This paper examines the role of generative AI and large language models (LLMs) in advancing intelligent manufacturing as we transition from Industry 4.0 to Industry 5.0. We begin by analyzing the current limitations of rule-based and manufacturing data systems in facilitating flexible, human-centric production. Next, we categorize LLM utilization strategies into three methodological axes: fine-tuning domain-specific models, employing general-purpose models through prompt engineering, and utilizing retrieval-augmented generation (RAG), which includes multimodal RAG that integrates sensor and text data. For each strategy, we present representative case studies across key application areas such as asset management, maintenance intelligence, quality control, process optimization, and knowledge- and document-centric support systems. Concurrently, we explore how information modeling and ontology-based knowledge graphs can be integrated with LLMs to enhance structured manufacturing semantics, improve source traceability, and minimize hallucinations. Finally, we summarize the advantages and limitations of each approach and propose future research directions for human-centric manufacturing, including the development of trustworthy LLM pipelines, standardized data schemas, and closer integration between digital twins and LLM-based decision support systems.

To date, biomedical application of three-dimensional (3D) printing technology remains one of the most important research topics and business targets. A wide range of approaches have been attempted using various 3D printing systems with general materials and specific biomaterials. In this review, we provide a brief overview of the biomedical applications using 3D printing techniques, such as surgical tool, medical device, prosthesis, and tissue engineering scaffold. Compared to the other applications of 3D printed products, the scaffold fabrication should be performed with careful selection of bio-functional materials. In particular, we describe how the biomaterials can be processed into 3D printed scaffold and applied to tissue engineering area.