## 1 Introduction  
Higher demands of manufacturing engineering processes are expected with the rapid development of an advanced manufacturing industry. In recent years, increasingly advanced materials, structures and equipment have enhanced the safety and reliability of manufacturing engineering processes. In addition, researchers from different disciplines, such as materials science, computer science, safety science, mechanical engineering and controlling engineering, have aimed to improve the quality and reliability of manufacturing engineering processes.  
However, the \"languages\" of different disciplines are varied. For instance, the gap between materials science and manufacturing engineering remains large, requiring that scientists and engineers attempt to solve this problem. The materials database is a bridge between materials science and manufacturing engineering, and such materials databases for different industrial fields have been established [1; 2; 3]. However, most databases are built only by scientists and engineers of materials science or manufacturing engineering. Therefore, how to plan and build a platform that can be widely applied to the manufacturing process has become a state-of-the-art issue during the past decade. In 2008, a new and promising engineering approach known as integrated computational materialsengineering (ICME) emerged [4]. Its goal was to enable the optimization of materials, manufacturing processes, and component design long before components are fabricated. After ICME, the concept of integrated computational materials and manufacturing engineering (ICM\({}^{2}\)E) was also proposed. Furthermore, in 2011, the materials genome initiative (MGI) was announced by President [PERSON]'s government in the United States of America. In general, these technologies make full use of data platforms and numerical simulation, providing new research directions for materials science and manufacturing engineering.  
In recent years, the new era of big data and Industry 4.0 for manufacturing engineer processes has arrived [5; 6]. Therefore, the full integration of various data, models, and knowledge in manufacturing engineering processes is very important for both theoretical research and engineering application. Some works now in development in commercial software inspired by Industry 4.0 are [7] and the linked work in [8], which both provide the whole approach and examples. Based on this, several works have applied advances, such as [9]. Other ideas are now in practice in Europe, including the well-known example of the Aeronautics Advanced Manufacturing Centre (CFAA) in Spain [10].  
In addition to building a unified integration platform, the corresponding analysis methods and models also play an important role in the manufacturing process. Due to the development of future Internet technology, more data can be obtained from the experimental system and the industrial system. How to use the data in the platform for modeling is an important challenge. [PERSON] systematically discussed the framework and progress of probabilistic machine learning and artificial intelligence in [11]. In the current paper, a multidisciplinary integrated platform framework based on probabilistic analysis for manufacturing engineering processes will be proposed. Key technologies of the proposed platform are also provided in this paper. The platform can be used to develop new models of probabilistic analysis of manufacturing engineering processes for scientists and engineers of multi-disciplines.  
This paper is organized as follows. Section 2 will summarize the requirements and the challenges of manufacturing engineering processes. The basic platform framework based on probabilistic analysis will be introduced in Section 3. Key technologies will be analyzed in Section 4. Section 5 will illustrate an application case. Finally, conclusions will be drawn at Section 6.