Digital Twin Development in Collaboration with Global Manufacturing Laboratories

The global manufacturing industry continues to pursue real-time responsiveness, autonomy, and automation, while increasingly demanding smart manufacturing environments that can be easily adopted and operated even by non-expert or less-skilled operators. In response to these industrial needs, the LAMM research team has been collaborating with leading international manufacturing laboratories to perform Digital Twin modeling based on operational and sensor data collected from manufacturing equipment. LAMM’s Digital Twin technology precisely replicates the behavior and processes of real physical systems within a virtual physics-based simulation environment, enabling process prediction, diagnostics, and optimization. By integrating online server systems and VR devices, users can remotely access mobile robots and manufacturing machinery in real production sites to monitor and control both equipment and process conditions in real time. Furthermore, LAMM aims to incorporate AI-driven analytical models capable of conducting quality assessment, process simulation, and anomaly detection for manufactured parts. Through these advancements, LAMM seeks to establish a user-centric, interactive manufacturing environment and to maximize human-machine collaboration, ultimately contributing to the realization of the next-generation autonomous manufacturing ecosystem.

Status Diagnosis of Battery Management System Using Sound Sensors

LAMM has developed a novel sound sensing system for manufacturing equipment inspired by the Stethoscope which is the bell structure used by physicians to diagnose the condition of the human heart and lungs. Unlike conventional microphone-based sound sensors, this technology allows for flexible installation with minimal constraints on sensor placement. As a result, it enables the effective acquisition of both normal and abnormal acoustic signatures from various components of manufacturing equipment, facilitating precise and real-time condition diagnostics. In recent years, growing attention has been directed toward the detection of thermal runaway and explosion phenomena in lithium-ion batteries, which are critical components in emerging mobility industries. LAMM has applied its Stethoscope-type sound sensing technology to Battery Management Systems (BMS) to enable predictive maintenance and health monitoring of battery systems. This research encompasses the entire spectrum from low-level circuit design to high-level anomaly detection and data interpretation, employing unsupervised AI classification models that have successfully achieved high-accuracy fault detection for batteries and equipment. The technology has garnered significant attention not only from leading global manufacturing and energy corporations, but also from major U.S.-based industrial partners, demonstrating its strong potential as a next-generation intelligent diagnostic solution for advanced manufacturing systems.

Digital Transformation of Legacy Industrial Models in the United States

Building upon the industrial applicability and scalability of its Stethoscope-based sound sensing technology, LAMM is actively exploring its potential applications not only in manufacturing processes but also across a wide range of industrial domains. This technology demonstrates exceptional durability and reliability even in harsh manufacturing environments such as those involving high temperatures, strong vibrations, or structural complexity where conventional sensing methods often fail. As a result, it is being recognized as an innovative monitoring solution that transcends the limitations of traditional sensing technologies. Furthermore, LAMM anticipates that sound sensing will play a critical role in the legacy-driven manufacturing sectors, where digital infrastructure and data accessibility remain limited. By integrating edge devices with diverse communication protocols, LAMM is spearheading a sound-based digital transformation (DX) initiative that enables real-time condition monitoring and predictive maintenance without requiring replacement of existing equipment. In collaboration with major manufacturing enterprises in the state of Indianapolis, LAMM has established a robust industrial network and developed a real-time sound data acquisition and analytics platform capable of handling terabyte-scale (TB-level) datasets. Leveraging this extensive data collection and analytical capability, LAMM ultimately aims to develop the world’s first Sound Foundation AI Model, positioning itself as a global leader in industrial sound intelligence and setting a new paradigm for intelligent manufacturing diagnostics and optimization.

Multimodal Sensing for Welding Quality in Shipbuilding Robotic Systems

The foundation of global logistics undeniably lies in large-scale ocean-faring vessels, as most of the international trade is conducted through maritime transportation. The construction of such ships still relies predominantly on arc welding technology, a process that demands precise fit-up (material positioning), extensive tack welding, and the expert craftsmanship of skilled welders to achieve high-quality joints. However, due to a growing shortage of skilled labor and the inherent hazards of shipyard environments, there is an increasing demand for automation and intelligent welding systems. In response, LAMM is developing a next-generation robotic welding system for shipbuilding that compensates for labor shortages, minimizes human error, and enables real-time monitoring and correction of welding quality. This system integrates sound sensing and vision-based sensing technologies into a multimodal sensing platform, capable of simultaneously capturing acoustic, thermal, and optical data during the welding process. The collected multimodal signals are analyzed through AI-based diagnostic and control algorithms to autonomously optimize welding parameters and ensure process stability. Serving as a testbed for multimodal sensing technologies, LAMM continues to advance its research in AI-driven diagnostics and process optimization. Through these innovations, LAMM aims to lead intelligent automation of ship manufacturing and to elevate the global standards of welding quality in the shipbuilding industry.

Collision Avoidance Algorithm and Motion Planning for Manipulation in Industrial Robots

Industrial robot arms and manipulators are among the core technologies of the Fourth Industrial Revolution, serving as powerful tools for automation, precision control, and productivity enhancement in modern manufacturing. However, several technical challenges remain unresolved. For successful deployment in real manufacturing environments, it is essential to develop a deep understanding of the robot’s kinematic behavior and its situational awareness within complex workspaces. Current robotic systems often lack sufficient capability for posture estimation and self-collision detection, requiring multiple external sensors and complex electrical signal control mechanisms to compensate for these limitations. LAMM focuses on the potential of a single vision sensor as a minimal yet highly capable perception unit. The research team utilizes a vision sensor mounted on the end-effector to enhance the robot’s ability to perform depth estimation and three-dimensional spatial perception of surrounding objects. Building on this foundation, LAMM is advancing a collision avoidance algorithm that allows the robot to autonomously recognize its working trajectory, predict potential hazards, and operate safely within dynamic environments. Ultimately, LAMM aims to establish an intelligent control framework enabling robots to achieve autonomous decision-making and spatial awareness through vision-only sensing. This approach seeks to pioneer the next generation of lightweight, intelligent, and highly adaptive manufacturing robots, setting new standards for smart automation in industrial environments.

Optical Fiber Sensor-Based Monitoring for Advanced Manufacturing Processes

Light is a fundamental medium and a core carrier of energy that constantly surrounds us. Recognizing its physical properties and the potential for precise control, LAMM has been conducting advanced research in laser-based optical systems and optical fiber–assisted sensing technologies for manufacturing applications. By precisely analyzing the characteristics and unique patterns of incident, reflected, and deflected light, LAMM has successfully developed a smart diagnostic tool capable of real-time process monitoring. This technology enables the detection of surface defects, reflectance variations, and impurity distributions in semiconductor-grade silicon wafers, thereby contributing to quality evaluation and process optimization in semiconductor fabrication. Furthermore, LAMM has extended its research to femtosecond laser–based micromachining, leveraging its ultrafast and high-precision capabilities for the fabrication of Through Glass Via (TGV) structures and groove patterning on glass substrates. These technologies provide high-precision glass processing solutions essential for next-generation display, sensor, and packaging industries. Beyond process monitoring, LAMM continues to strengthen its expertise in photonics-based precision manufacturing and sensing technologies, aiming to establish itself as a leading research institute in intelligent photonic manufacturing. Through this integrated approach, LAMM envisions advancing the frontier of light-driven manufacturing innovation.

Additive Manufacturing and Fabrication Research Using Cold Spray Processing

The Cold Spray (CS) manufacturing process, as its name implies, is a non-thermal, solid-state deposition technique that does not rely on high-temperature heat sources, making it a next-generation advanced manufacturing technology. In this process, sub-micron metallic powders are accelerated through a high-pressure supersonic jet stream and propelled toward a substrate, where they impact the surface at extremely high velocities. Upon impact, the resulting kinetic energy and localized plastic deformation enable strong metallurgical bonding between the powder particles and the bulk substrate, achieving high adhesion strength and material integrity without thermal distortion or oxidation. Compared to conventional melt-based fabrication techniques, Cold Spray offers superior energy efficiency, minimal material degradation, and enhanced microstructural control. Owing to these advantages, the technology has been successfully applied to surface coating, repair and restoration of damaged components, as well as micro-scale additive manufacturing, circuit patterning, and metallic plating for thermal and electrical energy generation. In recent years, Cold Spray has demonstrated remarkable potential in battery electrode formation, aerospace component manufacturing, and thermal management materials, where high reliability and durability are required. Building on this foundation, LAMM aims to lead the advancement of non-thermal additive manufacturing technologies, driving innovation in sustainable, high-performance material fabrication for future industrial applications.