In the case of advanced manufacturing technologies, which include Abrasive Water Jet Machining, optimization of control parameters is necessary to achieve appropriate efficiency and quality. One of the optimization methods used in the presented research is SAW, from the Multi-Criteria Decision Making (MCDM) group. In multi-criteria decision-making (MCDM) situations, the criterion weights are crucial components that have a big impact on the outcomes. A novel technique called MEREC (MEthod based on the Removal Effects of Criteria) was presented to find the objective weights of the criteria. The research covered cutting high-alloy steel using AWJ, under the Design of Experiment (DoE) within the L9 orthogonal table. Abrasive flow rate, pressure, and feed were selected as control parameters. The cutting depth (beneficial) and the roughness of the cut surface Sa (non-beneficial) were taken as the output parameters. The result of the research is the determination of the impact of individual control parameters and the determination of a set of control parameters from the point of view of efficiency and quality.

High-pressure water jet machining is characterized by wide possibilities of cutting diverse materials together with multi-layer materials with dissimilar properties, accurate cutting complex profiles, as well as conducting them in uncommon conditions, especially in cases of thick materials. An additional advantage of water jet technology is its environmental friendliness. This paper presents tests of the cutting performance of tool steel with the use of an abrasive water jet (AWJ). The state-of-the-art has shown insufficient scientific evidence in AWJ tool steels cutting using recycled abrasive materials. Therefore, the main motivation for this paper was to carry out research from an environment aspect. The reuse of abrasives and the use of recycled materials have immense potential to reduce processing costs while remaining environmentally friendly. The RSM method was used for modeling and optimization. A response surface design (RSM) is a package of an advanced design-of-experiment (DOE) approaches that support better understanding and optimize response, exploring the relationships between several explanatory variables and one or more response variables. Based on this research, feed rate is the key factor influencing the depth of cut, while the water nozzle diameter has a secondary effect, and the concentration of abrasive has the least influence on the depth of cut. High level of variance (the percentage of variability in the reaction that is interpreted by the formula) confirms that the models fit well to the investigational data.

The common machining technologies for difficult-to-machine materials do not remarkably ensure acceptable efficiency and precision in bulk materials cutting. High-energy abrasive water injection jet (AWIJ) treatment can cut diverse materials, even multi-layer composites characterized by divergent properties, accurately cutting complex profiles and carrying them out in special circumstances, such as underwater locations or explosion hazard areas. This work reports research on the AWIJ machining quality performance of X22CrMoV12-1 high-alloy steel. The response surface method (RSM) was utilized in modeling. The most influencing process control parameters on cut kerf surface roughness—abrasive flow rate, pressure, and traverse speed—were tested. The result is a mathematical model of the process in the form of a three-variable polynomial. The key control parameter affecting the cut slot roughness turned out to be the traverse speed. In contrast, pressure has a less significant effect, and the abrasive mass flow rate has the slightest impact on the cut slot roughness. Under the optimal conditions determined as a result of the tests, the roughness of the intersection surface Sq does not exceed 2.3 μm. Based on the ANOVA, we confirmed that the model fits over 96% appropriately with the research outcomes. This method reduces the computations and sharply determines the optimum set of control parameters.

The paper presents the application of an ant lion meta-heuristic algorithm to optimize the parameters of the water-abrasive cutting process of white marble The paper presents the application of an ant lion meta-heuristic algorithm to optimize the parameters of the water-abrasive cutting process of white marble

The article presents an intelligent temperature transducer (ITT), which can work with a two-wire or a three-wire platinum resistance temperature detector (RTD). The ITT design allowed for compensation of the RTD’s lead wire resistance. The ITT used the author’s auto-calibration procedure, which minimized linearity errors of the ITT and RTD processing characteristics, ITT offset and gain errors, and errors resulting from changes in the ITT operating conditions concerning the nominal conditions. The presented results of a simulation and experimental studies confirmed the high effectiveness of this procedure. The determined uncertainty of temperature measurement using the Monte Carlo method and the obtained experimental results confirmed the possibility of measuring temperatures in the range of 0–200 °C with an expanded uncertainty of 0.02 °C at a 99% confidence level.