Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry

• URL: http://arxiv.org/abs/2008.01053v1
• Date: Fri, 24 Jul 2020 07:06:57 GMT
• Title: Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry
• Authors: Jannis Walk, Niklas K\"uhl and Jonathan Sch\"afer
• Abstract summary: We propose that end-of-life products have -- besides their value as recyclable assets -- additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools. We present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have -- besides their value as recyclable assets -- additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools to foster value co-creation between tool manufacturer and tool user (customer) in the future. In the work at hand, we present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping. The resulting Matthews Correlation Coefficient of 0.878 and 0.644 confirms the feasibility of our system based on the VGG-16 network and Gradient Boosting. Based on these first results we derive a research agenda which addresses the need for a more holistic tool characterization by semantic segmentation and assesses the perceived business impact and usability by different user groups.

Related papers

• Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model [0.16666049492586987]
  We propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator. The results consistently highlight our approach's advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets.
  arXiv  Detail & Related papers  (2024-07-01T11:57:53Z)
• EcoVal: An Efficient Data Valuation Framework for Machine Learning [11.685518953430554]
  Existing Shapley value based frameworks for data valuation in machine learning are computationally expensive. We introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner.
  arXiv  Detail & Related papers  (2024-02-14T16:21:47Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• Reliability in Semantic Segmentation: Can We Use Synthetic Data? [69.28268603137546]
  We show for the first time how synthetic data can be specifically generated to assess comprehensively the real-world reliability of semantic segmentation models. This synthetic data is employed to evaluate the robustness of pretrained segmenters. We demonstrate how our approach can be utilized to enhance the calibration and OOD detection capabilities of segmenters.
  arXiv  Detail & Related papers  (2023-12-14T18:56:07Z)
• Tool-Augmented Reward Modeling [58.381678612409]
  We propose a tool-augmented preference modeling approach, named Themis, to address limitations by empowering RMs with access to external environments. Our study delves into the integration of external tools into RMs, enabling them to interact with diverse external sources. In human evaluations, RLHF trained with Themis attains an average win rate of 32% when compared to baselines.
  arXiv  Detail & Related papers  (2023-10-02T09:47:40Z)
• LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department [85.1523466539595]
  This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company. Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be. Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
  arXiv  Detail & Related papers  (2023-04-18T15:35:43Z)
• Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision [0.0]
  This work shows how deep learning can be harnessed to increase sustainability in production and product usage. We utilize deep learning-based computer vision to determine the wear states of products. We demonstrate our approach on two products: machining tools and rotating X-ray anodes.
  arXiv  Detail & Related papers  (2023-03-17T16:01:39Z)
• Performance of different machine learning methods on activity recognition and pose estimation datasets [0.0]
  This paper employs both classical and ensemble approaches on rich pose estimation (OpenPose) and HAR datasets. The results show that overall, random forest yields the highest accuracy in classifying ADLs. Relatively all the models have excellent performance across both datasets, except for logistic regression and AdaBoost perform poorly in the HAR one.
  arXiv  Detail & Related papers  (2022-10-19T02:07:43Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Strategic Evaluation in Optimizing the Internal Supply Chain Using TOPSIS: Evidence In A Coil Winding Machine Manufacturer [1.6114012813668934]
  This study takes a critical look into the factors that affect the Performance of internal supply chain. The results of this indicate that supplier relationship and inventory planning were most principal factors positively influencing on-time delivery of the product.
  arXiv  Detail & Related papers  (2020-07-08T12:46:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.