Papers for

manufacturing software developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Asset administration shells compared and assessed for manufacturing use

A Set-Theoretic Evaluation Framework for Assessing Asset Administration Shell Instances: Towards Comparability and Suitability

Abstract: Asset Administration Shells (AAS) provide a standardized means of representing assets and their information in manufacturing and increasingly serve as a basis for software services. However, different AAS instances vary in structure, content, and degree of completion, making it difficult to determine whether a given AAS is suitable for a specific application. This paper presents two complementary methods to support the comparison and application-oriented assessment of AAS. First, set-theoretic operations are employed to compare AAS models, enabling the identification of common, missing, and differing submodels and parameters. Second, an AAS suitability model assesses the conformity of an AAS to the requirements of a specific use case. The assessment considers structural conformity, semantic consistency, cardinality, and specification conformity and can be performed either against a reference AAS or a set of required SemanticIDs. A suitability value is derived from the identified deviations and is complemented by a detailed report of missing or non-conforming information. The proposed approach support practitioners and researchers in the comparison of evolving AAS and provide application-specific information on their suitability for manufacturing software services.

Tue 15 SeptSoftware Engineering
The gist
In manufacturing, Asset Administration Shells (AAS) store information about machines and parts in a standard way, but different AAS versions can look very different and have missing info. To help decide if an AAS is right for a job, the authors developed two methods: one to compare different AAS versions by looking for what matches or is missing, and another to check how well an AAS fits a specific use case by looking at structure, meaning, and completeness. This makes it easier for companies to choose or improve AAS for software that runs manufacturing processes.
Open → 2609.17062v1

AI agent improves industrial process control model building

little m: An AI Agent for Industrial Process Optimization

Abstract: Manufacturing consumes one third of global energy and still has significant room for improvement in terms of energy efficiency. Optimal process control is essential for this purpose. However, synthesizing mathematical optimization models from messy, real-world industrial specifications requires bridging unstructured natural language and spatial diagrams with rigorous mathematical syntax. This poses a profound challenge for general-purpose Large Language Models (LLMs), which may introduce invalid constraints when tasked with modeling continuous multi-physics dynamics. To address this, we introduce little m, an AI agent designed to assist the formulation of industrial process control models. Combining a domain-specific knowledge repository with LLM-driven interaction, the proposed framework formulates real-world optimization problems as mathematical models. For systematic evaluation, we introduce the Industrial Process Control Benchmark (IPC-Bench), a novel multimodal dataset of 50 canonical scenarios requiring joint reasoning over text and process diagrams. Through comprehensive automated structural assessments and double-blind human evaluation, little m substantially outperforms state-of-the-art LLMs, generating semantically correct models. These evaluations assess formulation quality rather than solver feasibility, formal physical validity, or closed-loop industrial performance. The implementation of little m and the IPC-Bench dataset are available at https://github.com/yeyongchao/process-modeling-benchmark.

Tue 15 SeptArtificial Intelligence
The gist
Industrial manufacturing uses a lot of energy and can get better at saving it by controlling processes in an optimal way. The authors created an AI system called little m that helps turn messy instructions and diagrams into proper math models needed for optimizing these processes. They tested little m on a new set of 50 real-world examples that mix text and diagrams and found it performed better than current language models at making correct models. This work focuses on creating good formulations rather than testing actual physical operation or solver performance.
Open → 2609.16680v1