Dataset for the development of a diagnostic schedule for a defective LC-195V5 CNC milling machine at FUTA central workshop

The dataset represented in this article describe a diagnostic schedule for a defective LC-195V5 CNC milling machine using PERT. The efficiency of the technicians who repaired the CNC machine tools was measured based on fault location within the shortest possible time. A diagnostic schedule was developed which showed the sequential means of troubleshooting within a possible shortest time. Two approaches were employed. Forward Pass (FP), which involved the diagnosis from electrical parts through Computer (CNC) to mechanical components and Backward Pass (BP) which involved the diagnosis from computer component through electrical parts to mechanical parts. Three different levels of expertise (trials) were used for each of the mode of diagnosis and the time to diagnose each component part was recorded. Two separate PERT network diagrams were drawn based on the inter-relationship of the component parts of the machine and their Critical Paths were determined.


a b s t r a c t
The dataset represented in this article describe a diagnostic schedule for a defective LC-195V5 CNC milling machine using PERT. The efficiency of the technicians who repaired the CNC machine tools was measured based on fault location within the shortest possible time. A diagnostic schedule was developed which showed the sequential means of troubleshooting within a possible shortest time. Two approaches were employed. Forward Pass (FP), which involved the diagnosis from electrical parts through Computer (CNC) to mechanical components and Backward Pass (BP) which involved the diagnosis from computer component through electrical parts to mechanical parts. Three different levels of expertise (trials) were used for each of the mode of diagnosis and the time to diagnose each component part was recorded. Two separate PERT network diagrams were drawn based on the inter-relationship of the component parts of the machine and their Critical Paths were determined. &

Value of the data
The dataset can be used to show the sequence of diagnosing a defective LC-195V5 milling machine and the time take to diagnose each component part.
The data can be used to evaluate the two possible routes of diagnosis through the setting of Experiments I and II.
The reported data can be useful for relating information on the relationship between sequence of diagnosis and time taken for the overall diagnosis of the machine as a whole.

Data
The dataset presented in this article are experimental results obtained from diagnosis of each component part of the CNC milling machine. The picture of the machine is as shown in Fig. 1 Tables 3 and 4. Analytical tables used in estimating the critical path are as displayed in Tables 5 and 6 for Experiment I and II, respectively.

Experimental design, materials, and methods
The defective LC-195V5 milling machine is owned by the Federal University of Technology Akure, Nigeria. Two routes or methods of diagnosing tagged Experiment I and Experiment II were used. In Experiment I, the diagnosing was performed from Electrical parts through computer parts to mechanical components. Experiment II on the other hand involves diagnosing from computer components through electrical components to mechanical parts. Diagnostic exercise followed the prescribed procedures [2].
Stopwatch was used to record the time taken to diagnose each component part. For reliability of data, three experts were used in diagnosing the parts and time spent by each one for both experiments were recorded as optimistic time "a", pessimistic time "b" and mostly likely time "m".
Average/expected time (T E ) was computed using Eq. (1) and standard deviation (σ) was obtained using Eq. (2) Signal generator was used for generating electronic signals (repeating and non-repeating signals); Oscilloscope was used to display and analyse waveform of electronic signals; multimeter was used measure voltage, current and resistance; line tester was used to test phase/live or positive conductor; RCL tester was used to simultaneously detect resistance, capacitance and inductance; while neon tester was for electrical testing.

Acknowledgements
Appreciation goes to the Federal University of Technology Akure (FUTA) for the permission to work on the CNC milling machine for obtaining the data used for this study and also the Landmark University (LMU) for their financial supports and for providing a conducive environment for research.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at https://doi.org/ 10.1016/j.dib.2018.10.160.