In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.

Aim: This work aims to investigate the nature of waste being generated by automobile service stations (ASS) and to devise a microbial-based formulation for the treatment of ASS wastewater. Methods and Results:Analysis of soil and water samples from the vicinity of different ASS in and around the Pune city region (India) revealed the presence of significant amounts of many heavy metals including zinc (Zn) 13.8-175.44 mg kg −1 , nickel (Ni) 0.6-5.5 mg kg −1 and copper (Cu) 8.07-179.2 mg kg −1 as well as oil and grease (O&G).A consortium, consisting of selected members from the ASS soil bacterial isolates, was formulated. The selection of consortium members was based on their ability to degrade hydrocarbons, tolerate heavy metals, and produce biosurfactant and lipase.The developed microbial consortium was capable of reducing the concentration of Ni, manganese (Mn) and chromium (Cr) by 69.25%, 14.63% and 84.93%, respectively, and O&G by 71.8% in the aqueous medium under laboratory conditions. Conclusions:Wastewater and soil analysis confirmed the presence of a high amount of O&G and metals in and around ASS. The developed microbial consortium holds potential for the treatment of wastewater rich in O&G and heavy metals. Significance and Impact of the study:There is a dearth of scientific studies in India on the wastewater and polluted soils associated with ASS. This work reveals and confirms the hazardous nature of ASS and the need for the development and feasibility of microbial-based technology for the sustainable bioremediation of such sites.