Last updated on April 9th, 2024

Case Study

Reducing rework in axle manufacturing


  • Find the root cause of backlash issues in axle assemblies
  • Reduce failure and rework rates overall


  • Axle assemblies have over 200 signals across 20 operations
  • Multiple causes contributed to the backlash problems

Key Results


Reduction in failure and rework rates

Significant improvements to first time through yield and cost savings

Background: need to reduce rework

A major Tier-1 supplier wanted to leverage machine learning techniques on assembly data to reduce the rate of product fallout and rework for axle assemblies. A company-wide search for advanced quality tools resulted in an engagement with Acerta, the only SaaS provider that demonstrated an extensive history of automotive manufacturing use cases for machine learning.

Several production facilities were considered for the initial deployment, with LinePulse ultimately being implemented globally on multiple lines in several manufacturing facilities to improve quality metrics across the client’s supply chain.

Problem: over 200 signals to investigate

Just one of the client’s production lines consists of more than 20 different operations which collectively generate over 200 signals per assembled unit. Given the number of available signals that can influence each unit, narrowing down the sources of failures was critical. This difficulty was compounded by the existence of multiple failure modes, each of which could have a different underlying set of causes.

Solution: LinePulse deployment

Acerta initially assisted with qualitatively and quantitatively describing the failure mode by capturing information on any complex interactions that might be present outside the standard arithmetic methods. LinePulse identified several key signals from the manufacturing process that most reliably predicted backlash failures. This enabled the client to narrow down the list of likely causes of the failures—including previously unsuspected relationships resulting from signals across operations— without the usual manual effort involved in sifting through large volumes of production data.

These early results helped build confidence in a more progressive AI/ML approach, that could integrate with the client’s network layer and down to the PLC, whereby a model built on the insights from the LinePulse platform could be deployed. As someone familiar with axle or PTU assemblies might know, setting the correct backlash is critical to function of the assembly but is typically done using shims calculated by considering the relevant component tolerance stack. In a perfect world this calculation should always be able to set the nominal backlash and provide the correct preload, but the reality is that this is not the case. Acerta was able to prove out and deploy a decision point solution that replaced the tolerance stack calculation with ML models that would pull an assembly’s entire build record, as well as previous assemblies, leveraging all information to provide a more accurate output.

Working through progressively production-ready trials through LinePulse, adapted and customized to fit the plant network infrastructure, Acerta was able implement the solution across two assembly lines in the facility.

Results: reduced failure and rework

The impacts on cost optimization and throughput observed in initial deployments of LinePulse‘s Anomaly Detection and Capability Metrics modules have led the customer to utilize LinePulse across facilities globally. 

The implementation achieved a 65% reduction in failure and rework rates, leading to a significant improvement in first time yield and significant cost reductions.

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