A novel drill bit design for reducing bone-chip morphology in orthopaedic bone drilling

Agarwal, Raj; Gupta, Vishal; Singh, Jaskaran

doi:10.1016/j.matpr.2022.02.408

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…[ 16 ] The literature has accounted that almost 11% of fracture treatments have screw‐related complications. [ 17 ] Therefore, in this new era of fabrication, AM technology is found to be a boon for orthopedic screws fabrication that has the potential to diminish bone‐screw‐related complications.…”

Section: Introductionmentioning

confidence: 99%

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

2022

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Additive manufacturing (AM) technology is an innovative technique that hasshown potential in several surgical innovations such as the fabrication of costeffective orthopedic screws. It is imperative that the process parameters used during the fabrication of bone screws determine the strength of the screw to support the load-bearing fracture sites. In this present work, the application of machine learning (ML) models was leveraged for predicting the compressive strength of additively manufactured orthopedic cortical screws. Different ML models such as k-nearest neighbors, support vector regression, decision trees, and random forest were compared to offer a robust predictive ML model. During the study, fused deposition modeling based additive manufacturing technology is used to fabricate poly(lactic acid) based cortical screws and compressive strength was monitored using the universal testing machine. The predictive ML models were developed by various independent parameters such as infill percentage, layer height, infill pattern and wall thickness. The adequacy and robustness of different ML models were observed at different error metrics. The error metrics of the random forest model were comparatively lower for testing data with a mean absolute error of 2.06 ± 0.91, root mean square error of 2.37 ± 0.9 and coefficient of determination of 0.96 ± 0.042. The results highlight that the random forest is the most adequate ML model for predicting the compressive strength of additively manufactured cortical screws.additive manufacturing, bone screw, compressive strength, machine learning | INTRODUCTIONAdditive manufacturing (AM) technology provides the ideal tailor-made solution for different orthopedic applications. [1] This technology offers various merits and functionalities such as precision, [2] ease of the process, [3] less material wastage, [4] personalization, [5] degree of freedom, [6] accuracy, [7] biocompatibility, [8] and customization [9] for the

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Section: Introductionmentioning

confidence: 99%

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

2022

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“…3 During the medical surgery, the increase in cutting force and surface roughness may damage the surrounding bone area in the form of microcracks, 4 drill-Vander, 5 surrounding soft tissue damage, 6 crack propagation, 7 bone asperities, 8 poor anchorage strength, 9 and chip clogging. 10 With further increase in cutting force and surface irregularities may lead to implant failure, 11 thermal osteonecrosis, 12 intolerable pain in the joints, 13 and nerve injury 14 that may result in post-operative revision surgery. Therefore, it becomes inevitably important for a surgeon to minimize the cutting force and surface damage during the surgery for the success of osteosynthesis and improve the healing of fractured bone.…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms

Agarwal

Gupta

Singh

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The ultrasonic-assisted drilling of bone is gaining significant attention in orthopedic research and clinical applications due to the advantage of reducing thermal necrosis, cutting force, and microcracks. Mechanical and surface morphological damage due to cutting force and surface roughness rise during bone drilling may result in implant failure during osteosynthesis. It is difficult to predict and measure cutting force and surface irregularities during real-time medical orthopedic surgery. Therefore, the present work uses the application of different machine learning models to predict cutting force and surface roughness induced during bone drilling. The predictive results of machine learning models were compared with statistical analysis. During the study, rotary ultrasonic drilling is performed on the pig femur bone. The surface roughness and cutting forces are monitored using a surface roughness tester and a dynamometer respectively. Predictive models were developed by drilling parameters such as spindle rotational speed, abrasive girt size, and feed rate. Various machine learning algorithms such as ridge regression, lasso regression, support vector regression, multi-linear regression, and artificial neural networks were leveraged and compared at different error metrics to provide a robust predictive model. It was observed that ridge regression has the least error metrics compared to other machine learning algorithms during surface roughness prediction. Moreover, the most accurate model for predicting cutting force was support vector regression. The error metrics for statistical analysis were comparatively higher than the machine learning algorithms. Therefore, machine-learning algorithms are preferable for adequate prediction of surface roughness and cutting force induced during bone drilling compared to statistical analysis.

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Influence of cutting force on temperature, microcracks and chip morphology during rotary ultrasonic bone drilling: An in-vitro study

Agarwal

Singh

Gupta

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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A novel drill bit design for reducing bone-chip morphology in orthopaedic bone drilling

Cited by 9 publications

References 28 publications

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

Prediction of surface roughness and cutting force induced during rotary ultrasonic bone drilling via statistical and machine learning algorithms

Influence of cutting force on temperature, microcracks and chip morphology during rotary ultrasonic bone drilling: An in-vitro study

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