Every automotive company is using ISO/TS 16949 standard for automotive industry. According to this standard of Process failure mode and effect analysis (PFMEA), is obligatory. Also, the application of lean in automotive industry is a trend nowadays. Both, PFMEA and lean have the same main purposeidentification, prevention, and correction of failures during the production process. But, PFMEA have many shortcomings. In this paper, an integrated lean approach to PFMEA for solving specific shortcomings, is presented. This approach is new and it has not been used until now. Lean approach (tools and principles), were integrated in PFMEA. The new approach to solving PFMEA was presented in algorithm form. Some of those lean tools and principles integrated in PFMEA are: Genchi Genbutsu, Kaizen, standardized work, Jidoka, and 5 why. The approach presented was realized in a case study from automotive industry where traditional approach to PFMEA was compared to the new lean approach integrated to PFMEA. Changed and improved conditions were: number of team members, the actions taken, identification of failures, change of Severity (S), Occurrence (O), detection (D) and risk priority number (RPN) values, reduced S, O, D, and RPN values after taken actions, RPN with value over 100, and S, O, D indexes with value over 8.

Cylindrical wire electrical discharge turning (CWEDT) is a special form of wire electrical discharge machining (WEDM) process, which uses submerged rotation spindle as a clamping device for workpiece rotation in order to produce cylindrical parts. This study aims at determining influence on material removal rate (MRR) of CWEDT as an objective function. In the preliminary experiments, the widely used X5CrNi18-10 (DIN) and hard machinable S390PM (DIN) were used. The results of preliminary experiments showed that the type of steel is not the factor that has a significant influence on MRR. Pulse maximum current, pulse pause time, rotation speed, length of discharge area and cutting radius were used in MRR mathematical modelling by neural network programming. The results of the study exhibit that among the machining parameters, the pulse maximum current has the strongest influence on MRR. When the pulse maximum current increases, MRR increases as well. The discharge area length has an influence on MRR only on higher pulse maximum current values, and by the increase of the discharge area length, the MRR also increases. The derived mathematical model for MRR, which was finally validated and tested, enables calculation of complex cylindrical part production machining time for the given experimental set-up condition.

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Additive manufacturing is a technology of making a three-dimesional solid object of any shape from a digital model. Today on the global market exist various additive manufacturing processes. All of these processes build parts by applying material layer by layer. In a wide range of different processes there is a problem of selecting an adequate process for a user or company interested in additive manufacturing technology. Solving of such a problem is possible by using multicriteria decision methods which result in ranking of alternatives. Thus the user or company can easily select one of the available additive manufacturing processes. In this paper basic methodology of application of three different multicriteria decision methods in solving the mentioned problem was shown. These methods are: Analytic hierarchy process (AHP), Fuzzy AHP and Preference ranking organization method (PROMETHEE). Available alternatives are processes: 3D printing, Fused Deposition Modeling, Selective Laser Sintering and Photopolymer Jetting.

Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.