WASPAS Optimization in Advanced Manufacturing

[Audio] My name is Andrzej Perec and I would like present paper Waspas Optimization in Advanced Manufacturing by myself and Dr. Alexandra Radomska Zalas.

[Audio] Agenda. The scope of presentation: Introduction Materials WASPAS method Test procedure Results and discussion Conclusions.

[Audio] Introduction. Usual cutting methods for heavy to machining metals do not provide enough effectiveness and accuracy. Abrasive water jet and pulsating water jet treatment are devoid of such disadvantages. An additional advantage of the water jet technology is its environmental friendliness. High-pressure water jet treatment is one of the fastest-growing advanced production technologies. It competes effectively with conventional methods of materials separation. This is due in large part to it from the wide possibilities of cutting both diverse materials, including multi-layer materials with different properties and precise cutting complex contour, or conducting them in uncommon conditions ( risk of detonation, conflagration, etc.). Materials machining by the abrasive water jet is more complicated in relation to conventional treatments. Water under a high-pressure condition, generated in the special high-pressure pump - intensifier, is transformed inside a nozzle to a high-speed jet, almost 800 m/s speed and grabs abrasive grains in mixing chamber, accelerate them to a high speed of the jet..

[Audio] Addition of dry abrasive to the jet dramatic rises cutting efficiency. As an effect, it is possible to cut each material. The most generally used abrasives material is garnet, but there are other natural and synthetic abrasives such as crushed glass, olivine, and aluminum oxide, that can be used. A careful selection of abrasive material is recommended to achieve a trade-off between nozzle life and the performance of the workpiece. Water jet machining is one of the newest advanced manufacturing technique. Control over this process is more complex compared to traditional cutting processes as more control parameters affect its performance. Therefore, research is underway on various methods to optimize this process, especially from the point of view of maximizing efficiency. Consequently, research is ongoing on the use of various optimization methods: mathematical and statistical based on heuristic algorithms and even on elements of artificial intelligence..

[Audio] Abrasive material - The garnet class contains closely associated, isomorphic minerals that may grow into each other or contain a small quantity of components from other garnets which substitute for the original. Garnets are isostructural, meaning they have the same crystalline structure resulting in similar crystal shapes and properties. Almandine (Fe3Al2[SiO4] 3) is the most popular form of garnet used in AWJ technology. In research the crushed rock garnet type J80A from Jinhong Mining located in Jiangsu, China was used. Mineral content, chemical composition and physical characteristics present in Table below..

[Audio] The shape of grains is close to isometric. Grains are characterized by sharp edges and corners. The grains color is from light to deep violet with a smaller number dark brown to black..

[Audio] Cut material - in tests, the Hardox 500 steel was used. It is bendable, weldable, and erosion-resistant steel, with a nominal hardness of 500 HBW. Hardox steel is manufactured in six grades. It is characterized by high-level wear endurance, ability to machining by special tools, decent weldability, and high mechanical properties. Hardox 500 steel has a typical yield strength 1400 MPa. The heat treatment is carried out from the normalized states through quenching in water and tempering at temperature 200 - 700°C. It is suitable for applications that demand higher wear resistance. Chemical composition presents Table below..

[Audio] WASPAS method. WASPAS is a weighted aggregate product score method for parametric optimization. The method may indicate one and many optimization responses. It combines the WSM ( Weighted Sum Model) and the Weighted Product Model ( WPM). The WASPAS method algorithm consists of successive stages. First, a decision matrix described by the equation is created: where: xij – the efficiency of the i-th variant for the j-th criterion, m – number of variants, n – number of evaluation criteria..

. Obraz zawierający rysunek Opis wygenerowany automatycznie.

. Obraz zawierający rysunek Opis wygenerowany automatycznie.

[Audio] Test procedure. The research was realized on the WaterJet CNC OMAX 60120 machine. Cutting of steel samples was executed by perpendicular directing of the jet to the top surface of the workpiece and causing linear movement of the cutting nozzle. The experiments were led using a full factorial design model under three of the control parameters: pressure, traverse speed, and abrasive flow rate. Process parameters such as the pressure, stand-off distance, and abrasive kind, were chosen based on earlier papers and the works of researchers: Hocheng et al., Hlavacova et al., and Spadlo et al. The cutting process carried out at the following parameters: Traverse speed: 100; 200 and 300 mm/min, The abrasive flow rate: 250; 350 and 450 g/min, Pressure: 350, 375 and 400 MPa. To appraising test results, the maximal cutting depth and roughness of the kerf surface were measured. The cutting depth was measured on a digital caliper altimeter Digimatic Height Gage. Surface roughness was determined with the Olympus LEXT OLS5000 laser scanning confocal microscope using Sq parameter. It is root mean square height, represents the root mean square value of ordinate values within the defined area. It is equivalent to the standard deviation of heights..

[Audio] The popular surface roughness parameter Sa shows the mean of the average height difference for the average plane. It gives steady effects because is not substantially affected by scratches, contamination, and measurement noise and the spacing of the varied texture assets. Sdr values increase as the surface texture becomes fine and rough. Sdr is a parameter indicating the rate of growth in the superficial area. If height parameters such as Sa and Sq are on a comparable level, the degree of fineness becomes finer as parameters Sdq ( gradient) and Sdr (superficial area) become larger..

[Audio] Results and discussion. The cutting samples was tested for two output parameters: cutting depth and surface roughness Sdr. The cutting depth was measured on the digital high gauge and the surface roughness with Olympus DSX1000 digital microscope combines unique accuracy and optical implementation with smart tools. In the Table 3 were given the results of the two output parameters: • the cutting depth, the beneficial factor, thus always requiring maximum values. • the cut surface roughness Sdr, the non beneficial factor, thus always requiring minimum values. and were given additionally the normalized parameters for all output factors, WSM and WPM factors and of course WSM, WSP and WSPM preferences with corresponding ranks..

[Audio] From this table, it becomes clearly evident that experimental trial number 25 getting rank nr 1, marked in red color. This means that the optimum is reached under this set of control parameters..

[Audio] Sample view of cut surfaces at optimum parameters are presented below. Shallow machining marks deepening in the lower part of the material were observed. Parallel traces of micro-cutting visible in the upper part of the sample, became less ordered in the middle and finally in the lower zone. Particularly, the characteristic signs of erosion by the abrasive grains in the form of parallel tracks could be observed..

[Audio] Conclusions. Research was carried out as per Taguchi L27 orthogonal array with three control factors, each at three levels. The basic aim of this tests was to obtain the optimal set of control parameters that affect the cutting depth and the surface roughness in the presence of multiple responses. The research conducted confirmed the equity of applying the method in multi-criteria optimization of the Hardox steel cutting process by AWJ and confirmed improvement in performance measures. On the basis the factors calculation concluded that the best possible combination of the process parameters to optimize the output parameters is follow: • abrasive flow rate = 450 g/min, • pressure = 400 MPa, • traverse speed = 100 mm/min. The change in traverse speed causes the biggest changes in effects, next by the abrasive flow rate and the smallest impact was noted for the pressure output. The proposed methodology of simultaneous optimization can be used also to optimize 4– 5 responses, and robustness can be checked. WASPAS approach can be carried out with different weights assigned to responses, and results can be compared. Further, a suitable technique can be used for weight optimization. Application of other optimization techniques such as GRA and VIKOR can also be explored..

[Audio] Thank You for Your attention! If you have any questions, please send an email.