih značajki, kako bi se utvrdili optimalni parametri obrade za omjer maksimalnog skidanja materijala (MRR) i omjer minimalnog trošenje alata (TWR) u elektro-erozijskoj obradi. Taguchi-fuzzy utemeljeno mapiranje omjera maksimalnog skidanja materijala i omjera minimalnog trošenja alata s produktivnošću otkrilo je da u cilju postizanja veće produktivnosti pri obradi aluminijskih pjena, dva parametra, "pulse current" i "pulse-on time", moraju biti postavljeni visoko u kombinaciji s nisko postavljenim radnim ciklusom.Conventional machining of aluminium foams is a difficult task because of the fact that their cells and cell edges are damaged and/or collapsed during the machining processes and thereby their original properties deteriorated. This problem can be overcome to a certain extent by machining this material by Electro Discharge Machining (EDM) process. The present paper deals with identifying the various control parameters responsible for effective machining of aluminium foam. Taguchi-Fuzzy Logic based technique is used for parameter design of performance characteristics to determine optimal machining parameters for maximum Material Removal Rate (MRR) and minimum Tool Wear Rate (TWR) in EDM. Taguchi-fuzzy based mapping of MRR and TWR with productivity revealed that in order to achieve higher productivity while machining aluminium foam, the two parameters, pulse current and pulse-On time are required to be set high in combination with the low setting of duty cycle

Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material

Poor surface finish on die and mould transfers the bad quality to processed parts. High surface roughness is an example of bad surface finish that is normally reduced by manual polishing after conventional milling machining process. Therefore, in order to avoid disadvantages by manual polishing and disadvantage by the machining, a sequence of two machining operations is proposed. The main operation is run by the machining and followed by Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this sequence operation requires optimum input parameters to generate the lowest surface roughness. Hence, this paper aims to optimize the input parameters for both machining operations by three soft-computing approaches – Genetic Algorithm, Tabu Search, and Particle Swarm Optimization. The method adopted in this paper begins with a fitness function development, optimization approach usage and ends up with result evaluation and validation. The soft-computing approaches result outperforms the experiment result in having minimum surface roughness. Based on the findings, the conclusion suggests that the lower surface roughness can be obtained by applying the input parameters at maximum for the cutting speed and vibration frequency, and at minimum for machining feed rate. This finding assists manufacturers to apply proper input values to obtain parts with minimum surface roughness