Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring

Lutz, Benjamin; Kißkalt, Dominik; Regulin, Daniel; Reisch, Raven T.; Schiffler, Andreas; Franke, Jörg

doi:10.1109/icmla.2019.00321

Cited by 18 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the commands issued to the machine, especially the unplanned interactions of the machine's operator, are stored. After each experiment, the condition of the cutting tool insert, expressed as flank wear width, is measured with the procedure described in Lutz et al (2019). A unique identifier, batch A-E, is assigned to each investigated material batch.…”

Section: Cutting Experimentsmentioning

confidence: 99%

In-situ identification of material batches using machine learning for machining operations

et al. 2020

Self Cite

View full text Add to dashboard Cite

In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

show abstract

Section: Cutting Experimentsmentioning

confidence: 99%

In-situ identification of material batches using machine learning for machining operations

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Throughout operation, the used cutting tools experience wear and are exchanged once the operator judges that they reach their end-of-life, with the exchange times being recorded by the operator. The wornout cutting tool inserts are evaluated using a tool maker's microscope (see Figure 3) and the image evaluation procedure proposed in [25]. Thereby, ground-truth data about the actual tool condition are generated.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the absolute value of tool condition is investigated as well. Tool wear was measured at the insert radius with the image evaluation procedure [25], yielding the average flank wear width. These values thus represent the ground truth of the wear at the end of each tool's lifetime and are represented as circles in Figure 7.…”

Section: Tool Condition Assessmentmentioning

confidence: 99%

Towards Material-Batch-Aware Tool Condition Monitoring

Lutz

Howell

Regulin

et al. 2021

JMMP

Self Cite

View full text Add to dashboard Cite

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

show abstract

“…The results indicated an average recognition precision rate of 96.20% in tool wear classification (flank wear, tool breakage, adhesive wear and rake face wear). Other applications are required to stop the machining process for the tool wear measurement [32,104], with significant limits in terms of time waste. Despite the high model accuracy and precision of direct methods, the request to stop the MT generates several issues in production.…”

Section: Tool Condition Monitoringmentioning

confidence: 99%

“…Moreover, it allows the implementation of simpler ML methods, such as k-nearest neighbors (k-NN) or simple ANNs or a Decision Tree (DT). Beside this application, the actual use of DL algorithms in machining in the substitution of ML reduces the number of the required steps, since feature extraction and selection (4 to 5) is typically done by the same model [31,32]. These steps may improve the final results of the DL applications, as highlighted in References [33,34].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

View full text Add to dashboard Cite

This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

show abstract

Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring

Cited by 18 publications

References 22 publications

In-situ identification of material batches using machine learning for machining operations

In-situ identification of material batches using machine learning for machining operations

Towards Material-Batch-Aware Tool Condition Monitoring

Recent Advances on Machine Learning Applications in Machining Processes

Contact Info

Product

Resources

About