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Intelligent Machining: Process Optimisation

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 12746

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Special Issue Editor

Dr. Mirko Ficko

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Guest Editor

Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
Interests: machine tools; manufacturing; product lifecycle management; manufacturing and process optimisation; computer-aided manufacturing

Special Issue Information

Dear Colleagues,

Recent developments in information and communications technology, especially artificial intelligence, has enabled new advances in machining. Modern machining systems are highly capable and automated, but despite this, their performance depends largely on operators’ knowledge, created from a mix of theory and experiences. Intelligent machining tries to enable intelligent behaviour in the machining system with the integration of simulation, sensing, modelling, control, and monitoring of the process. Recording of the process input and output variables allows for machine learning and the intelligent prediction of machining system behaviour. The input process variables usually include machine, tool, and environment conditions, and output variables can include the productivity, part quality, and machining cost.

In this Special Issue, recent advances on the study of intelligent machining are highlighted and discussed, including but not limited to the following:

Intelligent machining systems;
Intelligent tools;
Intelligent clamping;
Modelling and simulation of machining;
Optimisation of process parameters;
Decision-making and control in machining;
Condition monitoring and fault diagnosis;
Machine self-awareness and maintenance;
Big data and cloud-based machining;
Machine vision and machining.

It is my pleasure to invite you to submit a manuscript for this Special Issue.

Prof. Dr. Mirko Ficko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

machining systems
modelling
simulation
optimisation techniques
intelligent machining
machine learning in machining

Published Papers (6 papers)

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Research

18 pages, 2189 KiB

Open AccessArticle

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

by Aleksandar Kosarac, Cvijetin Mladjenovic, Milan Zeljkovic, Slobodan Tabakovic and Milos Knezev

Materials 2022, 15(3), 700; https://0-doi-org.brum.beds.ac.uk/10.3390/ma15030700 - 18 Jan 2022

Cited by 29 | Viewed by 2153

Abstract

Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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31 pages, 3336 KiB

Open AccessArticle

A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints

by Mijodrag Milosevic, Robert Cep, Lenka Cepova, Dejan Lukic, Aco Antic and Mica Djurdjev

Materials 2021, 14(23), 7360; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14237360 - 30 Nov 2021

Cited by 5 | Viewed by 1463

Abstract

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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18 pages, 2922 KiB

Open AccessArticle

A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions

by Kriz George, Sathish Kannan, Ali Raza and Salman Pervaiz

Materials 2021, 14(21), 6717; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14216717 - 08 Nov 2021

Viewed by 1712

Abstract

As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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16 pages, 2937 KiB

Open AccessArticle

Development and Investigation of an Inexpensive Low Frequency Vibration Platform for Enhancing the Performance of Electrical Discharge Machining Process

by Abhimanyu Singh Mertiya, Aman Upadhyay, Kaustubh Nirwan, Pravin Pandit Harane, Ahmad Majdi Abdul-Rani, Catalin Iulian Pruncu and Deepak Rajendra Unune

Materials 2021, 14(20), 6192; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14206192 - 18 Oct 2021

Cited by 3 | Viewed by 1730

Abstract

Difficulty in debris removal and the transport of fresh dielectric into discharge gap hinders the process performance of electrical discharge machining (EDM) process. Therefore, in this work, an economical low frequency vibration platform was developed to improve the performance of EDM through vibration assistance. The developed vibratory platform functions on an eccentric weight principle and generates a low frequency vibration in the range of 0–100 Hz. The performance of EDM was evaluated in terms of the average surface roughness (R_a), material removal rate (MRR), and tool wear rate (TWR) whilst varying the input machining parameters viz. the pulse-on-time (T_on), peak current (I_p), vibration frequency (VF), and tool rotational speed (TRS). The peak current was found to be the most significant parameter and contributed by 78.16%, 65.86%, and 59.52% to the R_a, MRR, and TWR, respectively. The low frequency work piece vibration contributed to an enhanced surface finish owing to an improved flushing at the discharge gap and debris removal. However, VF range below 100 Hz was not found to be suitable for the satisfactory improvement of the MRR and reduction of the TWR in an electrical discharge drilling operation at selected machining conditions. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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24 pages, 8402 KiB

Open AccessArticle

Modeling and Optimization of Cut Quality Responses in Plasma Jet Cutting of Aluminium Alloy EN AW-5083

by Ivan Peko, Dejan Marić, Bogdan Nedić and Ivan Samardžić

Materials 2021, 14(19), 5559; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14195559 - 25 Sep 2021

Cited by 7 | Viewed by 1704

Abstract

The plasma jet cutting process has a high potential for the machining of aluminium and its alloys. Aluminium is well known as a highly thermally conductive and sensitive material, and because of that there exist uncertainties in defining process parameters values that lead to the best possible cut quality characteristics. Due to that, comprehensive analysis of process responses as well as defining optimal cutting conditions is necessary. In this study, the effects of three main process parameters—cutting speed, arc current, and cutting height—on the cut quality responses: top kerf width, bevel angle, surface roughness Ra, Rz, and material removal rate were analyzed. Experimentations were conducted on aluminium EN AW-5083. In order to model relations between input parameters and process responses and to conduct their optimization, a novel hybrid approach of response surface methodology (RSM) combined with desirability analysis was presented. Prediction accuracy of developed responses regression models was proved by comparison between experimental and predicted data. Significance of process parameters and their interactions was checked by analysis of variance (ANOVA). Desirability analysis was found as an effective way to conduct multi-response optimization and to define optimal cutting area. Due to its simplicity, the novel presented approach was demonstrated as a useful tool to predict and optimize cut quality responses in plasma jet cutting process of aluminium alloy. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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16 pages, 2578 KiB

Open AccessArticle

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

by Mirko Ficko, Derzija Begic-Hajdarevic, Maida Cohodar Husic, Lucijano Berus, Ahmet Cekic and Simon Klancnik

Materials 2021, 14(11), 3108; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14113108 - 05 Jun 2021

Cited by 21 | Viewed by 2475

Abstract

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using

k

-fold cross-validation. A lowest test root mean squared error (RMSE) of

0.2084

was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a

95

% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation. Full article

(This article belongs to the Special Issue Intelligent Machining: Process Optimisation)

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