Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Awasthi, Utsav; Wang, Zhigang; Mannan, Nasir; Pattipati, Krishna R.; Bollas, George M.

doi:10.1016/j.jmapro.2022.06.027

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…Table 1 shows the nomenclature of the terms used in the model. Table 2 summarizes the model in Equations ( 1)-( 13), while a detailed explanation of the model can be found in Schmitz and Smith [33] and Awasthi et al [34] Cutting takes place when the tool engages with the workpiece and removes its material. During milling, a net cutting force acts on the workpiece via the tooth/ teeth involved in cutting, as shown in Equation (10).…”

Section: Face Milling Modelmentioning

confidence: 99%

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Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Wilson

Shen

Awasthi

et al. 2022

J Adv Manuf & Process

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With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost-efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi-objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi-objective formulation. The optimization framework is tested on a high-fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to 15% as compared to the default control scheme.

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Section: Face Milling Modelmentioning

confidence: 99%

“…Table 1 shows the nomenclature of the terms used in the model. Table 2 summarizes the model in Equations (1)–(13), while a detailed explanation of the model can be found in Schmitz and Smith [ 33 ] and Awasthi et al [ 34 ]…”

Section: Face Milling Modelmentioning

confidence: 99%

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Wilson

Shen

Awasthi

et al. 2022

J Adv Manuf & Process

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“…The processing times of the parts on each operation, randomly selected in References [1,10] are given in F I G U R E 8 Tool wear data [48] D ¼ ð20, 20,24,30,32,30,30,26,35,50,36,38,40,42,42,45,48,50, 60,54Þ:…”

Section: Experiments Setupmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

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In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

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“…The first approach involves traditional health evaluation, primarily reliant on expert knowledge or the establishment of physical models grounded in domain expertise [6]. This method is comprehensible through a physical perspective and enjoys the advantage of leveraging lucid physical theories [7,8]. Nonetheless, this technique mandates specialised insights from researchers within the field, and the quality of modelling remarkably governs the evaluation accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

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Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Cited by 17 publications

References 26 publications

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

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