Nor Amirah binti Mohd Amran, Mohd Sayuti bin Ab Karim, Rusdi bin Abd Rashid, Waleed Alghani and Nur Aqilah binti Derahman
This study aims to present a direct repurposing activity of consumed high-speed steel (HSS) hacksaw blade into fine-looking handmade knives to increase the awareness about…
Abstract
Purpose
This study aims to present a direct repurposing activity of consumed high-speed steel (HSS) hacksaw blade into fine-looking handmade knives to increase the awareness about sustainability by evaluating the relationship between the quality of material alloys and heat treatment as well as cultural aspects such as the treatment on the HSS hacksaw blade that will affect the material hardness.
Design/methodology/approach
The quality of HSS hacksaw blade samples was analyzed by using scanning electron microscope/energy dispersive X-Ray spectroscopy (SEM/EDX) through the identification of material element’s properties. Besides, finite element structural analysis was performed by using SolidWorks Simulation to evaluate the material performance by determining the Von Mises stress to find the factor of safety of the knife designs. Then, the effect of tribology implementation toward mechanical properties of the handmade knives was determined by using a Rockwell C hardness test.
Findings
It is found that the material composition of carbon plays a vital role in increasing and improving the hardness and wear resistance of the HSS hacksaw blade. The Von Mises stress obtained is lower than the yield strength of 3,250 MPa by 71.44 per cent with the safety factor of 3.58,which means the design will not be subjected to failure. The mechanical properties of the HSS hacksaw blade such as hardness were determined averagely by 5 per cent of hardness increase.
Originality/value
It has been validated that the tribological effect toward the material characteristic leads to hardness changes which contributed to the enhancement of tool life of the HSS hacksaw blade, thus producing better quality knives.
Details
Keywords
Yusuf S. Dambatta, Mohd Sayuti, Ahmed A.D. Sarhan, Hamdi Bin Ab Shukor, Nur Aqilah binti Derahman and Sunusi Marwana Manladan
Optimisation of grinding processes involves enhancing the surface quality and reducing the cost of manufacturing through reduction of power consumptions. Recent research works…
Abstract
Purpose
Optimisation of grinding processes involves enhancing the surface quality and reducing the cost of manufacturing through reduction of power consumptions. Recent research works have indicated the minimum quantity lubrication (MQL) system is used to achieve near dry machining of alloys and hard materials. This study aims to provide an experimental analysis of the grinding process during machining of aluminium alloy (Al6061-T6). MQL nanofluid was used as the lubricant for the grinding operations. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality was studied. Adaptive neuro-fuzzy inference system (ANFIS) prediction modelling was used to predict the specific normal force, specific tangential force and surface quality, the ANFIS models were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further study shows that both the specific grinding forces and surface roughness are inversely proportional to the nanofluid concentration. Also, the depth of cut and table feed rate were found to have a directly proportional relationship with both the grinding forces and surface roughness. Moreover, higher MQL air pressure was found to offer better delivery of the atomised nanofluid into the grinding region.
Design/methodology/approach
Grinding experiments were performed using MQL nanofluid as the lubricant. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality has been studied.
Findings
The grinding process parameters were optimised using Taguchi S/N ratio analysis, whereas the prediction of the response parameters was done using ANFIS modelling technique. The developed ANFIS models for predicting the specific normal force, specific tangential force and surface quality were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further findings show that both the specific grinding forces and surface roughness are inversely proportional to the percentage of nanoparticle concentration in the lubricant. Also, the depth of cut and table feed rate were found to exhibit a direct proportional relationship with both the grinding forces and surface roughness, while high MQL air pressure was observed to offer more efficient delivery of the atomised nanofluid into the grinding region.
Practical implications
The work can applied into manufacturing industries to prevent unnecessary trials and material wastages.
Originality/value
The purpose of this study is to develop an artificial intelligent model for predicting the outcomes of MQL grinding of the aluminium alloy material using ANFIS modelling technique.