Many industrial problems are inherently multi-objective, and require special attention to find different trade-off solutions. Typical multi-objective approaches calculate a scalarization of the different objectives and subsequently optimize the problem using a single-objective optimization method. Several scalarization techniques are known in the literature, and each has its own advantages and drawbacks. In this paper, we explore various of these scalarization techniques in the context of an industrial application, namely the engagement of a wet clutch using reinforcement learning. We analyse the approximate Pareto front obtainable by each technique, and discuss the causes of the differences observed. Finally, we show how a simple search algorithm can help explore the parameter space of the scalarization techniques, to efficiently identify possible trade-off solutions.