ORIGINAL ARTICLE Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding Mahmoud El-Banna & Dimitar Filev & Ratna Babu Chinnam Received: 30 May 2006 / Accepted: 3 October 2006 / Published online: 11 January 2007 # Springer-Verlag London Limited 2007 Abstract Real-time estimation of weld quality from pro- cess data is one of the key objectives in current weld control systems for resistance spot-welding processes. This task can be alleviated if the weld controller is equipped with a voltage sensor in the secondary circuit. Replacing the goal of quantifying the weld quality in terms of button size by the more modest objective of indirect estimation of the class of the weld, e.g., satisfactory (acceptable, “normal” button size), unsatisfactory (undersized, “cold” welds), and defects (“expulsion”), further improves the feasibility of the mission of indirect estimation of the weld quality. This paper proposes an algorithmic framework based on a linear vector quantization (LVQ) neural network for estimation of the button size class based on a small number of dynamic resistance patterns for cold, normal, and expulsion welds that are collected during the stabiliza- tion process. Nugget quality classification by using an LVQ network was tested on two types of controllers; medium- frequency direct current (MFDC) with constant current controller and alternating current (AC) with constant heat controller. In order to reduce the dimensionality of the input data vector, different sets of features are extracted from the dynamic resistance profile and are compared by using power of the test criteria. The results from all of these investigations are very promising and are reported here in detail. Keywords Learning vector quantization . Nugget quality classification . Spot welding 1 Introduction For several decades, resistance spot welding has been an important process in sheet metal fabrication. The automo- tive industry, for example, is dominated by spot welding, due to its simple and cheap operation. The advantages of spot welding are numerous and include the following: an economical process, adaptable to a wide variety of materials (including low-carbon steel, coated steels, stain- less steel, aluminum, nickel, titanium, and copper alloys) and thicknesses, a process with short cycle times, and a relatively robust process with some tolerance to fit-up variations. However, given the uncertainty associated with individual weld quality (attributed to factors such as tip wear, sheet metal surface debris, fluctuations in power supply, etc.), it is a common practice in the industry to add a significant number of redundant welds to gain confidence in the structural integrity of the welded assembly. In recent years, global competition for improved productivity and reduced non-value-added activity is forcing companies such as the automotive OEMs to eliminate these redundant spot welds. In order to minimize the number of spot welds and, yet, still satisfy essential properties such as strength, weld quality must be obtained. Traditionally, to check weld quality, destructive tests (the dominant method of inspection in the industry) and nondestructive tests are used on randomly sampled work Int J Adv Manuf Technol (2008) 36:237–248 DOI 10.1007/s00170-006-0835-5 M. El-Banna (*) Industrial Engineering Department, University of Jordan, Amman 11942, Jordan e-mail: m.albanna@ju.edu.jo D. Filev Ford Motor Company, Dearborn, MI 48121, USA e-mail: dfilev@ford.com R. B. Chinnam Department of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI 48201, USA e-mail: r_chinnam@wayne.edu