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Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.

Conventional design of jigs and fixtures has become unsuitable given the requirements of modern technology and complexity and diversity in the production with the rapid update of products. Computer-aided design (CAD) of jigs and fixtures is an effective solution in this direction. The current paper focuses on a computer-aided design of the traditional jigs and fixtures and developed a system containing tailor-made software, created using the Visual Basic programming language and installed on it the viewer screen to show the part. The developed system has been built by connecting Visual Basic programming language to the SolidWorks software on which the part is drawn and saved as STEP AP-203 file format, and the system reads and extracts the data from the STEP AP-203 file. Heuristic rules of feature recognition are pre-prepared for checking the extracted geometric data and deciding which data shape will represent the machining feature; then, the system provides the optimum design of the traditional jigs and fixtures for a group of hollow cylindrical parts that contain a group of cross-holes on the cylinder body, whether perpendicular or offset from the cylinder’s axis, (inclined or inclined offset, or blind or through, by applying pre-prepared heuristic rules for the design of traditional jigs and fixtures.

Tolerance cost and machining time play crucial roles while performing tolerance allocation in complex assemblies. The aim of the proposed work is to minimize the above-said manufacturing objectives for allocating optimum tolerance to the components of complex assemblies, by considering the proper process and machine selections from the given alternatives. A novel methodology that provides a two-step solution is developed for this work. First, a heuristic approach is applied to determine the best machine for each process, and then a combined whale optimization algorithm with a univariate search method is used to allocate optimum tolerances with the best process selection for each sub-stage/operation. The efficiency of the proposed novel methodology is validated by solving two typical tolerance allocation problems of complex assemblies: a wheel mounting assembly and a knuckle joint assembly. Compared with previous approaches, the proposed methodology showed a considerable reduction in tolerance cost and machining time in relatively less computation time.

The proposed work aims to acquire the maximum number of non-linear assemblies with closer assembly tolerance specifications by mating the different bins’ components. Before that, the components are classified based on the range of tolerance values and grouped into different bins. Further, the manufacturing process of the components is selected from the given and known alternative processes. It is incredibly tedious to obtain the best combinations of bins and the best process together. Hence, a novel approach using the combination of the univariate search method and the harmony search algorithm is proposed in this work. Overrunning clutch assembly is taken as an example. The components of overrunning clutch assembly are manufactured with a wide tolerance value using the best process selected from the given alternatives by the univariate search method. Further, the manufactured components are grouped into three to nine bins. A combination of the best bins is obtained for the various assembly specifications by implementing the harmony search algorithm. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The results show that the contribution of the proposed novel methodology is legitimate in solving selective assembly problems.

In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L27 orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithm’s effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithm’s optimal combination of turning parameters.

Pocket milling has been regarded as one of the most widely used operations in machining. The surface quality of the machined pockets is an essential aspect of any engineering and medical applications. In the current study, rotary ultrasonic machining (RUM) was applied for milling micro-pockets on alumina (Al2O3) ceramic. The objective of this research was to analyze the effect of the tool overlapping parameters on the surface roughness, surface morphology and the profiles of the machined pockets. Subsequently, the effect of different tool path strategies was analyzed on the surface quality and the material removal rate (MRR) of the machined pockets. A scanning electron microscope is used for analyzing the tool wear mechanisms. The experimental results provide evidence that the surface roughness, surface morphology and the MRR have been significantly affected by the considered tool overlapping and the tool path strategies. Furthermore, among the selected tool overlapping parameters (5−25%) and the tool path strategies, the best surface roughness (Ra = 0.155 μm and Rt = 1.432 µm) of the machined pockets can be found at 20% of the tool overlapping with a mix of uni-directional and zigzag tool path strategy.