## 5 Discussion and Future Challenges  
Through the AI and optimization applications of the SG in the manufacturing industry, the related technologies, resources, and infrastructure have driven manufacturers to move towards low energy consumption, net-zero carbon emissions, and sustainability. However, even if the latest AI technologies are applied, one of the key success factors is dependent on whether the cross-domain knowledge [105] and the knowledge base are available. In addition, problems such as the lack of AI talents, lack of more efficient data analysis technologies, and less development of algorithm applicability still exist, and they even have an impact on human resource management [106]. It would also be worth discussing how to customize the smart manufacturing processes of different products under different \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Problem** & **Method** & **Result** & **Reference** \\ \hline The factory consumed a lot of energy and could not achieve the goal of green manufacturing. & Efficient energy usage scheduling of SGs with dynamic mechanisms Smart meters replaced conventional electricity meters. Stability index & The energy efficiency equaled 129\%, and the electricity cost saving equaled 28\%. & [2; 89] \\ Power voltage instability affected manufacturing and generated high carbon emissions. & Smart meters replaced conventional electricity meters. Stability index with smart meters was implemented to predict voltage in SG. & Achieving overload current protection to maintain effective manufacturing and have 30 min early to take action to avoid blackouts. & [13; 22] \\ High electricity prices increased production costs. & Integrated smart grid solution was implemented. & The manufacturing efficiency increased by 84\%. & [85] \\ Unstable power quality affected client operations & The AI technology was applied to improve power supply quality. & The total harmonic distortion for electricity was under 2.8\%. & [66] \\ Production line equipment consumed too much energy and had inefficient energy scheduling. & A1 multi-agent deep deterministic method was proposed for equipment scheduling and manufacturing scheduling model with SG. & The electricity cost equaled 90.92\%, and the energy cost saving equaled 66–68\%. & [83; 88] \\ How to optimize manufacturing systems to comply with Industry 4.0 & Optimization of the digital twin for SG energy distribution & The average power saving was 18.6\%. & [96] \\ Low reliability of SG affected manufacturing. & Big data communication and data exchange power prediction model & The accuracy rate for predicting energy usage was 96\%. & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of AI and SG applications in smart manufacturing. process conditions and requirements, and how to further improve them in a project-based manner to achieve the optimization goal. On future challenges, with the advance in AI technologies and emerging energy sources, the SG systems will continue to transform, and smart manufacturing will have more highly automated production processes. Therefore, this survey proposes several future challenges along this line of research as follows:  
* Integration of the SG system with renewable energies: As environmental sustainability issues have received increasing attention, more and more renewable energies will be integrated into the SG system in the future [107] so that the system will become more complex, especially when supply and demand of the SC are intermittent, thus, supporting demand-side management in industrial environments becomes key to grid stability and flexibility [108; 109]. One of the future challenges is to investigate how to make this complex integrated system more stable and provide manufacturing more efficiently.