Interpreting learning models in manufacturing processes: Towards explainable AI methods to improve trust in classifier predictions

Goldman, Claudia V.; Baltaxe, Michael; Chakraborty, Debejyo; Arinez, Jorge; Escobar, Carlos A.

doi:10.1016/j.jii.2023.100439

Cited by 11 publications

(1 citation statement)

References 15 publications

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“…Firstly, the search criteria need to be formulated to suit the targeted business model (e.g., assembly-to-order, engineer-to-order, make-to-stock, MTO) [422], industry type, and information availability. Secondly, prior machine learning (ML) based methodologies portray supplier selection as a black box in a data intelligence manner, resulting in a need to manually verify potential options for explainability [433]. Thirdly, incomplete, inaccurate, and unavailable data leads to the derivation of nonoptimal decisions, causing missed opportunities and financial missteps.…”

Section: Overviewmentioning

confidence: 99%

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim

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In today's increasingly dynamic business landscape, industries are faced with global headwinds, intensified competition, and increased volatility driven by factors such as increased demand for tailored products and solutions as well as geopolitical, economic, and climate disruptions.To address these challenges and remain competitive, the product lifecycle management (PLM) paradigm, which allows companies to be in control of their products and services across the lifecycle, has gained attention from industries. This cross-functional approach, enabled by Industry 4.0 information and communication technology (ICT) enablers such as digital twins (DT) and knowledge graphs (KGs), can elevate existing capabilities for customizability and manufacturing resilience. This thesis explores the role of DT and KG in enhancing PLM capabilities, integrating multiple lifecycle phases with emphasis on product design, manufacturing, and supply chain management (SCM). The adoption of these transformative technologies has the potential to enhance existing mass customization strategies such as the product family (PF) approach and Smart Product-Service Systems (Smart PSS), paving the way for intelligent decision support systems. Applicable to a wide range of industries, the use of DT systems with KG as a computational driver can support stakeholders in improving operational efficiencies, and responsiveness for disruption management. The main contributions of the thesis can be categorized into five research areas highlighted below:1) Establishing an environment-based context-aware DT system to enhance PF design and optimization. To overcome the challenges of identifying optimal product configurations in the planning phase and overcoming user requirement changes in the usage phase, a context-aware Abstract 10 DT system incorporating real-world environment aspects is proposed to aid PF reconfiguration and redesign. A novel benchmark and interacting mechanism automatically identifies feasible PF modules, reducing the need for costly and bias-prone expert recommendations, as demonstrated by a case study on tower crane planning and deployment.2) Developing DT-as-a-Service to advance service-oriented digital manufacturing. Based on the flexibility and versatility of DT in manufacturing processes, these systems are pivotal towards customized production systems. A four-tier technology stack is proposed to construct DT systems using a modular approach and leverages advanced computational methodologies and tools such as extended reality (XR) to support a wider range of shop floor manufacturing processes. Here, DT systems are used as a fundamental to enable Smart PSS and circular economy paradigms to drive sustainability and customizability efforts. These multi-faceted capabilities are featured in two use cases, additive manufacturing, and gearbox assembly line. 3) Enabling causal inference for maintenance operations using cognitive digital twins (CDTs).The intricate nature of large production systems results in challenges pertaining to the root ca...

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Section: Overviewmentioning

confidence: 99%

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim

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Value co‐creation mechanism for AI‐based enterprises with systems thinking: Case study of Baidu

Su,

Du,

Chen

et al. 2024

Syst Res Behav Sci

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How to realize value co‐creation is a challenge for artificial intelligence (AI)‐based enterprises. From the perspective of systems thinking, this paper conducts a case study on Baidu to explore the mechanism for Ai‐based enterprises to realize value co‐creation. The results indicate that value co‐creation of AI companies goes through a dynamic iteration of three stages: start‐up, growth, and maturity. Based on the finding, this paper proposes a model for AI‐based enterprises to realize value co‐creation with stakeholders under the interaction of innovation resilience and value proposition, as well as the combined effect of digital‐intelligence empowerment, resource‐focused action, and network embedding. Findings in this study not only fill the gap in existing literature but also provide insights for AI‐based enterprises to realize sustainable development.

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Introduction to the Digital Twin of a Process Plant

Stjepandić,

Lützenberger,

Kremer

2024

Generation and Update of a Digital Twin in a Process Plant

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Interpreting learning models in manufacturing processes: Towards explainable AI methods to improve trust in classifier predictions

Cited by 11 publications

References 15 publications

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Value co‐creation mechanism for AI‐based enterprises with systems thinking: Case study of Baidu

Introduction to the Digital Twin of a Process Plant

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