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Proceedings of Congress on Control, Robotics, and Mechatronics : CRM 2023
High-quality research papers presented at the International Conference of Mechanical and Robotic Engineering 'Congress on Control, Robotics, and Mechatronics' (CRM 2023), jointly organised by Modi Institute of Technology, Kota, India, and Soft Computing Research Society, India, during 25-26 March 2023.
Book
Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review
Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.
Journal Article
Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)
A non-dominated sorting modified teaching–learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed Vc, feed rate (f), depth of cut (ap) and nose radius Nr is formulated. A CNC turning machine is used to perform experiments with cemented carbide tool at an insert angle of 80° and the response variables known as surface finish and material removal rate are measured. A response surface model is rendered from the experimental results to derive the minimization function of surface roughness Ra and maximization function of material removal rate (MRR). Both optimization functions are solved simultaneously using NSMTLBO. A fuzzy decision maker is also integrated with NSMTLBO to determine the preferred optimum machining parameters from Pareto-front based on the relative importance level of each objective function. The best responses Ra = 2.2347 µm and MRR = 96.835 cm3/min are predicted at the optimum machining parameters of Vc = 160 mm/min, f = 0.5 mm/rev, ap = 0.98 mm and Nr = 0.8 mm. The proposed NSMTLBO is reported to outperform other six peer algorithms due to its excellent capability in generating the Pareto-fronts which are more uniformly distributed and resulted higher percentage of non-dominated solutions. Furthermore, the prediction results of NSMTLBO are validated experimentally and it is reported that the performance deviations between the predicted and actual results are lower than 3.7%, implying the applicability of proposed work in real-world machining applications.
Journal Article
The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works
A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research.
Journal Article
Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining
This paper proposes and demonstrates a domain-adapted ensemble machine learning approach for enhanced prediction of surface roughness (Ra) during the machining of polymeric materials. The proposed model methodology employs a two-stage pipelined architecture, where classified data are fed into the model for regressive analysis. First, a classifier (Logistic Regression or XGBoost, selected based on performance) categorizes machining data into distinct regimes based on cutting Speed (Vc), feed rate (f), and depth of cut (ap) as inputs. This classification leverages output discretization to mitigate data imbalance and capture regime-specific patterns. Second, a regressor (Support Vector Regressor or XGBoost, selected based on performance) predicts Ra within each regime, utilizing the classifier’s output as an additional feature. This structured hybrid approach enables more robust prediction in small, noisy datasets characteristic of machining studies. To validate the methodology, experiments were conducted on Polyoxymethylene (POM), Polytetrafluoroethylene (PTFE), Polyether ether ketone (PEEK), and PEEK/MWCNT composite, using a L27 Design of Experiments (DoEs) matrix. Model performance was optimized using k-fold cross-validation and hyperparameter tuning via grid search, with R-squared and RMSE as evaluation metrics. The resulting meta-model demonstrated high accuracy (R2 > 90% for XGBoost regressor across all materials), significantly improving Ra prediction compared to single-model approaches. This enhanced predictive capability offers potential for optimizing machining processes and reducing material waste in polymer manufacturing.
Journal Article
Exploiting the mechanical properties and optimising wear characteristics of Mg–Al–Zn hybrid composites reinforced with Si3N4 and MoS2
The advancement of lightweight structural materials with enhanced wear resistance continues to be a significant problem for magnesium-based alloys exploited in engineering applications. This research investigates the constraints of mechanical integrity and subpar tribological performance in Mg–Al–Zn alloys by integrating Si
3
N
4
and MoS
2
hybrid reinforcements through a powder metallurgy approach. The effect of reinforcement content on density, hardness, and compressive strength was assessed, and wear behaviour was optimised through a Taguchi L9 orthogonal array to determine the main variables influencing the tribological responses. Three different trials (1–3) were used to analyse the mechanical properties with varied processing parameters, and its microstructure and mechanical properties were analysed as per ASTM standards. The microstructure observation revealed that trial 1 (Mg–Al–Zn alloy + 2 wt% Si
3
N
4
+ 2 wt% MoS
2
) and trial 2 (Mg–Al–Zn alloy + 4 wt% Si
3
N
4
+ 2 wt% MoS
2
) showed Si
3
N
4
and MoS
2
particles agglomerating in the Mg alloy due to lower surface energy, while trial 3 (6 wt% of Si
3
N
4
, 2 wt% of MoS
2
) exhibited a refined grain structure and acted as nucleation sites for grain refinement during sintering. SEM morphology inferred that the ceramic particles are uniformly distributed in trial 3 comprising of MoS
2
, 510 °C of sintering temperature, 2.5 h soaking time, 550 MPa compaction pressure, improving mechanical properties such as hardness (43.47 ± 0.1%), compressive strength (51.16 ± 0.2%), and corrosion resistance (30.86 ± 0.01%) compared to trials 2, 3, and the Mg–Al–Zn alloy. XRDA confirms that due to higher sintering temperatures, Mg alloy interacts with ceramic particles to form the Mg
2
Si, Mg
3
N
2
interface. The intermixture’s enhanced particle diffusion and bonding resulted in improved corrosion resistance. ANOVA analysis confirmed that Trial 3 confirmed that applied load significantly influences wear rate and CoF, with a contribution exceeding 90% and
p
< 0.05 in CoF analysis, followed by sliding speed and distance.
Journal Article
Drilling Parameters Analysis on In-Situ Al/B4C/Mica Hybrid Composite and an Integrated Optimization Approach Using Fuzzy Model and Non-Dominated Sorting Genetic Algorithm
In-situ hybrid metal matrix composites were prepared by reinforcing AA6061 aluminium alloy with 10 wt.% of boron carbide (B4C) and 0 wt.% to 6 wt.% of mica. Machinability of the hybrid aluminium metal matrix composite was assessed by conducting drilling with varying input parameters. Surface texture of the hybrid composites and morphology of drill holes were examined through scanning electron microscope images. The influence of rotational speed, feed rate and % of mica reinforcement on thrust force and torque were studied and analysed. Statistical analysis and regression analysis were conducted to understand the significance of each input parameter. Reinforcement of mica is the key performance indicator in reducing the thrust force and torque in drilling of the selected material, irrespective of other parameter settings. Thrust force is minimum at mid-speed (2000 rpm) with the lowest feed rate (25 mm/min), but torque is minimum at highest speed (3000 rpm) with lowest feed rate (25 mm/min). Multi-objective optimization through a non-dominated sorting genetic algorithm has indicated that 1840 rpm of rotational speed, 25.3 mm/min of feed rate and 5.83% of mica reinforcement are the best parameters for obtaining the lowest thrust force of 339.68 N and torque of 68.98 N.m. Validation through experimental results confirms the predicted results with a negligible error (less than 0.1%). From the analysis and investigations, it is concluded that use of Al/10 wt.% B4C/5.83 wt.% mica composite is a good choice of material that comply with European Environmental Protection Directives: 2000/53/CE-ELV for the automotive sector. The energy and production cost of the components can be very much reduced if the found optimum drill parameters are adopted in the production.
Journal Article
Targeted Pre-Treatment of Hemp Fibers and the Effect on Mechanical Properties of Polymer Composites
Research on plant-fiber-reinforced composites has gained significant research interest since it generates composites with exceptional mechanical properties; however, the potential of hemp fibers can only be fully exploited if the fibers are well separated from the bundle to achieve cellulose-rich fibers. This is because well-separated bast fibers that are long and exhibit higher fiber aspect ratio enhance the mechanical properties of the composite by influencing property translations upon loading. A key feature for successful implementation of natural fibers is to selectively remove non-cellulosic components of hemp fiber to yield cellulose-rich fibers with minimal defects. Targeted pre-treatment techniques have been commonly used to address the aforementioned concerns by optimizing properties on the fiber’s surface. This in turn improves interfacial bonding between the fibers and the hydrophobic polymer, enhances the robustness of hemp fibers by improving their thermal stability and increases resistance to microbial degradation. In this study, we comprehensively review the targeted pre-treatment techniques of hemp fiber and the effect of hemp fiber as a reinforcement on the mechanical properties of polymeric composites.
Journal Article
A comparative study on acoustical properties using waste recycled porous materials for environmental sustainability
As noise pollution intensifies in urban areas, the need for sustainable and effective sound-reducing porous materials becomes increasingly critical. This research addresses that need by developing gypsum-based composites enhanced with vermiculite and recycled rigid polyurethane (RPU) powder, using a blend-press-sinter methodology. Gypsum-based composites were chosen for their cost-effectiveness, recyclability, structural stability, and sound-absorbing properties, all with minimal environmental impact. This approach supports the circular economy by repurposing waste materials. High-resolution scanning electron microscopy (HR-SEM) and Fourier transform infrared spectroscopy (FTIR) were employed to analyze the size, structure, uniformity, and presence of organic and inorganic compounds. Using response surface methodology (RSM) for optimization, the ideal formulation for the noise reduction coefficient (NRC) was identified, with an optimal mix of 5.3 wt% vermiculite and 6.5 wt% RPU, achieving an NRC value of 0.3628. Acoustic simulations using COMSOL Multiphysics, guided by the Johnson-Allard model, demonstrated that the optimized composite effectively reduced sound pressure levels by 17 to 58 dB across the 200 to 2000 Hz frequency range. These findings underscore the composite’s potential for room acoustics applications. By incorporating recycled and natural materials, this approach not only enhances acoustic performance but also promotes sustainable material practices.
Journal Article
Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method
Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.
Journal Article