## 2 Literature Review and Research Gap  
Energy consumption is one of the key criteria in life-cycle assessment (LCA) studies. There is enough information about energy usage during the manufacturing phase of LCA studies, and various assumptions must be made. According to statistics, the manufacturing sector consumes 90% of the total energy consumption of industry. As the resources of energy on earth are limited and steadily decreasing, sustainable manufacturing is gaining more and more attention. Energy efficient manufacturing is an important part of sustainable manufacturing aimed at processing products with less energy and the minimum carbon footprint [22]. [PERSON] et al. [23] and [PERSON] et al. [24] worked to minimize effectively the total energy utilized in the transportation of automated storage and retrieval system (AS/RS). Renewable energy is predicted to play a leading role in order to solve the issues related to energy demand and the environment. The energy payback time, net energy ratio, and the CO2 emissions are calculated using methodologies based on LCA. [PERSON] [25] calculated the energy requirements and concerned CO2 emissions for the manufacturing of grid-connected system. [PERSON] et al. [26] have successfully analyzed the cost and life cycle of 100 MW, solar systems in the Gobi desert, Mongolia. GHG emissions are best controlled by the methodologies of carbon footprint (CFP) assessment. CFP is based on the life cycle concept and is used to evaluate the GHG emissions during  
Figure 1: Machining cost breakdown [21]. extraction, manufacturing, and transportation of goods and services throughout the life cycle [27; 28; 29]. [PERSON] et al. [30] and [PERSON] et al. [31] optimize the total cost and carbon footprint by designing the warehouse system of the production system. The literature has found that automation in manufacturing has a significant environmental impact due to the high consumption of electricity in machining [32]. [PERSON] and [PERSON]\(\ddot{u}\)ll\(\ddot{u}\)[33] used the Taguchi design method and concluded that MQL is better for machining operation in order to retain the surface quality of a cutting part. The automotive industry in Germany presented in a survey that cutting fluid comprises 7-17% of manufacturing costs, which was several times higher than the cost of cutting tools having a proportion of 2-4% [34]. [PERSON] and [PERSON] [35] analyzed the turning process using MQL and evaluated the performance of the machining process on the basis of force, the roughness of material, and temperature distribution. However, there is a need to consider MQL in production and manufacturing models for sustainable machining. The general production models consider perfect quality items with constant demand in a manufacturing system. However, in real situations, it is not possible to adhere to the ideal assumptions. Due to long-term usage and breakdown of equipment, in addition to human errors, no production system can produce perfect items [36]. For this reason, researchers and scientists have extended the models by considering imperfect production. [PERSON] et al. [37] derived a model for multi-stage production system considering defective items. Similarly, [PERSON]\(\acute{a}\)rdenas-Barr\(\acute{o}\)n et al. [38] developed an EMQ model for imperfect production associated with rework and multiple shipment strategies. [PERSON] et al. [39] derived an economic manufacturing quantity (EMQ) model under the effects of reliability and inflation. [PERSON] and [PERSON] [40] presented an EPQ model considering inflation in an imperfect production system to maximize the profit function of production. [PERSON] and [PERSON] [41] worked on lean production in a manufacturing system by considering a random defect rate. Researchers also made contributions to develop different mathematical models considered energy utilization and carbon footprint using numerous analytical techniques. [PERSON] et al.