Artificial intelligence–built analysis framework for the manufacturing sector: performance optimization of wire electric discharge machining system

In the era of industry 4.0, digitalization and smart operation of industrial systems contribute to higher productivity, improved quality, and efficient resource utilization for industrial operations and processes. However, artificial intelligence (AI)–based modelling and optimization analysis following a generic analysis framework is lacking in literature for the manufacturing sector thereby impeding the inclusion of AI for its potential application's domain. Herein, a comprehensive and generic analysis framework is presented depicting the key stages involved for carrying out the AI-based modelling and optimization analysis for the manufacturing system. The suggested AI framework is put into practice on wire electric discharge machining (WEDM) system, and the cutting speed of WEDM is adjusted for the stainless cladding steel material. Artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) are three AI modelling techniques that are trained with meticulous hyperparameter tuning. A better-performing model is chosen once the trained AI models have undergone the external validation test to investigate their prediction performance. The sensitivity analysis on the developed AI model is performed and it is found that pulse on time (Pon) is the noteworthy factor affecting the cutting speed of WEDM having the percentage significance value of 26.6 followed by the Dw and LTSS, with the percentage significance value of 17.3 and 16.7 respectively. The parametric optimization incorporating the AI model is conducted and the results pertain to the cutting speed are 27.3% higher than the maximum value of cutting speed achieved for WEDM. The cutting speed performance optimization is realized following the proposed AI-based analysis framework that can be applied, in general, to other manufacturing systems therefore unlocking the potential of AI to contribute to industry 4.0 for the smart operation of manufacturing systems

Sustainable EDM of Inconel 600 in Cu-mixed biodegradable dielectrics: Modelling and optimizing the process by artificial neural network for supporting net-zero from industry

The properties of Nickel-based superalloy(s) like stability at extreme conditions, greater strength, etc., complicate its cutting through conventional operations. Therefore, electric discharge machining (EDM) is preferred for its accurate cutting. However, the conventional dielectric i.e., kerosene used in EDM is hydrocarbon based which generates toxic fumes and contribute to the CO2 emissions during the discharging process in EDM. This affects the operator’s health and the environment. Therefore, the potentiality of five biodegradable dielectrics has been deeply examined herein to address the said issues. Nano copper powder is also employed for uplifting the cutting proficiency of these dielectrics. A set of 15 experiments was performed via full factorial design. An artificial neural network (ANN) is constructed to model and optimize the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). The highest MRR (5.527 mm3 /min) was achieved in coconut oil whereas for obtaining the lowest SR, the sunflower oil at powder concentration (Cp) of 1.0 g/100 ml is the best choice. Sunflower oil also gave a 17.05% better surface finish compared to other dielectrics. Amongst the biodegradable dielectrics, olive oil consumes lowest specific energy (SEC) i.e., 264.16 J/mm3 which is 28.8% less than the SEC of other oils. Furthermore, the maximum CO2 reduction of 72.8 ± 1.4% is achieved with Olive oil in comparison to that found with kerosene in EDM. The multi-objective optimization is conducted and sunflower oil with Cp of 0.667 g/100 ml is termed out to be optimal solution. The biodegradable dielectrics have demonstrated excellent performance for EDM to support net-zero goals from the industrial sector

Climate change and heat-waves : rural-to-urban migration in Pakistan, a silent looming crisis

The policy brief summarizes findings and recommendations from a recent study on climate-induced internal migration in Pakistan. Heat stress in particular affects agricultural productivity of winter crops like wheat, a staple food which is grown in arid and semiarid areas of Pakistan. Given the sensitivity of wheat crop to heat-stress, by 2030 the anticipated decline in wheat production will affect the rural poor and marginal households across Pakistan, who will be forced to cope with the situation and will incentivise the rural poor to out-migrate

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 μm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC’s nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments

TAGUCHI BASED OPTIMIZATION OF MACHINING PARAMETERS TO CONTROL SURFACE ROUGHNESS USING TiAlN-COATED TUNGSTEN CARBIDE MILLING CUTTER

Surface roughness is one of the determinant factors that governs the quality of machined surfaces. This paper experimentally studied effects of machining parameters on the surface roughness (Ra) for end milling of AISI 1045 work piece using a TiAlN coated carbide milling cutter. Taguchi optimization method was used to determine the optimal level of three control factors, namely, the feed rate, the spindle speed and the depth of cut. Analysis of variance demonstrates that the feed rate is the most significant parameter and contributes 47% for surface roughness. Finally, the contour plots of these three parameters have been analysed to determine the optimal ranges of control factors

EDM of Ti6Al4V under nano graphene mixed dielectric: A detailed roughness analysis

Surface finish has an essential role in superior performance of machined products which becomes crucial for sophisticated applications like invasive biomedical implants and aerospace components. Ti6Al4V is popular in these applications due to its exceptional characteristics of weight-to-strength ratio. However, Ti6Al4V is a difficult-to-cut material, therefore, non-traditional cutting techniques especially, Electric Discharge Machining (EDM) are widely adopted for Ti6Al4V cutting. The engagement of nano powders are used to upsurge the cutting rate and surface quality. Among the different powders a novel nano-powder additive i.e. graphene has not been tested in EDM of Ti6Al4V. Therefore, the potential of nano-graphene is comprehensively investigated herein for roughness perspective in EDM of Ti-alloy. The experimental design is based on Taguchi L18 orthogonal framework which includes six EDM parameters. The experimental findings are thoroughly discussed with statistical tests and physical evidence. The surface quality achieved with an aluminum electrode was found best amongst its competitors. Whereas, the worst surface asperities were noticed when brass electrode was used under graphene mixed dielectric. Moreover, it is conceived that the positive tool polarity provides lower roughness for all types of electrodes. Furthermore, optimal settings have been developed that warrant a reduction of 61.4 in the machined specimen's roughness compared to the average roughness value recorded during the experimentation

Progressing towards sustainable machining of steels : a detailed review

Machining operations are very common for the production of auto parts, i.e., connecting rods, crankshafts, etc. In machining, the use of cutting oil is very necessary, but it leads to higher machining costs and environmental problems. About 17% of the cost of any product is associated with cutting fluid, and about 80% of skin diseases are due to mist and fumes generated by cutting oils. Environmental legislation and operators’ safety demand the minimal use of cutting fluid and proper disposal of used cutting oil. The disposal cost is huge, about two times higher than the machining cost. To improve occupational health and safety and the reduction of product costs, companies are moving towards sustainable manufacturing. Therefore, this review article emphasizes the sustainable machining aspects of steel by employing techniques that require the minimal use of cutting oils, i.e., minimum quantity lubrication, and other efficient techniques like cryogenic cooling, dry cutting, solid lubricants, air/vapor/gas cooling, and cryogenic treatment. Cryogenic treatment on tools and the use of vegetable oils or biodegradable oils instead of mineral oils are used as primary techniques to enhance the overall part quality, which leads to longer tool life with no negative impacts on the environment. To further help the manufacturing community in progressing towards industry 4.0 and obtaining net-zero emissions, in this paper, we present a comprehensive review of the recent, state of the art sustainable techniques used for machining steel materials/components by which the industry can massively improve their product quality and production

Progressing towards Sustainable Machining of Steels: A Detailed Review

Machining operations are very common for the production of auto parts, i.e., connecting rods, crankshafts, etc. In machining, the use of cutting oil is very necessary, but it leads to higher machining costs and environmental problems. About 17% of the cost of any product is associated with cutting fluid, and about 80% of skin diseases are due to mist and fumes generated by cutting oils. Environmental legislation and operators’ safety demand the minimal use of cutting fluid and proper disposal of used cutting oil. The disposal cost is huge, about two times higher than the machining cost. To improve occupational health and safety and the reduction of product costs, companies are moving towards sustainable manufacturing. Therefore, this review article emphasizes the sustainable machining aspects of steel by employing techniques that require the minimal use of cutting oils, i.e., minimum quantity lubrication, and other efficient techniques like cryogenic cooling, dry cutting, solid lubricants, air/vapor/gas cooling, and cryogenic treatment. Cryogenic treatment on tools and the use of vegetable oils or biodegradable oils instead of mineral oils are used as primary techniques to enhance the overall part quality, which leads to longer tool life with no negative impacts on the environment. To further help the manufacturing community in progressing towards industry 4.0 and obtaining net-zero emissions, in this paper, we present a comprehensive review of the recent, state of the art sustainable techniques used for machining steel materials/components by which the industry can massively improve their product quality and production