Introduction
In construction engineering, it is essential to approach the issues of creating a sample of the required size with complete accuracy and mechanics. Failure to follow the rules and guidelines results in the manufacturer receiving a product with variable characteristics at the output. This situation is unacceptable for the manufacturer and for customers who want to buy construction material of a given size. To regulate this process, it is necessary to develop an experiment, the result of which would become the benchmark for engineering works.
Experiment Development
It is impossible to start designing an experiment without first understanding the purpose and hypotheses. According to Helmenstine (2018), the primary task is active observation. Thus, with the help of visual control, professional engineers should consider all the variables that potentially determine the thickness of the laminates. According to the data given, there are four such variables: the soaking time and temperature, as well as the knife pressure and its setting. These are independent variables, which are primary in themselves — from their change, there is a variation of the final result, thickness. (Thomas, 2020). Experimenters need to consider these variables separately and try to modify their values to determine the extent to which they influence the final result (Thomas, 2020). Among the four variables, blade pressure is the easiest to change: it is assumed that blades can be set to the force with which they affect the raw material. Consequently, the first line of experimentation will be based on fixing the blade pressure to achieve the desired thickness parameter. This choice is based on the expectation that the harder the knife pressure on the material, the thinner the final plates will be. By setting the pressure to a specific, standard value, the experimenters start to vary the variables — soaking time, water temperature, and knife set. The final scheme of the experiment can look as follows:
Table 1. Three experimental baselines for determining the nature of the influencing factor. In total, more than twenty repetitions are conducted to determine the optimum parameters.
Result Repeatability
In the case of an experiment, it is important to ensure the reliability and credibility of the results obtained, at a minimum, by checking their reproducibility. This ensures that, regardless of the manufacturing method, location, or expertise of the specialists, if all conditions are met, the thickness of the plates will prove to be the reference (MacKenzie, 2019). This approach is incredibly important because customers will be able to receive continuous, one-size building materials and will not have any problems using the purchase.
For the proposed experiment, reproducibility can be guaranteed in two ways — by repeating the test and experimenting in parallel. First of all, the experiment’s line can be repeated sometime after the first series is completed. For example, having fixed three variables and changing the soaking time, it is possible to determine the necessary degree. After a few hours or days, one can repeat the same experiment to determine how significant the result was.
On the contrary, one can conduct parallel experiments to measure the degree of influence of one/a a few variables. For example, if the technical equipment allows this, it is possible to soak one large fragment of the raw material at a set temperature and then cut it partially with a knife. In other words, on one material, three or five sections are worked on at once, each of which presents thickness data. To save time, four stones can be soaked simultaneously at different temperatures. Meanwhile, the human factor plays a vital role in the organization of a standardized experiment. In particular, it is essential to ensure that there is no subjectivity of data or negligence on the part of engineers when conducting a series of experiments. For this purpose, it would be appropriate to invite two or three professionals to monitor or measure the results’ quality through statistical processing.
References
Helmenstine, A. M. (2018). What is an experiment? Definition and design. ThoughtCo. Web.
MacKenzie, R. J. (2019). Repeatability vs. reproducibility. Technology Networks. Web.
Thomas, L. (2020). Independent and dependent variables. Scribbr. Web.