Search

Mineral oil–based lubricants play an important role to serve industrial lubrication and cooling requirements. However, these oils are environmentally unacceptable due to their negative impact on human health and environmental ecology. The current research focuses on generating an eco-friendly oil which has enhanced lubrication properties. In this work, a four-ball tribometer was employed for friction and wear evaluation of a palm oil-commercial grade oil (PO-CGO) blend, which consisted of various weight fractions of functionalized multiwall carbon nanotubes (FMWCNTs) to lubricate steel-steel contacts of ball specimens mounted in tribometer. The specimens were lubricated by using 0.1 wt.% FMWCNT blended PO-CGO oil showed about 38% and 35% reduction in wear scar diameter and wear volume respectively compared to CGO lubricated test samples. Furthermore, roughness values of specimens obtained from atomic focal microscope (AFM) by using 0.1 wt.% nanomaterial blended lubricant showed about 88% lower values than that of CGO lubricated specimens. Raman spectroscopy and SEM/EDS results revealed the details of tribofilm formation and corresponding chemical composition on sliding contact surfaces of ball specimens subjected to PO-CGO and FMWCNT-blended oil lubrication. [ABSTRACT FROM AUTHOR]

In CNC milling, the feed rate scheduling is a frequently used method to increase machining quality and efficiency. Among the benefits of feed rate scheduling, this paper focuses on controlling the tool load and optimizing the machining time. Although the advantages of feed rate scheduling are undeniable, some areas remain still to be addressed. In order to control the tool load, geometric methods are often used, which are based on keeping a specific parameter, such as chip thickness or material removal rate (MRR) constant. However, a high level of tool load control can only be provided if cutting force models or experimental-based techniques are used. Besides traditional methods, this paper presents an artificial neural network (ANN)-based feed rate scheduling method to keep the tool load constant, using data gained by preliminary cutting experiments. A case study demonstrates that a significantly higher level of tool load control can be achieved with this method as compared to the geometric models. Besides controlling the tool load, the present feed rate scheduling method also addresses the consideration of acceleration limits which is of great importance for practical uses. The application of feed rate scheduling in trochoidal milling is also discussed in detail in this paper. This area has not received enough attention, as due to the limited fluctuation of cutter engagement, the tool load was considered as well-controlled. However, experiments have shown that in the case of trochoidal milling, the introduction of feed rate scheduling can still further increase the machining efficiency. Using the developed ANN-based feed rate scheduling method, significant progress could be made as compared to conventional technologies in controlling the cutting force and optimizing the machining time. In the present case study, a reduction of 50% in machining time was achievable by adjusting the feed rate without increasing the peak value of cutting force. [ABSTRACT FROM AUTHOR]

In recent years, trochoidal milling has become greatly widespread since it provides high productivity and long tool life, especially when cutting difficult-to-machine materials. Numerous studies have dealt with the analytical description and the experimental investigation of the evolution of cutting force, temperature, tool wear, chatter stability, etc. However, most of these analyses are focusing on the conventional circular and cycloid-shape trajectories. Only a few studies address the potential of improving the efficiency by modifying the tool path shape, leaving significant reserves untapped. If the mathematical formulation of the trajectory cannot be described easily, then a sophisticated optimisation algorithm is needed to determine the appropriate tool path shape. To overcome these difficulties at trochoidal milling of straight slots, the present work proposes a new tool path generation algorithm that can maximize the average material removal rate (MRR) while controlling the tool load. In this approach, the tool path is modelled by a B-spline curve whose control points are optimised using a differential evolution algorithm. The developed tool path generation strategy was compared through simulations and experiments with two widely used solutions viz the traditional cycloid-shaped tool path and a modern approach used in computer-aided manufacturing (CAM) systems. The results reveal that the developed method is able to control the tool load and the smoothness of the tool path, and most importantly, also exceeds the existing methods in terms of productivity. [ABSTRACT FROM AUTHOR]