AI Powered Human-Robot Collaboration for Production Systems

Give a description for the topic investigated and typical industrial applications. Present a problem description and investigative statement for this course project.

A trending artificial intelligence (AI) technology in machine learning (ML) methods have shown to work effectively in the field of process monitoring (Amini & Chang, 2018).

Figure 1: Title (Source: )

multi-layer classification process monitoring diagram (Amini et al. 2019)

Table 2: Title (Source: )

Properties of the material (Ref.)

Properties Temperature, (°C)

20 100 200 300 400 500 600 700
Thermal capacity, C (J/kg °C) 611 624 653 674 691 703 710 712
Thermal conductivity, k (W/m °C) 6.8 7.4 8.7 9.8 10.3 11.8 12.8 13.5
Density, r (g/cm3) 4.44

Add MATLAB Code and Result

Literature Review
Classify and briefly introduce reviewed papers that are studying different aspects of this automation and AI enabled manufacturing topic. Summarize key findings in the papers and describe if any modeling is done with type of modeling (machine learning, regression, reinforcement learning etc.) discuss the relationship between the model and experimental results (if any given).

Materials and Methods
Describe the materials and methods used in the course project. For example, you may include experimental data extracted from papers review and a method to use this data to conduct this study.

Results and Discussion
In this section, present the findings of your course project and related investigations. You may generate additional tables, figures, graphs and plots to present your results in various forms. You must create a discussion section to evaluate your results against published literature reviewed in this course project. (For example, the results of the analysis conducted are in agreement with the paper by such and such…)

Conclusions
Present bulleted specific conclusions about this topic by using the results of your investigations.

This course project explores a cyber-manufacturing and AI framework of additive manufacturing systems.
This framework outlined has potential applications for the other multiple-stage production and manufacturing systems such as semiconductor manufacturing.