The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.

Titanium and its alloys give immense specific strength, imparting properties such as corrosion and fracture resistance, making them the right candidate for medical and aerospace applications. There is a wide range of engineering applications that use titanium alloys in a variety of forms. The cost of these alloys is slightly higher in comparison to other variants due to the problematic extraction of the molten process. To reduce costs, titanium alloy products could be made by casting, isothermal forging, radial swaging, or powder metallurgy, although these techniques require some kind of finishing machining process. Titanium and its alloys are difficult to machine due to skinny chips leading to a small cutting tool-workpiece contact area. The thermal conductivity of titanium alloys is too low and the stress produced is too large due to the small contact area, which results in very high cutting temperatures. This paper deals with the experimental study of the influence of the Minimum Quantity Lubricant (MQL) environment in the milling of Ti-6Al-4V alloy considering the optimization of surface roughness and production rate. Taguchi-based TOPSIS and ANOVA were used to analyze the results. The experimental results show that MQL with vegetable oil is successfully applied in the milling of Ti-6Al-4V. The research confirms the suitability of TOPSIS in solving the Multiple Criteria Decision Making (MCDM) issue, by choosing the best alternative at Vc=120m/min, fz=0.065mm/tooth, and ap=0.2mm, where the surface roughness and material removal rate are 0.41µm and 44.1492cm3/min respectively. Besides, ANOVA can be used to predict the best parameters set in the milling process based on the regression model. The parameters predicted by ANOVA analysis do not coincide with any implemented parameters

The ranking results of the alternatives are heavily influenced by the decision making method, data normalization method, and calculation method for criterion weights. In this study, five multi-criteria decision-making methods were compared: MABAC, COCOSO, MAIRCA, VIKOR, and ROV. All of these methods use the same data normarmalization method. The weights for the criteria were determined using four methods: Equal, ROC, RS, and Entropy. In two different examples, the combination of multi-criteria decision-making methods with weighting methods for ranking alternatives is demonstrated. In each example, the number of options and the number of criteria are also different. The results in both examples show that regardless of the weighting method, multi-criteria decision-making methods always determine the same best solution. Furthermore, the direction of future research has been mentioned in the final section of this paper.

This paper investigates the influence of dry high-speed milling on the surface quality of JIS SKD61 hard steel, compared to the conventional coolant fluid method. This research was conducted in a Super MC 500 high-speed CNC milling machine with a Hitachi coated carbide 20mm in diameter. High-speed cutting parameters such as cutting speed V, cutting depth t, and spindle speed S were considered as variants. The experiment was designed based on Taguchi's L9. Surface quality, including Ra and Rq, was measured using the Mitutoyo Surftest SV-210. A mathematical regression model was found for the average values of surface roughness through regression analysis for dry and coolant fluid conditions. The chosen high-speed milling parameters and the respective Ra and Rq values were obtained by ANOVA. The grey relation scores for wet and dry milling surface quality for cut depth, feed rate, and cutting speed were 0.7527, 0.7869, 0.6302, and 0.8167, 0.7199, 0.6040, respectively. The results showed that the feed rate had the greatest influence on the surface quality during the high-speed coolant milling of hardened steel, while the depth of the cut had the greatest influence on the surface quality during the high-speed dry milling process.

This paper shows an optimization study on calculating the optimum replaced wheel diameter in internal grinding of stainless steel. In this work, the effects of the input factors, including the initial diameter, the grinding wheel width, the ratio between the length and the diameter of the work-pieces, the dressing depth of cut, the wheel life and the radial grinding wheel wear per dress on the optimum replaced grinding wheel diameter were considered. Also, the effects of cost components, including the cost of the grinding machine and the wheel cost were examined. Moreover, to estimate the influences of these parameters on the optimum replaced diameter, a simulation experiment was given and conducted by programming. From the results of the study, a regression equation was proposed to calculate the optimum replaced diameter.