Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al‐Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat‐carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey‐ANFIS‐amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best‐suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.

In order to minimize makespan (C_max) without causing tool delay with the fewest copies of each tool type, this study investigates the concurrent scheduling of automated guided vehicles (AGVs), machines (MCs), tool transporter (TT), and tools in a multi-machine flexible manufacturing system (FMS). The tools are housed in a central tool magazine (CTM), accessible to and utilized by several machines. AGVs and the tool transporter (TT) move jobs and tools between machines. Since it involves allocating tool copies and AGVs to job operations, sequencing job operations on machines, and related trip operations, such as AGVs’ and TT’s empty trip and loaded trip times, this simultaneous scheduling problem is highly complicated. This issue is resolved using the symbiotic organisms search algorithm (SOSA), based on the symbiotic interaction strategies that organisms adapt to survive in the ecosystem. This study proposes a mixed nonlinear integer programming formulation to address this problem. Verification is performed using an industrial problem from a manufacturing organization. The results show that employing two copies for two tool types out of 22 tool kinds and one copy for the remaining tool types results in no tool delay, which causes a reduction in the C_max as well as cost. The industries that can benefit directly from this study are consumer electronics manufacturers, original equipment manufacturers, automobile manufacturers, and textile machine producers. The results demonstrate that the SOSA provides promising results compared to the flower pollination algorithm (FPA).