Manbir S. Sodhi

Classifying which operation a Computer Numerical Control (CNC) machine is executing, not just detecting whether it is functioning correctly, is a monitoring challenge that existing sensor-based studies rarely address. Unlike tool wear estimation, operation-type classification must resolve toolpath strategies and cutting conditions within heterogeneous, noisy sensor streams in which modalities differ widely in their discriminative value. Which sensors are genuinely necessary, and how many can be removed before performance degrades, directly informs retrofit cost and monitoring system design. We present a systematic cross-validated ablation study for a nine-class CNC toolpath and condition classification task, using 120 operation files collected from a desktop CNC mill instrumented with six distributed sensor units spanning inertial, acoustic, environmental, and electrical modalities. To handle multimodal fusion under sensor noise, we introduce the Multimodal Denoising Temporal Attention Encoder with Long Short-Term Memory (MM-DTAE-LSTM), which combines learned modality weighting, cross-modal attention, and a self-supervised denoising objective, followed by recurrent temporal modeling for classification. We evaluate MM-DTAE-LSTM against five baseline model families across five cumulative sensor-ablation levels and ten temporal resolutions, using file-level cross-validation to prevent data leakage from overlapping windows. MM-DTAE-LSTM maintains 92.5% classification accuracy when nearly half the sensor channels are removed (56 of 110 features), whereas simpler baselines degrade by up to 10.7 percentage points under the same reduction. Analysis of variance reveals that pressure channels encode session-level atmospheric variation rather than machining dynamics, exposing how models that cannot suppress uninformative modalities rely on environmental confounds rather than machining physics. Together, these findings translate into concrete sensor-selection and deployment recommendations for cost-effective CNC process monitoring at under USD 500 in hardware, though generalization to industrial machines, diverse materials, and production environments requires further validation.

Determining which sensor modalities carry genuine discriminative signal for CNC monitoring—and how many can be removed before performance degrades—is a practical question that prior work rarely answers quantitatively under leakage-resistant evaluation. We address this through a systematic cross-validated ablation study on a 9-class CNC toolpath and condition classification task, combining three toolpath strategies (adaptive, face, pocket) with three conditions (air-cutting, active-cutting, and damaged-spindle). Using 120 operation files from a desktop CNC mill with six consistently active sensors (17 channels per sensor) plus 8 machine-level electrical features, we evaluate six model families across 690 cross-validated runs spanning five cumulative feature-ablation levels (110–56 features) and ten temporal resolutions. To handle the fusion challenge, we introduce MM-DTAE-LSTM, a multi-modal denoising temporal attention encoder with unidirectional LSTM-based classification that combines learned modality gates, cross-modal attention, and a self-supervised denoising objective. Key findings on this single-machine, single-material dataset: (1) MM-DTAE-LSTM reaches 96.3% ± 4.7% accuracy at a 98-feature configuration (excluding proximity and pressure), leading all baselines by 3.1–5.2 points, though differences are not statistically significant at n=5 folds; (2) reducing the feature set by 49% (to accelerometer, gyroscope, temperature, RMS audio, and electrical channels) retains 92.5% ± 8.3% for the encoder while XGBoost drops to 84.4%, a loss of 10.7 points from its full-feature peak; (3) at full features, baselines are competitive (Random Forest: 95.6%, XGBoost: 95.1%); and (4) one-way ANOVA reveals that pressure channels encode session-level barometric confounds (F > 2,000) rather than machining dynamics, explaining baseline degradation when confound-prone channels are removed. These results suggest that core inertial, acoustic, and machine-level modalities may be sufficient for effective CNC operation classification on similar platforms, providing sensor-selection and temporal-configuration guidance for cost-effective monitoring deployments. Generalization to industrial machines, diverse materials, and production environments requires further validation.

In this paper the problem of scheduling to meet two performance criteria is considered. The first criteria relates to timeliness while the second relates to precedence (or technological) constraints. An ideal schedule is one that yeilds the best measure of timeliness without violating the precedence constraints. However in some systems, such as flexible manufacturing systems and real time scheduling systems, it is sometimes possible that all precedence constraints need not be met. Given deadlines by which all tasks must be completed, it may be desirable to perform an alternate set of tasks, perform tasks out of order or even omit some tasks given these deadlines. In this paper the case where it is permissible to violate some precedence constraints for a commensurate improvement in meeting timing constraints is considered. Thus, the precedence constraints are no longer inviolate constraints, but can be represented by another measure of performance. The problem of preparing task schedules is formulated such that a trade-off between timing and precedence constraints is made when both constraints cannot be met and procedures are developed to obtain solutions for arbitrary deadlines.

The Vehicle Routing Problem (VRP) is fundamental to logistics operations. Finding optimal solutions for VRPs related to large, real-world operations is computationally expensive. Genetic algorithms (GA) have been used to find good solutions for different types of VRPs but are slow to converge. This work utilizes high-performance computing (HPC) platforms to design a parallel GA (PGA) algorithm for solving large-scale VRP problems. The algorithm is implemented on an eight-GPU NVIDIA DGX-l server. Maximum parallelism is achieved by mapping all algorithm arrays into block threads to achieve high throughput and reduced latency for full GPU utilization. Tests with VRP benchmark problems of up to 20,000 nodes compare the algorithm performance (speed) with different GPU counts and a multi-CPU implementation. The developed algorithm provides the following improvements over CPU or single-GPU-based algorithms: (i) larger problem sizes up to 20,000 nodes are handled, (ii) execution time is reduced over the CPU by a factor of 1,700, and iii) for the range tested, the performance increases monotonically with the number of GPUs.