# Industry 4.0 Technologies for Manufacturing Sustainability:
## 5 Discussion on Review Results
### Main Technologies of Industry 4.0 for Achieving Sustainability
#### 5.2.3 Artificial Intelligence and Machine Learning  
Artificial intelligence (AI) and machine learning (ML) are considered as fundamental bases for Industry 4.0 which helps to make industries more productive and autonomous [58]. AI is the combination of several technologies that allow machines and software to understand, sense, act and learn based on self-learning or augmented human activities [59]. Research on AI in industry can be categorized into four major areas: (1) predictive quality and maintenance; (2) generative design; (3) supply chain activities; (4) human-robot collaboration [58]. [PERSON] et al. [39] discussed the role of machine learning approaches in manufacturing sustainability. It was also found that ML approaches help to analyze the large amount of data generated from the sustainable manufacturing operations. The benefits of using ML are discussed in the three aspects of sustainability with some new research scopes for Industry 4.0 practices. The major benefits of using ML and AI approaches results in error reduction, cost reduction and revenue growth in manufacturing industries. In the initial efforts, [PERSON] et al. [58] discussed the roadmap for AI-enabled manufacturing systems for Industry 4.0 practices. In the study, the current state of AI technologies and eco-systems for manufacturing applications is discussed in the Industry 4.0 context. [PERSON] et al. [60] discussed the evolution of smart manufacturing from intelligent manufacturing with AI technologies. The comparison between intelligent manufacturing and smart manufacturing is discussed in detail. [PERSON] et al.; [PERSON] et al. [61; 62] discussed the applications, opportunities and challenges for AI and ML techniques in Industry 4.0. [PERSON] et al. [63] discussed the fault diagnosis with the help ML approaches inIndustry 4.0 due to machine degradation. A detailed overview of ML approaches was provided for manufacturing activities to minimize the errors and improve human-machine interaction. [PERSON] et al. [64] discussed opportunities of ML for predictive maintenance in Industry 4.0 by providing a comprehensive review on ML and smart manufacturing. It was found that ML and AI applications are the emerging research areas in sustainable smart manufacturing. [PERSON] et al. [65] proposed a predictive model for Industry 4.0 practices. A case for HVAC(heating, ventilation and air conditioning) is presented in which data generated from a HVAC is utilized to predict and analyze that system temperature range using ML algorithms. However, few studies reported about the applications of AI and ML technologies in zero defect manufacturing. [PERSON] et al. 2020, [66] presented the state of the art on zero defect manufacturing and discussed the research opportunities for machine learning and AI technologies. [PERSON] et al. 2018, [67] discussed about the application of machine learning and AI technologies in real-time quality prediction and monitoring of defects in micro-semiconductor manufacturing. [PERSON] et al., 2020, [68] proposed a zero defect manufacturing model for industries with an Industry 4.0 perspective. It was found that the proposed model helps to improve the sustainability, profitability, efficiency and manufacturing competitiveness. [PERSON] et al., 2018, [69] discussed the zero defect manufacturing strategies in the Industry 4.0 context for scrap reduction in multi-stage production system. [PERSON] et al., 2020, [70] discussed the opportunities for AI-based algorithms for flexible job shop re-scheduling in zero-defect manufacturing. The above literature reveals that AI and machine learning based technologies delivers the skill set and zero defect manufacturing opportunities for Industry 4.0 which will help to improve the sustainability and efficiency of manufacturing industries.