Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Optimizing to Minimize Thrust Force in Drilling Carbon Fiber Reinforced Plastic Composites with HSS Drill Bit Using Taguchi-Pareto Particle Swarm Optimization Method

In this study, a robust method of Taguchi-Pareto (TP) coupled with particle swarm optimization (PSO) is proposed to minimize the thrust force in the drilling of carbon fiber reinforced plastic composites. Taguchi-Pareto is used against Taguchi (T) to emphasize the prioritization scheme essential for deploying the resources to parameters. Besides, and differently from earlier studies, particle swarm optimization is integrated with the Taguchi-Pareto to optimize the structure further. A further result is placed in the fitness function of the PSO to cultivate the velocity and position vectors. In the TP-PSO, the Pareto scheme is introduced to prioritize the factors based on the 80-20-rule. The Taguchi method yielded a feasible optimal parametric setting. The TPSO and TPPSO attained minimum thrust force in four and seven iterations, respectively. Furthermore, the PSO, TPPSO, and TPSO hold the first, second, and third positions, respectively. Results suggest that the proposed robust TPPSO offers an important indicator of optimization of the thrust force while drilling carbon fiber reinforced plastic composites using existing datasets. The usefulness of this effort is to help drilling operators and process engineers undertake energy-saving decisions

Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to execute the computations. Then the optimal solutions concerning the parameters of speed, feed rate, depth of cut, and nose radius were evaluated. With the Box Behnken as the objective function for the TLBO method, convergence was reached at 50 iterations with a class population of 5. The optimal parametric solutions are 800 rpm of speed, 0.06 min/min of feed rate, 1 min for depth of cut, and 0 min for nose radius. On the use of the regression method for the objective function, while the TLBO method was deployed, convergence was experienced after 50 iterations with a class population of 200 students. The optimal parametric solution is 1135rpm of speed, 0.06 min/min of feed rate, 1024 min of the depth of cut, and 0.61 min of nose radius. The speed, depth of cut, and nose radius showed higher values, indicating the use of more energy resources to accomplish the optimal goals using the regression method-based objective function. Therefore, the proposed method constitutes a promising route to optimize further the results of the Taguchi-Pareto-Box Behnken design for boring operation improvement

Integrated control mechanism of electrical discharge machining system for higher material removal rate

A servo control system in Electrical Discharge Machining (EDM) system is a control system with an appropriate control algorithm to position electrode on a particular distance from workpiece during machining process. The gap between the electrode and the workpiece is in the range of 10 – 50 μm. This ideal gap is achieved by applying an appropriate control algorithm to the servo control system of the EDM, and maintaining this gap will improve the Material Removal Rate (MRR) during the machining process. A considerable number of unique methods were proposed in the control algorithm in order to bring the electrode to the optimum position. This research proposes a new method called Integrated Control Mechanism (ICM) to improve the MRR of the EDM system. A rotary encoder is used as an additional mechanical sensor for the feedback control system in order to limit the electrode movement. Modelling of EDM is further investigated to predict the MRR parameter and optimization of electrode control position. A Neural Network system is used to predict MRR where Particle Swarm Optimization (PSO) and Differential Evolution (DE) are studied and simulated to optimize the Proportional Integral Derivative (PID) control parameters for the EDM system. Research conducted shows that the proposed Feed Forward Artificial Neural Network improves the accuracy of prediction in determining MRR by 2.92% and PID parameter optimization is successfully applied either using PSO or DE. The ICM is successfully implemented and the result shows that MRR is higher when compared to the normal machining process

Teaching Learning based Optimization Applied to Mechanical Constrained Design Problems

Amidst all the evolutionary optimization algorithms Teaching–Learning-Based Optimization (TLBO) seems to be a promising technique with relatively competitive performances. It outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. This dissertation presents the application of TLBO to various problems of mechanical engineering. Both constrained and unconstrained optimization has been performed on some manufacturing processes and design problems. Parametric optimization of three non-conventional machining processes namely electro-discharge machining, electrochemical machining and electro-chemical discharge machining, have been carried out and the results are compared with other evolutionary algorithms. Improvement in the existing TLBO algorithm has been incorporated in this dissertation using two schemes namely bit string mutation and replacement of worst solutions with fresh ones. Performance evaluation of these modifications have been presented in this dissertation by solving six optimization problems using original TLBO and proposed modifications. It has been found that better results are achieved in reaching the global optimal values by the use of these modifications. However, the results prefer the use of bit string mutation over scheme of replacing the worst solutions with fresh solutions in addition to the original logic of TLBO. The bit wise mutation and replacement of the worst solutions with fresh ones, proved an added advantage to the existing algorithm. Both these modifications resulted in a steeper convergence rate and finally provided global optimal solutions, and in some cases even better solutions than previously published results

Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies

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