The increase in the technical and functional requirements of plastic items has led the development of multimaterials. To achieve a good performance in the adhesion of dissimilar materials, in this case, polypropylene and coconut fiber composites, it is fundamental to understand the phenomena related to the specific interface. This work aims to evaluate the adhesion force of different multi-materials, namely between overmolded polymer-coconut composites using the digital image correlation (DIC) technique. First, coconut fiber composites were prepared using three different coupling agents, then they were overmolded with polypropylene. The results show that coupling agents have a positive influence increasing the mechanical properties of such multi-materials under tensile stress. The Lagrange vectors obtained by DIC analysis identified higher stress concentration at the joint edges, where the occurrence of surface cracks led the samples to fail in the region close to the overlap.

The application of high performance steels in combination with rolled profiles, hydro-formed or tailored tubes (TT) can create a stiffer and safer vehicle structure. However, it presents some challenges regarding the joining process, once accessibility to both sides of the surface to be joined is limited. The Single-Sided Spot Welding (SSSW) is an alternative to Resistance Spot Welding (RSW), as it only requires access to just one side of the joint to weld it. In order to validate SSSW as a robust and reliable welding process for automotive manufacturing, this work encompassed experiments comparing SSSW and RSW under manufacturing conditions and tolerance problems, such as sheets and electrode misalignment, gaps or shunt current conditions, using DX54D steel sheets, and steel profiles made of press-hardened 22MnB5 and S355J2 + N. The experimental parameters in this study were current, electrode force, geometry and misalignment angle. The performed tests were lap shear tensile strength, analyses of weld nugget quality and the measurement of spot diameters by chisel testing. The obtained results show that SSSW can meet RSW requirements regarding mechanical properties and quality standards, overcoming the set manufacturing tolerance issues, without reducing the spot diameter and with similar mechanical properties of RSW joints.

Application of the resistance spot welding (RSW) process is more predominant in the automotive industry in a way that approximately 3000-6000 weld spots were to be made to fabricate a single car [¹ 1 Dhawale P, Kulkarni M. Electric resistance spot welding: a state of art. International Journal of Engineering Research & Technology. 2017;5(1):1-6. ,² 2 Zhang X, Chen G, Zhang Y, Lai X. Improvement of resistance spot weldability for dual-phase (DP600) steels using servo gun. Journal of Materials Processing Technology. 2009;209(5):2671-2675. http://dx.doi.org/10.1016/j.jmatprotec.2008.06.008. http://dx.doi.org/10.1016/j.jmatprotec.2... ]. RSW is a process of joining two or more metal sheets by passing a focused electric current at a weld spot. A high electrical resistance created due to the concentrated electric current between the metal sheets, creates a weld spot [³ 3 British Standards Institution. Welding and allied processes: definitions of metal welding processes. London: BSI; 2004. ]. Nevertheless, the weld defects are more often created during RSW due to the inappropriate welding parameters and the type of weld materials due to the effect of its alloying elements (i.e.: its protective Zn layer) [⁴ 4 Kong J-P, Kang C-Y. Effect of alloying elements on expulsion in electric resistance spot welding of advanced high strength steels. Science and Technology of Welding and Joining. 2016;21(1):32-42. http://dx.doi.org/10.1179/1362171815Y.0000000057. http://dx.doi.org/10.1179/1362171815Y.00... ]. Especially, advanced high strength steels (AHSS) steels are coated with zinc on its surface for corrosion protection, which infiltrates into grain boundaries at critical process conditions, causes liquid metal embrittlement (LME) cracks. There are several factors which could influence the cause of weld defects like material related parameters (e.g., sheet thickness, steel grade, type of coating), process related parameters (e.g., welding current, welding time, electrode force) and electrode parameters (e.g., electrode diameter, shape). To guarantee a proper welding window, free of defects and discontinuities, a suitable range of welding parameters must be validated. It needs a deep understanding of the theory of the process, properties of materials and mechanisms of the defect formation. Therefore, to overcome this issue, most of the studies developed weld lobe curves for a robust welding process. The weld lobe curve shows the boundaries of certain working range of parameters for defect-free welding [⁵ 5 Bhattacharya D. Liquid metal embrittlement during resistance spot welding of Zn-coated high-strength steels. Materials Science and Technology. 2018;34(15):1809-1829. http://dx.doi.org/10.1080/02670836.2018.1461595. http://dx.doi.org/10.1080/02670836.2018.... ]. Last few decades, an advance in the artificial intelligence and the machine learning plays an important role in the automation of manufacturing process. Especially, in terms of recording the data of the process and its parameters at each moment generates a huge data from the machines that helps creating a better understand of the respective welding process [⁶ 6 Zhou B, Svetashova Y, Byeon S, Pychynski T, Mikut R, Kharlamov E. Predicting quality of automated welding with machine learning and semantics: a Bosch case study. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management; 2020; Ireland. New York: Association for Computing Machinery; 2020. http://dx.doi.org/10.1145/3340531.3412737. http://dx.doi.org/10.1145/3340531.341273... ]. Computing the historical data from the machines with the help of machine learning can help obtaining information about the relationship between input and its corresponding outputs [⁷ 7 Wuest T, Weimer D, Irgens C, Thoben K-D. Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research. 2016;4(1):23-45. http://dx.doi.org/10.1080/21693277.2016.1192517. http://dx.doi.org/10.1080/21693277.2016.... ]. Machine learning (ML) is a subfield of artificial intelligence that helps to predict or identify the useful information from the available data collected from the machine during a certain process. Using a different mathematical algorithm, a machine learning could develop a model using empirical machine data to analyze the pattern of the process. Compared to weld lobe curve, the ML model helps to predict the output of the process for a large range of parameters without utilizing many resources and time in the industry, which is useful for decision-making purposes [⁸ 8 Hu F, Hao Q, editors. Intelligent sensor networks. New York: Auerbach Publications; 2008. ]. However, implementing the machine learning methods in the manufacturing process, poses several challenges in terms of data quality and process complexity.