Innovative Mechanical Engineering

ANALYSIS, MODELLING AND OPTIMIZATION OF CHIP COMPRESSION RATIO IN MEDIUM TURNING OF C45E STEEL

2025-06-12T01:15:51+08:00

Given the multiple significance of chip compression ratio (CCR) for machinability assessment, this study focuses on the analysis of CCR in medium longitudinal turning of C45E steel. The standard 23 full factorial design was used to arrange three main cutting parameters, i.e., depth of cut, feed rate and cutting speed, at two levels. Based on experimental data, a quasi-linear CCR prediction model was developed for better understanding of the main and interaction effects of the considered cutting parameters. The obtained results suggest the dominant main effect of depth of cut and the interaction effect of cutting speed and feed rate. In addition to modelling, a Pareto multi-objective optimization problem was formulated and solved using a multi-objective genetic algorithm (MOGA). CCR and material removal rate were set as objective functions, and surface roughness and chip slenderness ratio as functional constraints. The analysis of Pareto sets revealed that the turning parameters of the highest significance are depth of cut and cutting speed. A perfect linear relationship between material removal rate and CCR was also identified.

DEVELOPMENT OF PREDICTIVE CUTTING FORCE MODELS IN TURNING USING DIFFERENT EXPERIMENTAL DESIGNS

2025-04-16T21:24:40+08:00

Cutting forces are a very important indicator of the machining process and play a significant role in defining the machinability of the material, tool wear, vibrations, temperature in the cutting zone, process accuracy, modeling, monitoring and management of the machining process. The modeling of cutting forces is relatively difficult due to a number of influencing factors and the lack of knowledge of the interaction between them. The paper discusses and compares the application of different experimental designs (full factorial design, Box–Behnken and Taguchi’s design) for the development of the main force prediction models of varying mathematical forms. Dry longitudinal single-pass turning of not heat-treated non-alloyed and low-alloyed steels with carbon content higher than 0.55% was considered. Main cutting force estimates were acquired by varying feed rate, depth of cut and rake angle and by the application of the Walter machining calculator. Initially, linear, quasi-linear, power and quadratic models were developed and compared. In addition, based on dimensional analysis (DA), an approach that reduces the number of needed experimental trials, a cutting force prediction model was proposed.

MASS MINIMIZATION-BASED OPTIMIZATION OF LOADER MANIPULATOR DRIVE MECHANISMS

2025-06-21T21:09:46+08:00

This paper presents a structural optimization approach for the manipulator drive mechanisms of a wheel loader, with the primary objective function of minimizing the mass of kinematic chain members while ensuring allowable strength and stiffness. The study focuses on evaluating the influence of manipulator mechanism parameters on structural characteristics, such as cross-sectional dimensions and wall thicknesses, under static loading conditions derived from a typical operating cycle. Finite element analysis (FEA) is used to evaluate stress distribution and deformation, while optimization is performed by allowable stress constraints. Based on the analysis results, a minimum mass criterion was defined as part of a multi-criteria optimal synthesis procedure for the manipulator drive mechanisms. The analysis shows that reducing the mass of the manipulator not only enhances structural efficiency but also contributes to improved fuel economy, lower energy consumption, and enhanced overall operational performance of the loader.

ELASTIC CURVE-BASED ANALYSIS FOR BENT BEAM PROFILE OPTIMIZATION

2025-04-29T20:02:31+08:00

This paper investigates the selection of cross-sectional shapes for beams based on the theory of elastic bending and the shapes of elastic curves under various forms, intensities, and distributions of loads on beams with different boundary conditions. Choosing the cross-section profile shape of a bent beam is crucial for optimizing its performance under various loading conditions. Different shapes offer varying levels of efficiency in terms of strength, stiffness, and material usage. The linear elastic theory of bending is utilized to derive the differential equation of the elastic curve for bent beams. By solving this second-order differential equation using the direct integration method and the Clebsch procedure, the elastic curves are obtained. Various loading positions and scenarios are analyzed for overhanging and cantilever elastic beams, demonstrating the correlation between beam bending stiffness, shape factor, and degree of utilization of the cross-sectional shape. We apply the criterion of ultimate bending strength (flexural strength) of steel for dimensioning beams. Thus, the characteristic dimension of the profile is determined according to the maximum value of the bending moment for each type of beam, considering the values and distribution of the load. For the same bent beam and loading values, different cross-section profiles are suggested, and the maximal deflection in each case is obtained. Based on these findings, the best choice for profile selection is considered. The study also examines the relationship between shape factors and the degree of utilization of the cross-section, and the concept of the ideal shape of beams with variable stiffness along their length. The results provide insights into the optimal selection of cross-sectional shapes for beams subjected to bending, helping engineers and designers develop a sense for the most efficient selection of cross-sectional shapes.

TOWARDS NON-INTRUSIVE, DATA-DRIVEN DETECTION OF DISTRICT HEATING SYSTEM CONSUMER BEHAVIOR

2025-06-17T01:25:53+08:00

This paper takes initial steps in proposing non-intrusive, data-driven methodology for detecting consumer behavior patterns in District Heating Systems (DHS), focusing specifically on occupant presence and window opening events. Recognizing the limitations of legacy supply-driven control logic in DHS, paper aims to show the feasibility of a novel detection approach without ground truth data, utilizing environmental sensor data collected from residential apartments, including CO₂ concentration, indoor temperature, relative humidity, and outdoor temperature. Occupancy detection is performed by using analysis of CO₂ concentration signal. For window opening detection, a robust multivariate outlier detection based on the Mahalanobis distance computed over first-order differences in sensor signals was applied. Events are inferred by identifying anomalous combinations of rapid CO₂, temperature, and humidity changes during periods of thermal imbalance between indoor and outdoor environments. The results show consistent and interpretable behavioral patterns that demonstrate the feasibility of detecting consumer behavior using passive sensor data. This enables the integration of behavior-aware control strategies in DHS operations, with the potential to improve thermal comfort, reduce energy waste, and lower emissions.