Marek Balazinski

This paper presents the use of TOPSIS, a multi-criteria decision-making model combined with the Taguchi method to find the optimum milling parameters. TOPSIS is the Technique for Order Preference by Similarity to the Ideal Solution and shows the value of closeness to the positive ideal solution. This study shows the optimum combination of process parameters using the shortest distance from the ideal solution. The surface roughness and flank tool wear were considered the objectives for simultaneous optimization. After converting multiple responses into a single response, the Taguchi method was used to analyze and determine the optimum machining parameters. According to reported studies, the initial wear behavior and initial cutting conditions have significant effects on the tool wear progress. Several initial cutting parameters can contribute to tool life and therefore can be used to improve both tool life and surface roughness. However, the cutting speed may significantly affect tool wear and ultimate tool life. In this study, an innovative solution was proposed for interrupted machining with two different cutting speeds. The first level cutting speed was used for 1 s and the second level was used for the rest of the process. The experimental results indicate that the initial speed followed by the feed rate significantly affects tool life. In addition, using the proposed strategy with different levels of cutting speed during machining operations led to improved tool life and surface roughness compared to conventional machining with uniform cutting speed throughout the entire process.

In the metal cutting process, the tool condition directly affects the quality of the machined component. To control the quality of the cutting tool and avoid equipment downtime, it is essential to monitor its condition during the machining process. The primary purpose is to send a warning before tool wear reaches a certain level, which could influence product quality. In this paper, tool condition is monitored using fractal analysis of the spindle electric current signal. The current study analyzes the monitoring of the cutting tool when milling AISI 5140 steel with a four-flute solid carbide end mill cutter to develop monitoring techniques for wear classification of metal cutting processes. The spindle electric current signal is acquired using the machine tool internal sensor, which meets industrial constraints in their operating conditions. As a new approach, the fractal theory is referred to analyze the spindle electric current signal and then assess the tool wear condition during the metal cutting process. Fractal parameters were defined to extract significant characteristic features of the signal. This research provides a proof of concept for the use of fractal analysis as a decision-making strategy in monitoring tool condition.

Rapid tool wear and limited tool life are major problems when machining Inconel 718, which still need further attention. Amongst the reported strategies, limited studies have been reported on optimizing initial cutting conditions by means of tool life improvement. Therefore, in this work, the tool wear progress and tool life were investigated by varying the initial conditions in the transition period, which was set at four seconds. The transition point was discovered by previous works by the authors. After the transition point, similar cutting conditions were used as the reference condition. The tool wear morphology and size were recorded and analyzed in each condition. It was revealed that applying a lower cutting speed and feed rate in the transition period led to improved tool life as compared to the reference condition. In other words, the use of optimum levels of cutting parameters in the transition period of the cutting process may enhance tool life at higher cutting time. For instance, initial feed rate (0.15 mm/rev) and cutting speed (25 m/min) led to the improvement in the ultimate tool life by about 67% and 50%, respectively. Besides, applying the lower initial cutting speed, i.e., 25 m/min, increased the tool life by about 50% when the insert reached the maximum flank wear (v_Bmax) of 300 µm in comparison with those at higher initial cutting speeds. This phenomenon may lead to better insight into the effect of the influence of the initial cutting conditions in the transition period on tool life when machining hard-to-cut materials. Moreover, the built-up edge (BUE) was exhibited as the primary wear mode in all cutting conditions.

The TiC particles in titanium metal matrix composites (TiMMCs) make them difficult to machine. As a specific MMC, it is legitimate to wonder if the cutting mechanisms of TiMMCs are the same as or similar to those of MMCs. For this purpose, the tool wear mechanisms for turning, milling, and grinding are reviewed in this paper and compared with those for other MMCs. In addition, the chip formation and morphology, the material removal mechanism and surface quality are discussed for the different machining processes and examined thoroughly. Comparisons of the machining mechanisms between the TiMMCs and MMCs indicate that the findings for other MMCs should not be taken for granted for TiMMCs for the machining processes reviewed. The increase in cutting speed leads to a decrease in roughness value during grinding and an increase of the tool life during turning. Unconventional machining such as laser-assisted turning is effective to increase tool life. Under certain conditions, a “wear shield” was observed during the early stages of tool wear during turning, thereby increasing tool life considerably. The studies carried out on milling showed that the cutting parameters affecting surface roughness and tool wear are dependent on the tool material. The high temperatures and high shears that occur during machining lead to microstructural changes in the workpiece during grinding, and in the chips during turning. The adiabatic shear band (ASB) of the chips is the seat of the sub-grains’ formation. Finally, the cutting speed and lubrication influenced dust emission during turning but more studies are needed to validate this finding. For the milling or grinding, there are major areas to be considered for thoroughly understanding the machining behavior of TiMMCs (tool wear mechanisms, chip formation, dust emission, etc.).

Metal-matrix composites (MMCs) are made of non-metallic reinforcements in metal matrixes, which have excellent hardness, corrosion, and wear resistance. They are also lightweight and may pose a higher strength-to-weight ratio as compared to commercial titanium alloys. One of the MMCs with remarkable mechanical properties are titanium metal matrix composites (Ti-MMCs), which are considered a replacement for super-alloys in many industrial products and industries. Limited machining and machinability studies of Ti-MMCs were reported under different cutting and lubrication conditions. Tool wear morphology and life are among the main machinability attributes with limited attention. Therefore, this study presents the effects of cutting and lubrication conditions on wear morphology in carbide inserts when turning Ti-MMCs. To that end, maximum flank wear (VB) and cutting forces were recorded, and the wear morphologies within the initial period of the cut, as well as the worn condition, were studied under dry and wet conditions. Experimental results denoted that despite the lubrication mode used, abrasion, diffusion, and adhesion mechanisms were the main wear modes observed. Moreover, built-up layer (BUL) and built-up edge (BUE) were the main phenomena observed that increase the tendency of adhesion at higher cutting times.

Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing.