# A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
## 1 Introduction  
The home meal replacement (HMR) industry has experienced significant growth in recent years, driven by changing consumer lifestyles and increasing demand for convenience due to time constraints, lack of cooking skills, and increasing number of single households [1]. The rise of e-commerce and direct-to-consumer meal solutions has further accelerated the adoption of HMR products, particularly meal kits [2]. The meal kits refer to providing pre-portioned ingredients and recipes for home cooking to enable the consumer to easily prepare the meal at home [2]. The growing demand for meal kit products has resulted in a rising need for efficient meal kit production systems. However, the products typically consist of a diverse range of ingredients, including fresh produce, proteins, sauces, and dry goods, which vary in size, shape, and packaging requirements. This complexity makes full automation challenging, as it requires sophisticated handling mechanisms and adaptive systems. Consequently, meal kit assembly is still labor-intensive, relying heavily on manual classification and packaging, which increases operational costs and limits scalability. Robot systems of food manufacturing have emerged as a substitute for the manual labor that was previously required. The systems are anticipated to play a critical role in enhancing the efficiency and consistency of meal kit assembly lines. Robots are capable of precisely handling, sorting, and transporting meal kit components, thereby ensuring accuracy in portioning and minimizing human error. However, food processing remains a relatively minor application, constituting only approximately 5% of total robotic usage in contrast to welding (25%) and assembly (33%) operations in other industrial areas [3,4]. The implementation of robotic automation in food manufacturing processes presents significant challenges due to various constraints in addition to product variability [5]. One of the primary obstacles is the substantial initial investment required for robotic systems, including hardware, software, and infrastructure modifications. Additionally, the food industry, particularly meal kit production, often requires small-quantity batch production, necessitating highly flexible and adaptive automation solutions. Therefore, the successful implementation of such robotic systems necessitates extensive simulation and validation to optimize their performance prior to deployment in real-world production environments. Simulation is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing [6]. It has emerged as an effective and flexible approach for decision analytics and support in complex manufacturing environments [7]. This approach supports real-time decision-making, proactively prevents potential issues, optimizes resource allocation, and significantly reduces operational costs [8]. As a result, it enhances efficiency, ensures smoother production processes, and fosters continuous improvement within flexible manufacturing systems. These benefits ultimately contribute to higher productivity and competitiveness in a rapidly evolving industrial landscape [9]. Moreover, the simulation software for production process modeling is invaluable in investment decision-making. It allows companies to assess production capacities in a virtual environment, evaluate their utilization, and, consequently, make better-informed investment decisions. Several studies have reported that simulation results lead to the optimization of the robot's position and gripper structure, improving the workstation design and offering potential for simulating other industrial robot production lines. [PERSON] et al. [10] optimized an automated CAN packaging system using robot simulation tools like DELMIA V6 and MATLAB 2022b fuzzy logic. [PERSON] et al.